All posts by Julie Lee

With e-commerce booming and more transactions occurring online, the threat of chargeback fraud has never been more significant. In this article, we'll explore chargeback fraud, why it's a growing problem, and, most importantly, how to prevent it. Whether you're a small or large business, understanding and implementing robust chargeback fraud prevention measures is critical to protecting your organization. Understanding chargeback fraud Before we can prevent chargeback fraud, we need to know what we're dealing with. A chargeback happens when a cardholder disputes a transaction, or files a chargeback request, leading to the reversal of the payment to the merchant. Chargebacks can occur for various reasons including: Fraudulent transactions: If a card is stolen or its information is used without authorization, like in the case of account takeover fraud or card not present fraud, the cardholder can dispute the charges. Unauthorized transactions: Even if the cardholder didn't lose their card, they might notice charges they didn't make. Quality issues: If the product or service doesn't meet the cardholder's expectations or has a defect, they might dispute the charge. Billing errors: Sometimes, billing mistakes happen, such as being charged multiple times for the same transaction. Subscription cancellations: When a cardholder cancels a subscription but continues to be billed they can dispute the charges. While there are legitimate reasons for chargebacks, chargeback fraud, also known as friendly fraud, occurs when a customer makes a legitimate purchase with their credit card and then disputes the charge by filing a chargeback request. Unlike third-party fraud, where the cardholder's information is stolen or used without permission, in chargeback fraud, the cardholder initiates the dispute to avoid paying for goods or services they legitimately received. Chargeback fraud can take various forms: False claims of non-receipt: The cardholder claims they never received the purchased item, even though they did. Unauthorized transaction claims: The cardholder denies making the purchase, even though they did so legitimately. Product/service dissatisfaction: The cardholder claims dissatisfaction with the product or service as a reason for disputing the charge, even if the product or service was as described. Subscription services: The cardholder signs up for a subscription service and then disputes the recurring charges as unauthorized or unwanted. Why chargeback fraud is on the rise Chargeback fraud is becoming more pervasive for a couple of reasons. First, as e-commerce grows, so does the opportunity for fraud. Without face-to-face interactions, fraudsters can pull off their schemes more easily. Second, the process of issuing chargebacks has become consumer-friendly, with banks often siding with the cardholder without deep scrutiny of the claim. Finally, with the rise of subscription-based services and digital goods, the incidence of "friendly fraud" is increasing. The impact and repercussions of chargeback fraud The impact of chargeback fraud can be felt across several areas within a business. Financially, it's a clear and direct loss. There are also significant operational costs associated with managing chargebacks, including potential product loss, bank and related fees, and administrative work. However, the less tangible, more insidious repercussions involve damage to the business's reputation. A high chargeback rate can lead to a merchant account being suspended or terminated, causing a loss of the ability to process credit card payments. A tarnished reputation can further lead to losing consumer trust, which can be hard to regain. How to prevent chargeback fraud Preventing and managing chargeback fraud often involves implementing fraud prevention solutions, providing clear communication and customer support, and disputing illegitimate chargebacks with evidence when possible. Here are key actions you can take to protect your business against chargeback fraud: Educate and communicate with customers: Ensure your customers are fully aware of your return and refund policies. Be clear and transparent in your communications about what happens in the event of a disputed transaction. This can significantly reduce misunderstandings that often lead to legitimate chargebacks. Implement stringent transaction verification processes: Utilize Address Verification Services (AVS) and Card Verification Value (CVV2) verification for online and over-the-phone transactions. These credit card authentication services add an extra layer of security and can establish the validity of a purchase. Keep meticulous records: Document all transactions, including emails, phone calls, and any other purchase-related correspondence. In the event of a dispute, these records can serve as compelling evidence to defend the transaction. Immediate shipment and tracking: Ship products as quickly as possible after purchase and provide tracking information to customers. This delights customers and provides tangible proof of delivery should a chargeback be disputed. Utilize advanced fraud detection tools: Many fraud detection services are available that can instantly flag potentially fraudulent transactions, from monitoring for suspicious spending patterns to IP tracking for online orders. Examples include: Tokenization: Tokenization replaces sensitive card data with a "token," a random string of characters that is useless to fraudsters. This token can be stored or transmitted easily, with the actual payment information securely kept off-site. Machine learning and AI: Machine learning and AI fraud detection solutions can analyze vast amounts of transaction data to detect patterns and anomalies, thus flagging potentially fraudulent activity in real-time. The role of customer support in chargeback prevention While the above tools can help your organization prevent fraudulent charge backs, you likely already have a key tool in your company that can help mitigate chargebacks altogether. Your customer support team is your front line in chargeback prevention. Train them to handle customer inquiries effectively and resolve issues before they escalate. Offer multiple contact channels: Give customers several ways to reach your support team, such as email, phone, and live chat. The more easily they can contact you, the less likely they are to resort to a chargeback. Ensure prompt and courteous service: A positive and responsive customer service experience can turn a potential chargeback into a loyalty-building opportunity. Make refunds and returns as easy as possible for your customers. Additionally, clear and generous policies will reduce dissatisfaction and the likelihood of chargebacks. How Experian can help with chargeback fraud prevention Chargeback fraud can be a daunting prospect for any business, but with the right strategies in place, you can protect your business, your customers and your bottom line. Experian’s fraud management solutions provide robust verification options and layered risk management to help reduce the risk of chargeback fraud. Our advanced fraud detection solutions leverage machine learning algorithms and behavioral analytics to confirm the identity of customers during transactions, identify suspicious patterns and activities, and offer deeper insights that enhance fraud prevention strategies. These solutions can help detect potential instances of chargeback fraud in real-time or during post-transaction analysis. Learn More *This article includes content created by an AI language model and is intended to provide general information.

In a financial world that's increasingly connected and complex, monitoring transactions is not just good business practice — it's a regulatory necessity. Anti-money laundering (AML) transaction monitoring stands as a crucial barrier against financial crimes, which ensures the integrity of financial systems worldwide. For financial institutions, the challenges of AML compliance and the tools to meet them continue to evolve. In this blog post, we'll walk through the basics, best practices, and future of AML transaction monitoring. What is AML transaction monitoring? AML transaction monitoring refers to the systems and processes financial institutions use to detect and report potentially suspicious transactions to the Financial Crimes Enforcement Network (FinCEN) under the United States Department of Treasury, which spearheads the efforts to track financial crimes — money laundering and financing of criminal or terrorist activities. By continuously monitoring customer transactions and establishing patterns of behavior, suspicious activities can be identified for further investigation. The role of AML transaction monitoring AML transaction monitoring identifies potential criminal activities and helps maintain a clean and efficient financial ecosystem. By being proactive in preventing the misuse of services, organizations can protect their reputation, strengthen customer trust, and uphold regulatory requirements. The challenge of false positives However, AML compliance is not without challenges. The systems in place often produce many 'false positives', transactions identified as potentially suspicious that, after investigation, turn out to be mundane. These false alarms can overwhelm compliance departments, leading to inefficiency and potentially missing real red flags. Why is AML transaction monitoring important? Understanding the importance of AML transaction monitoring requires a broader look at the implications of financial crimes. Money laundering often supports other serious crimes such as drug trafficking, fraud, and even terrorism. The ability to interrupt the flow of illicit funds also disrupts these additional criminal networks. Furthermore, for organizations, the cost of non-compliance can be substantial — financially and reputationally. Penalties for inadequate AML controls can be hefty, signaling the need for robust monitoring systems. Specifics on compliance Compliance with AML regulations is not a choice but a must. Financial institutions are required to comply with AML laws and regulations to protect their businesses and the industry as a whole. This includes understanding and adhering to regulation changes, which can be complex and have significant operational impacts. How does AML transaction monitoring work? There are two main approaches to transaction monitoring: Rule-based systems: These rely on pre-defined rules that flag transactions exceeding certain thresholds, originating from high-risk countries, or involving specific types of activities. Scenario-based systems: These use more sophisticated algorithms to analyze transaction patterns and identify anomalies that might not be captured by simple rules. This can include analyzing customer behavior, source of funds, and the purpose of transactions. Most organizations use a combination of both approaches. Transaction monitoring software is a valuable tool, but it's important to remember that it's not a foolproof solution. Human analysis is still essential to investigate flagged transactions and determine if they are truly suspicious. Implementing AML transaction monitoring solutions Implementing a robust AML transaction monitoring system requires the right technology and the right strategy. Beyond the software, it's about embedding a culture of compliance within the organization. Choosing the right AML solution The right AML solution should be based on the specific needs of the institution, the complexity of its operations, and the sophistication of the fraud landscape it faces. It's imperative to pick a solution that is agile, scalable, and integrates seamlessly with existing systems. Leveraging KYC and CIP programs Know your customer (KYC) and customer identification program (CIP) are deeply connected to transaction monitoring. Implementing a robust KYC program helps to establish a strong customer identity, whereas a solid CIP ensures that essential customer information is verified at the time of account opening. Automation and AI in AML compliance Automation and AI are revolutionizing AML compliance, especially in transaction monitoring. AI systems, with their ability to learn and evolve, can significantly reduce false positives, making the compliance process more efficient and effective. Advanced AML solutions and the future Technological advancements are constantly reshaping the AML landscape, including solutions incorporating big data analysis and machine learning. Utilizing big data for better insights: Big data analytics provides an unprecedented ability to spot potential money laundering by analyzing vast amounts of transactional data, allowing for better contextual understanding and the ability to identify patterns of suspicious activity. Machine learning and predictive analytics: Machine learning technologies have the potential to refine transaction monitoring by continuously learning new behaviors and adapting to evolving threats. Predictive analytics can help in identifying potential risks well in advance and taking pre-emptive actions. The human element in AML Despite the advancing technology, the human element remains crucial. AML systems are only as good as the people who operate them. Organizations must invest in: Continuous training and skill development: Continuous training ensures that employees remain updated on regulations, compliance techniques, and the latest tools. Developing a team with AML expertise is an investment in the institution's security and success. Cultivating a compliance culture: Cultivating a corporate culture that values compliance is vital. From the highest levels of management to front-line staff, a mindset that embraces the duty to protect against financial crime is a powerful asset in maintaining an effective AML program. How we can help As a leader in fraud prevention and identity verification, Experian’s AML solutions can help you increase the effectiveness of your AML program to efficiently comply with federal and international AML regulations while safeguarding your organization from financial crime. We provide data, models, and automated systems and processes to monitor, detect, investigate, document and report potential money laundering activities across the entire customer lifecycle. Learn more about Experian’s AML solutions *This article includes content created by an AI language model and is intended to provide general information.

Financial institutions have long relied on anti-money laundering (AML) and anti-fraud systems to protect themselves and their customers. These departments and systems have historically operated in siloes, but that’s no longer best practice.  Now, a new framework that integrates fraud and AML, or FRAML, is taking hold as financial institutions see the value of sharing resources to fight fraud and other financial crimes.  You don’t need to keep them separated For fraudsters, fraud and money laundering go hand-in-hand. By definition, someone opening an account and laundering money is committing a crime. The laundered funds are also often from illegal activity — otherwise, they wouldn’t need to be laundered.  For financial institutions, different departments have historically owned AML and anti-fraud programs. In part, because AML and fraud prevention have different goals: AML is about staying compliant: AML is often owned by an organization’s compliance department, which ensures the proper processes and reporting are in place to comply with relevant regulations.  Fraud is about avoiding losses: The fraud department identifies and stops fraudulent activity to help protect the organization from reputational harm and fraud losses. As fraudsters’ operations become more complex, the traditional separation of the two departments may be doing more harm than good.  Common areas of focus There has always been some overlap in AML and fraud prevention. After all, an AML program can stop criminals from opening or using accounts that could lead to fraud losses. And fraud departments might stop suspicious activity that’s a criminal placing or layering funds. While AML and fraud both involve ongoing account monitoring, let’s take a closer look at similarities during the account creation: Verifying identities: Financial institutions’ AML programs must include know your customer (KYC) procedures and a Customer Identification Program (CIP). Being able to verify the identity of a new customer can be important for tracing transactions back to an individual or entity later. Similarly, fraud departments want to be sure there aren’t any red flags when opening a new account, such as a connection between the person or entity and previous fraudulent activity.  Preventing synthetic identity fraud: Criminals may try to use synthetic identities to avoid triggering AML or fraud checks. Synthetic identity fraud has been a growing problem, but the latest solutions and tools can help financial institutions stop synthetic identity fraud across the customer lifecycle.  Detecting money mules: Some criminals recruit money mules rather than using their own identity or creating a synthetic identity. The mules are paid to use their legitimate bank account to accept and transfer funds on behalf of the criminal. In some cases, the mule is an unwitting victim of a scam and an accomplice in money laundering.  Although the exact requirements, tools, processes, and reports for AML and fraud differ, there’s certainly one commonality — identify and stop bad actors.    Interactive infographic: Building a multilayered fraud and identity strategy The win-win of the FRAML approach Aligning AML and fraud could lead to cost savings and benefits for the organization and its customers in many ways. Save on IT costs: Fraud and AML teams may benefit from similar types of advanced analytics for detecting suspicious activity. In 2023, around 60 percent of businesses were using or trying to use machine learning (ML) in their fraud strategies, but a quarter said cost was impeding implementation.1 If fraud and AML can share IT resources and assets, they might be able to better afford the latest ML and AI solutions.  Avoid duplicate work: Cost savings can also happen if you can avoid having separate AML and fraud investigations into the same case. The diverse backgrounds and approaches to investigations may also lead to more efficient and successful outcomes.  Get a holistic view of customers: Sharing information about customers and accounts also might help you more accurately assess risk and identify fraud groups.  Improve your customer experience: Shared data can also reduce customer outreach for identity or transaction verifications. Creating a single view of each account or customer can also improve customer onboarding and account monitoring, leading to fewer false positives and a better customer experience.  Some financial institutions have implemented collaboration with the creation of a new team, sometimes called the financial crimes unit (FCU). Others may keep the departments separate but develop systems for sharing data and resources.  Watch the webinar: Fraud and identity challenges for Fintechs How Experian can help  Creating new systems and changing company culture doesn’t happen overnight, but the shift toward collaboration may be one of the big trends in AML and fraud for 2024. As a leader in identity verification and fraud prevention, Experian can offer the tools and strategies that organizations need to update their AML and fraud processes across the entire customer lifecycle.  CrossCore® is our integrated digital identity and fraud risk platform which enables organizations to connect, access, and orchestrate decisions that leverage multiple data sources and services. CrossCore cloud platform combines risk-based authentication, identity proofing and fraud detection, which enables organizations to streamline processes and quickly respond to an ever-changing environment. In its 2023 Fraud Reduction Intelligence Platforms (FRIP), Kuppinger Cole wrote, “Once again, Experian is a Leader in Fraud Reduction Intelligence Platforms. Any organizations looking for a full-featured FRIP service with global support should consider Experian CrossCore.”  Learn more about Experian’s AML and fraud solutions. 1. Experian (2023). Experian's 2023 Identity and Fraud Report  

This article was updated on March 12, 2024. The number of decisions that a business must make in the marketing space is on the rise. Which audience to target, what is the best method of communication, which marketing campaign should they receive? To stay ahead, a growing number of businesses are embracing artificial intelligence (AI) analytics, machine learning, and mathematical optimization in their decisioning models and strategies. What is an optimization model? While machine learning models provide predictive insights, it’s the mathematical optimization models that provide actionable insights that drive decisioning. Optimization models factor in multiple constraints and goals to leave you with the next best steps. Each step in the optimization process can significantly improve the overall impact of your marketing outreach — for both you and your customers. Using a mathematical optimization software, you can enhance your targeting, increase response rates, lower cost per acquisition, and drive engagement. Better engagement can lead to stronger business performance and profitability. Here are a few key areas where machine learning and optimization modeling can help increase your return on investment (ROI): Prospecting: Advanced analytics and optimization can be used to better identify individuals who meet your credit criteria and are most likely to respond to your offers. Taking this customer-focused approach, you can provide the most relevant marketing messages to customers at the right time and place. Cross-sell and upsell: The same optimized targeting can be applied to increase profitability with your existing customer base in cross-sell and up-sell opportunities. Gain insights into the best offer to send to each customer, the best time to send it, and which channel the customer will respond best to. Additionally, implement logic that maintains your customer contact protocols. Retention: Employing optimization modeling in the retention stage helps you make quicker decisions in a competitive environment. Instantly identify triggers that warrant a retention offer and determine the likelihood of the customer responding to different offers. LEARN MORE: eBook: Debunking the top 5 myths about optimization Gaining insight and strengthening decisions with our solutions Experian’s suite of advanced analytics solutions, including our optimization software, can help improve your marketing strategies. Use our ROI calculator to get a personalized estimate of how optimization can lift your campaigns without additional marketing spend. Start by inputting your organization’s details below. initIframe('62e81cb25d4dbf17c7dfea55'); Learn more about how optimization modeling can help you achieve your marketing and growth goals. Learn more  

Finding a reliable, customer-friendly way to protect your business against new account fraud is vital to surviving in today's digital-driven economy. Not only can ignoring the problem cause you to lose valuable money and client goodwill, but implementing the wrong solutions can lead to onboarding issues that drive away potential customers. The Experian® 2023 Identity and Fraud Report revealed that nearly 70 percent of businesses reported fraud loss in recent years, with many of these involving new account fraud. At the same time, problems with onboarding caused 37 percent of consumers to drop off and take their business elsewhere. In other words, your customers want protection, but they aren't willing to compromise their digital experience to get it. You need to find a way to meet both these needs when combating new account fraud. What is new account fraud? New account fraud occurs any time a bad actor creates an account in your system utilizing a fake or stolen identity. This process is referred to by different names, such as account takeover fraud, account creation fraud, or account opening fraud. Examples of some of the more common types of new account fraud include: Synthetic identity (ID) fraud: This type of fraud occurs when the scammer uses a real, stolen credential combined with fake credentials. For example, they might use someone's real Social Security number combined with a fake email. Identity theft: In this case, the fraudster uses personal information they stole to create a new scam account. Fake identity: With this type of fraud, scammers create an account with wholly fake credentials that haven't been stolen from any particular person. New account fraud may target individuals, but the repercussions spill over to impact entire organizations. In fact, many scammers utilize bots to attempt to steal information or create fake accounts en masse, upping the stakes even more. How does new account fraud work? New account fraud begins at a single weak security point, such as: Data breaches: The Bureau of Justice reported that in 2021 alone, 12 percent of people ages 16 or older received notifications that their personal information was involved in a data breach.1 Phishing scams: The fraudster creates an email or social media account that pretends to be from a legitimate organization or person to gain confidential information.2 Skimmers: These are put on ATMs or fuel pumps to steal credit or debit card information.2 Bot scrapers: These tools scrape information posted publicly on social media or on websites.2 Synthetic ID fraud: 80 percent of new account fraud is linked to synthetic ID fraud.3 The scammer just needs one piece of legitimate information. If they have a real Social Security number, they might combine it with a fake name and birth date (or vice versa.) After the information is stolen, the rest of the fraud takes place in steps. The fake or stolen identity might first be used to open a new account, like a credit card or a demand deposit account. Over time, the account establishes a credit history until it can be used for higher-value targets, like loans and bank withdrawals. How can organizations prevent new account fraud? Some traditional methods used to combat new account fraud include: Completely Automated Public Turing Tests (CAPTCHAs): These tests help reduce bot attacks that lead to data breaches and ensure that individuals logging into your system are actual people. Multifactor authentication (MFA): MFA bolsters users' password protection and helps guard against account takeover. If a scammer tries to take over an account, they won't be able to complete the process. Password protection: Robust password managers can help ensure that one stolen password doesn't lead to multiple breaches. Knowledge-based authentication: Knowledge-based authentication can be combined with MFA solutions, providing an additional layer of identity verification. Know-your-customer (KYC) solutions: Businesses may utilize KYC to verify customers via government IDs, background checks, ongoing monitoring, and the like. Additional protective measures may involve more robust identity verification behind the scenes. Examples include biometric verification, government ID authentication, public records analysis, and more. Unfortunately, these traditional protective measures may not be enough, for many reasons: New account fraud is frequently being perpetrated by bots, which can be tougher to keep up with and might overwhelm systems. Institutions might use multiple security solutions that aren't built to work together, leading to overlap and inefficiency. Security measures may create so much friction in the account creation process that potential new customers are turned away. How we can help Experian's fraud management services provide a multi-layered approach that lets businesses customize solutions to their particular needs. Advanced machine learning analytics utilizes extensive, proprietary data to provide a unique experience that not only protects your company, but it also protects your customers' experience. Customer identification program (CIP) Experian's KYC solutions allow you to confidently identify your customers via a low-friction experience. The tools start with onboarding, but continue throughout the customer journey, including portfolio management. The tools also help your company comply with relevant KYC regulations. Cross-industry analysis of identity behavior Experian has created an identity graph that aggregates consumer information in a way that gives companies access to a cross-industry view of identity behavior as it changes over time. This means that when a new account is opened, your company can determine behind the scenes if any part of the identity is connected to instances of fraud or presents actions not normally associated with the customer's identity. It's essentially a new paradigm that works faster behind the scenes and is part of Experian's Ascend Fraud Platform™. Multifactor authentication solutions Experian's MFA solutions utilize low-friction techniques like two-factor authentication, knowledge-based authentication, and unique one-time password authentication during remote transactions to guard against hacking. Synthetic ID fraud protection Experian's fraud management solutions include robust protection against synthetic ID fraud. Our groundbreaking technology detects and predicts synthetic identities throughout the customer lifecycle, utilizing advanced analytics capabilities. CrossCore® CrossCore combines risk-based authentication, identity proofing, and fraud detection into one cloud platform, allowing for real-time decisions to be made with flexible decisioning workflows and advanced analytics. Interactive infographic: Building a multilayered fraud and identity strategy Precise ID® The Precise ID platform lets customers choose the combination of fraud analytics, identification verification, and workflows that best meet their business needs. This includes machine-learned fraud risk models, robust consumer data assets, one-time passwords (OTPs), knowledge-based authentication (KBAs), and powerful insights via the Identity Element Network®. Account takeover fraud represents a significant threat to your business that you can't ignore. But with Experian's broad range of solutions, you can keep your systems secure while not sacrificing customer experience. Experian can keep your business secure from new account fraud Experian's innovative approach can streamline your new account fraud protection. Learn more about how our fraud management solutions can help you. Learn more References 1. Harrell, Erika. "Just the Stats: Data Breach Notifications and Identity Theft, 2021." Bureau of Justice Statistics, January 2024. https://bjs.ojp.gov/data-breach-notifications-and-identity-theft-2021 2. "Identity Theft." USA.gov, December 6, 2023. https://www.usa.gov/identity-theft 3. Purcell, Michael. "Synthetic Identity Fraud: What is It and How to Combat It." Thomson Reuters, April 28, 2023. https://legal.thomsonreuters.com/blog/synthetic-identity-fraud-what-is-it-and-how-to-combat-it/

This article was updated on February 28, 2024. There's always a risk that a borrower will miss or completely stop making payments. And when lending is your business, quantifying that credit risk is imperative. However, your credit risk analysts need the right tools and resources to perform at the highest level — which is why understanding the latest developments in credit risk analytics and finding the right partner are important. What is credit risk analytics? Credit risk analytics help turn historical and forecast data into actionable analytical insights, enabling financial institutions to assess risk and make lending and account management decisions. One way organizations do this is by incorporating credit risk modeling into their decisions. Credit risk modeling Financial institutions can use credit risk modeling tools in different ways. They might use one credit risk model, also called a scorecard, to assess credit risk (the likelihood that you won't be repaid) at the time of application. Its output helps you determine whether to approve or deny an application and set the terms of approved accounts. Later in the customer lifecycle, a behavior scorecard might help you understand the risk in your portfolio, adjust credit lines and identify up- or cross-selling opportunities. Risk modeling can also go beyond individual account management to help drive high-level portfolio and strategic decisions. However, managing risk models is an ongoing task. As market conditions and business goals change, monitoring, testing and recalibrating your models is important for accurately assessing credit risk. Credit scoring models Application credit scoring models are one of the most popular applications for credit risk modeling. Designed to predict the probability of default (PD) when making lending decisions, conventional credit risk scoring models focus on the likelihood that a borrower will become 90 days past due (DPD) on a credit obligation in the following 24 months. These risk scores are traditionally logistic regression models built on historical credit bureau data. They often have a 300 to 850 scoring range, and they rank-order consumers so people with higher scores are less likely to go 90 DPD than those with lower scores. However, credit risk models can have different score ranges and be developed to predict different outcomes over varying horizons, such as 60 DPD in the next 12 months. In addition to the conventional credit risk scores, organizations can use in-house and custom credit risk models that incorporate additional data points to better predict PD for their target market. However, they need to have the resources to manage the entire development and deployment or find an experienced partner who can help. The latest trends in credit risk scoring Organizations have used statistical and mathematical tools to measure risk and predict outcomes for decades. But the future of credit underwriting is playing out as big data meets advanced data analytics and increased computing power. Some of the recent trends that we see are: Machine learning credit risk models: Machine learning (ML) is a type of artificial intelligence (AI) that's proven to be especially helpful in evaluating credit risk. ML models can outperform traditional models by 10 to 15 percent.1 Experian survey data from September 2021 found that about 80 percent of businesses are confident in AI and cloud-based credit risk decisioning, and 70 percent frequently discuss using advanced analytics and AI for determining credit risk and collection efforts.2 Expanding data sources: The ML models' performance lift is due, in part, to their ability to incorporate internal and alternative credit data* (or expanded FCRA-regulated data), such as credit data from alternative financial services, rental payments and Buy Now Pay Later loans. Cognitively countering bias: Lenders have a regulatory and moral imperative to remove biases from their lending decisions. They need to beware of how biased training data could influence their credit risk models (ML or otherwise) and monitor the outcomes for unintentionally discriminatory results. This is also why lenders need to be certain that their ML-driven models are fully explainable — there are no black boxes. A focus on agility: The pandemic highlighted the need to have credit risk models and systems that you can quickly adjust to account for unexpected world events and changes in consumer behavior. Real-time analytical insights can increase accuracy during these transitory periods. Financial institutions that can efficiently incorporate the latest developments in credit risk analytics have a lot to gain. For instance, a digital-first lending platform coupled with ML models allows lenders to increasingly automate loan underwriting, which can help them manage rising loan volumes, improve customer satisfaction and free up resources for other growth opportunities. READ: The getting AI-driven decisioning right in financial services white paper to learn more about the current AI decisioning landscape. Why does getting credit risk right matter? Getting credit risk right is at the heart of what lenders do and accurately predicting the likelihood that a borrower won't repay a loan is the starting point. From there, you can look for ways to more accurately score a wider population of consumers, and focus on how to automate and efficiently scale your system. Credit risk analysis also goes beyond simply using the output from a scoring model. Organizations must make lending decisions within the constraints of their internal resources, goals and policies, as well as the external regulatory requirements and market conditions. Analytics and modeling are essential tools, but as credit analysts will tell you, there's also an art to the practice. CASE STUDY: Atlas Credit, a small-dollar lender, worked with Experian's analytics experts to create a custom explainable ML-powered model using various data sources. After reworking the prequalification and credit decisioning processes and optimizing their score cutoffs and business rules, the company can now make instant decisions. It also doubled its approval rate while reducing risk by 15 to 20 percent. How Experian helps clients With decades of experience in credit risk analytics and data management, Experian offers a variety of products and services for financial services firms. Ascend Intelligence Services™ is an award-winning, end-to-end suite of analytics solutions. At a high level, the offering set can rapidly develop new credit risk models, seamlessly deploy them into production and optimize decisioning strategies. It also has the capability to continuously monitor and retrain models to improve performance over time. For organizations that have the experience and resources to develop new credit risk models on their own, Experian can give you access to data and expertise to help guide and improve the process. But there are also off-the-shelf options for organizations that want to quickly benefit from the latest developments in credit risk modeling. Learn more 1Experian (2020). Machine Learning Decisions in Milliseconds 2Experian (2021). Global Insights Report September/October 2021

Developing machine learning (ML) credit risk models can be more challenging than traditional credit risk modeling approaches. But once deployed, ML models can increase automation and expand a lender’s credit universe.  For example, by using ML-driven credit risk models and combining traditional credit data with transactional bank data, a type of alternative credit data* , some lenders see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.1   New approaches to model operations are also helping lenders accelerate their machine learning model development processes and go from collecting data to deploying a new model in days instead of months.  READ MORE: Getting AI-driven decisioning right in financial services What is machine learning model development? Machine learning model development is what happens before the model gets deployed. It's often broken down into several steps. Define the problem: If you’re building an ML credit risk model, the problem you may be trying to solve is anticipating defaults, improving affordability for borrowers or expanding your lending universe by scoring more thin-file and previously unscorable consumers.  Gather, clean and stage data: Identify helpful data sources, such as internal, credit bureau and alternative credit data. The data will then need to be consolidated, structured, labeled and categorized. Machine learning can be useful here as well, as ML models can be trained to label and categorize raw data. Feature engineering: The data is then analyzed to identify the individual variables and clusters of variables that may offer the most lift. Features that may directly or unintentionally create bias should be removed or limited.  Create the model: Deciding which algorithms and techniques to use when developing a model can be part art and part science. Because lenders need to be able to explain the decisions they make to consumers and regulators, many lenders build model explainability into new ML-driven credit risk models. Validate and deploy: New models are validated and rigorously tested, often as challengers to the existing champion model. If the new model can consistently outperform, it may move on to production.  The work doesn’t stop once a model is live — it needs to be continuously monitored for drift, and potentially recalibrated or replaced with a new model. About 10 percent of lenders use tools to automatically alert them when their models start to drift. But around half make a point of checking deployed models for drift every month or quarter.3  READ MORE: Journey of an ML Model What is model deployment? Model deployment is one of the final steps in the model lifecycle — it’s when you move the model from development and validation to live production.  New models can be deployed in various ways, including via API integration and cloud service deployment using public, private or hybrid architecture. However, integrating a new model with existing systems can be challenging. About a third (33 percent) of consumer lending organizations surveyed in 2023 said it took them one to two months for model deployment-related activities. A little less (29 percent) said it took them three to six months.  Overall, it often takes up to 15 months for the entire development to deployment process — and 55 percent of lenders report building models that never get deployed.2  READ MORE: Accelerating the Model Development and Deployment Lifecycle Benefits of deploying machine learning credit risk models Developing, deploying, monitoring and recalibrating ML models can be difficult and costly. But financial institutions have a lot to gain from embracing the future of underwriting. Improve credit risk assessment: ML-driven models can incorporate more data sources and more precisely assess credit risk to help lenders price credit offers and decrease charge-offs.  Expand automation: More precise scoring can also increase automation by reducing how many applications need to go to manual review.  Increase financial inclusion: ML-models may be able to evaluate consumers who don’t have recent credit information or thick enough credit files to be scorable by traditional models. In short, ML models can help lenders make better loan offers to more people while taking on less risk and using fewer internal resources to review applications.  CASE STUDY: Atlas Credit, a small-dollar lender, partnered with Experian® to develop a fully explainable machine learning credit risk model that incorporated internal data, trended data, alternative financial services data and Experian’s attributes. Atlas Credit can use the new model to make instant decisions and is expected to double its approvals while decreasing losses by up to 20 percent.  How we can help Experian offers many machine learning solutions for different industries and use cases via the Experian Ascend Technology Platform™. For example, with Ascend ML Builder™, lenders can access an on-demand development environment that can increase model velocity — the time it takes to complete a new model’s lifecycle. You can configure Ascend ML Builder based on the compute you allocate and your use cases, and the included code templates (called Accelerators) can help with data wrangling, analysis and modeling.  There’s also Ascend Ops™, a cloud-based model operations solution. You can use Ascend Ops to register, test and deploy custom features and models. Automated model monitoring and management can also help you track feature and model data drift and model performance to improve models in production. Learn more about our machine learning and model deployment solutions *When we refer to “Alternative Credit Data,” this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data” may also apply and can be used interchangeably. 1. Experian (2023). Raising the AI Bar 2. Experian (2023). Accelerating Model Velocity in Financial Institutions 3. Ibid.

Companies depend on quality information to make decisions that move their business objectives forward while minimizing risk exposure. And in today’s modern, tech-driven, innovation-led world, there’s more  information available than ever before. Expansive datasets from sources, both internal and external, allow decision-makers to leverage a wide range of intelligence to fuel how they plan, forecast and set priorities. But how can business leaders be sure that their data is as robust, up-to-date and thorough as they need — and, most importantly, that they’re able to use it to its fullest potential? That’s where the power of advanced analytics comes in. By making use of cutting-edge datasets and analytics insights, businesses can stay on the vanguard of business intelligence and ahead of their competitors. What is advanced analytics? Advanced analytics is a form of business intelligence that takes full advantage of the most modern data sources and analytics tools to create forward-thinking analysis that can help businesses make well-informed, data-driven decisions that are tailored to their needs. Simply put, advanced analytics is an essential component of any proactive business strategy that aims to maximize the future potential of both customers and campaigns. These advanced business intelligence and analytics solutions  help leaders make profitable decisions no matter the state of the current economic climate. They use both traditional and non-traditional data sources to provide businesses with actionable insights in the formats best suited to their needs and goals. One key aspect of advanced analytics is the use of AI analytics solutions. These efficient and effective tools help businesses save time and money by harnessing the power of cutting-edge technologies and deploying them in optimal use-case scenarios. These AI and machine-learning solutions use a wide range of tools, such as neural network methodologies, to help organizations optimize their allocation of resources, expediting and automating some processes while creating valuable insights to help human decision-makers navigate others. Benefits of advanced analytics Traditional business intelligence tends to be limited by the scope and quality of available data and ability of analysts to make use of it in an effective, comprehensive way. Modern business intelligence analytics, on the other hand, integrates machine learning and analytics to maximize the potential of data sets that, in today's technology-driven world, are often overwhelmingly large and complex: think not just databases of customer decisions and actions but behavioral data points tied to online and offline activity and the internet of things. What's more, advanced analytics does this in a way that's accessible to an entire organization — not just those who know their way around data, like IT departments and trained analysts. With the right advanced analytics solution, decision-makers can access convenient cloud-based dashboards designed to give them the information they want and need — with no clutter, noise or confusing terminology. Another key advantage of advanced analytics solutions is that they don't just analyze data — they optimize it, too. Advanced analytics offers the ability to clean up and integrate multiple data sets to remove duplicates, correct errors and inaccuracies and standardize formats, leading to high-quality data that creates clarity, not confusion. The result? By analyzing and identifying relationships across data, businesses can uncover hidden insights and issues. Advanced analytics also automate some aspects of the decision-making process to make workflows quicker and nimbler. For example, a business might choose to automate credit scoring, product recommendations for existing customers or the identification of potential fraud. Reducing manual interventions translates to increased agility and operational efficiency and, ultimately, a better competitive advantage. Use cases in the financial services industry Advanced analytics gives businesses in the financial world the power to go deeper into their data — and to integrate alternative data sources as well. With predictive analytics models, this data can be transformed into highly usable, next-level insights that help decision-makers optimize their business strategies. Credit risk, for instance, is a major concern for financial organizations that want to offer customers the best possible options while ensuring their credit products remain profitable. By utilizing advanced analytics solutions combined with a broad range of datasets, lenders can create highly accurate credit risk scores that forecast future customer behavior and identify and mitigate risk, leading to better lending decisions across the credit lifecycle. Advanced analytics solutions can also help businesses problem-solve. Let's say, for instance, that uptake of a new loan product has been slower than desired. By using business intelligence analytics, companies can determine what factors might be causing the issue and predict the tweaks and changes they can make to improve results. Advanced analytics means better, more detailed segmentation, which allows for more predictive insights. Businesses taking advantage of advanced analytics services are simply better informed: not only do they have access to more and better data, but they're able to convert it into actionable insights that help them lower risk, better predict outcomes, and boost the performance of their business. How we can help Experian offers a wide range of advanced analytics tools aimed at helping businesses in all kinds of industries succeed through better use of data. From custom machine learning models that help financial institutions assess risk more accurately to self-service dashboards designed to facilitate more agile responses to changes in the market, we have a solution that's right for every business. Plus, our advanced analytics offerings include a vast data repository with insights on 245 million credit-active individuals and 25 million businesses, as well as the industry's largest alternative data set from non-traditional lenders. Ready to explore? Click below to learn about our advanced analytics solutions. Learn more

This article was updated on February 6, 2024. Lenders looking to gain a competitive edge need to improve their credit underwriting process in the coming years. The most obvious developments are the advances in artificial intelligence (AI) — machine learning in particular — the increased available computing capacity, and access to vast amounts of data. But when it comes to credit underwriting models, those are tools you can use to reach your goals, not a strategy for success. The evolution of credit underwriting Credit underwriters have had the same goal for millennia — assess the creditworthiness of a borrower to determine whether to offer them a loan. But the process has changed immensely, and the pace of change has recently increased. Fewer than 50 years ago, an underwriter might consider an applicant's income, occupation, marital status, and sex to make a decision. The Equal Credit Opportunity Act didn't pass until 1974. And it wasn't expanded to prohibit lending discrimination based on other factors, such as color, age, and national origin, until two years later. Regulatory changes can have an immediate and immense impact on credit underwriting, but there were also slower changes developing. As credit bureaus centralized and computers became more readily available, credit decisioning systems offered new insights. The systems could segment groups and help lenders make more complex and profitable decisions at scale, such as setting risk-appropriate credit limits and terms. INFOGRAPHIC: Data-driven decisioning journey map With access to more data and computing power, lenders get a more complete picture of applicants and their current customers. Technological advances also lead to automated decisions, which can improve lenders' workflows and customer satisfaction. In the late 2000s, fintech lenders entered the scene and disrupted the ecosystem with a completely online underwriting and funding process. More recently, AI and machine learning started as buzzwords, but quickly became business necessities. In fact, 66% of businesses believe advanced analytics, including machine learning and artificial intelligence, are going to rapidly change the way they do business.1 The latest explainable machine learning models can increase automation and efficiency while outperforming traditional modeling approaches. Access to increased computing power is, once again, helping power this shift.2 But it's also only possible because of the lenders access to alternative credit data.* WATCH: Why Advanced Analytics is Now Available for All Future-proofing your credit underwriting strategy Today's leading lenders use innovative technology and comprehensive data to improve their credit decisioning — including fraud detection, underwriting, account management, and collections. To avoid getting left behind, you need to consider how you can incorporate new tools and processes into your strategy. Get comfortable with machine learning models Although machine learning models have repeatedly shown they can offer performance improvements, lenders may hesitate to adopt them if they can't explain how the models work. It's smart to be cautious as so-called “black box" models generally don't pass regulatory muster — even if they can offer a greater lift. But there is a middle ground, and credit modelers use machine learning techniques to develop more effective models that are fully explainable. READ MORE: Explainability: ML and AI in credit decisioning Explore new data sources Machine learning models are great at recognizing patterns, but you need to train them on large data sets if you want to unlock their full potential. Lenders' internal data can be important, especially if they're developing custom models. But lenders should also try leveraging various types of alternative credit data to train models and more accurately assess an applicant's creditworthiness. This can include data from public records, rental payments, alternative financial services, and consumer-permissioned data. READ MORE: 2023 State of Alternative Credit Data Report Focus on financial inclusion Using new data sources can also help you more accurately understand the risk of an applicant who isn't scorable with traditional models. For example, Lift Premium™ uses machine learning and a combination of traditional consumer bureau credit data and alternative credit data to score 96 percent of U.S. consumers — 15 percent more than conventional scores.3 As a result, lenders can expand their lending universe and offer right-sized terms to people and groups who might otherwise be overlooked. Use AI to fuel automation Artificial intelligence can accelerate automation throughout the credit life cycle. Machine learning models do this within underwriting by more precisely estimating the creditworthiness of applicants. The more accurate a model is, the better it will be at identifying applicants who lenders want to approve or deny. Consider your decisioning strategy Although a machine learning model might offer more precise insight, lenders still need to set their decisioning strategy and business rules, including the cutoff points. Credit decisioning software can help lenders implement these decisions with speed, accuracy, and scalability. CASE STUDY: Experian partnered with OneAZ Credit Union to upgrade to an advanced credit decisioning platform and automate its underwriting strategy. The credit union increased load funding rates by 26 percent within one month and reduced manual reviews by 25 percent. Use underwriting as a component of strategic optimization Advanced analytics allow companies to move away from simpler rule-based decisions and toward strategies that take the business's overall goals into account. For example, lenders may be able to optimize decisions that involve competing goals — such as targets for volume and bad debt — to help the business reach its goals. Test and benchmark Underwriting is an iterative process. Lenders can use machine learning techniques to build and test challenger models and see how well they perform. You can also compare the results to industry benchmarks to see if there's likely room for more improvement. Why lenders choose Experian Lenders have used Experian's consumer and business credit data to underwrite loans for decades, but Experian is also a leader in advanced analytics. As lenders try to figure out how they'll approach underwriting in the coming years, they can partner with Experian's data scientists, who understand how to develop and deploy the latest types of compliant and explainable credit underwriting models. Experian also offers credit underwriting software and cloud-based and integrated decisioning platforms, along with modular solutions, such as access to alternative credit data, predictive attributes and scores. And lenders can explore collaborative approaches to developing ML-aided models that incorporate internal and third-party data. If you're not sure where to start, a business review can help you identify a few quick wins and create a road map for future improvements. Explore our credit decisioning solutions. * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably. 1Experian (2022). Explainability: ML and AI in credit decisioning2Experian (2022). Webinar: Driving Growth During Economic Uncertainty with AI/ML Strategies 3Experian (2022). Lift Premium

In today’s complex business landscape, data-based decision-making has become the norm, with advanced technologies and analytics tools facilitating faster and more accurate modeling and predictions. However, with the increased reliance on models, the risk of errors has also increased, making it crucial for organizations to have a comprehensive model risk management framework. In this blog post, we will dive deeper into model risk management, its importance for organizations, and the key elements of a model risk management framework. What is model risk? First, let's define what we mean by model risk. Many institutions use models to forecast and predict the future performance of investments, portfolios or consumers' creditworthiness. Model risk can happen when the results produced by these models are inaccurate or not fit for the intended purpose. This risk arises due to several factors, like data limitations, model assumptions and inherent complexities in the underlying modeled processes. For example, in the credit industry, an inaccurately calibrated credit risk model may incorrectly assess a borrower's default risk, resulting in erroneous credit decisions and impacting overall portfolio performance. What is a risk management model and why is it important? A risk management model, or model risk management, refers to a systematic approach to manage the potential risks associated with the use of models and, more specifically, quantitative models built on data. Since models are based on a wide range of assumptions and predictions, it's essential to recognize the possibility of errors and acknowledge its impact on business decisions. The goal of model risk management is to provide a well-defined and structured approach to identifying, assessing, and mitigating risks associated with model use. The importance of model risk management for institutions that leverage quantitative risk models in their decisioning strategies cannot be overstated. Without proper risk management models, businesses are vulnerable to significant consequences, such as financial losses, regulatory enforcement actions and reputational damage. Model risk management: essential elements The foundation of model risk management includes standards and processes for model development, validation, implementation and ongoing monitoring. This includes: Policies and procedures that provide a clear framework for model use and the associated risks. Model inventory and management that captures all models used in an organization. Model development and implementation that documents the policies for developing and implementing models, defining critical steps and role descriptions. Validation and ongoing monitoring to ensure the models meet their stated objectives and to detect drift. In addition to these essential elements, a model risk management framework must integrate an ongoing system of transparency and communication to ensure that each stakeholder in model risk governance is aware of the policies, processes and decisions that support model use. Active engagement with modelers, validators, business stakeholders, and audit functions, among other stakeholders, is essential and should be included in the process. How we can help Experian® provides solutions and risk mitigation tools to help organizations of all sizes establish a solid model risk management framework to meet regulatory and model risk governance requirements, improve overall model performance and identify and mitigate potential risk. We provide services for back testing, benchmarking, sensitivity analysis and stress testing. In addition, our experts can review your organization’s current model risk management practices, conduct a gap analysis and assist with audit preparations. Learn more *This article includes content created by an AI language model and is intended to provide general information.

Model explainability has become a hot topic as lenders look for ways to use artificial intelligence (AI) to improve their decision-making. Within credit decisioning, machine learning (ML) models can often outperform traditional models at predicting credit risk.  ML models can also be helpful throughout the customer lifecycle, from marketing and fraud detection to collections optimization. However, without explainability, using ML models may result in unethical and illegal business practices.  What is model explainability?  Broadly defined, model explainability is the ability to understand and explain a model's outputs at either a high level (global explainability) or for a specific output (local explainability).1  Local vs global explanation: Global explanations attempt to explain the main factors that determine a model's outputs, such as what causes a credit score to rise or fall. Local explanations attempt to explain specific outputs, such as what leads to a consumer's credit score being 688. But it's not an either-or decision — you may need to explain both.  Model explainability can also have varying definitions depending on who asks you to explain a model and how detailed of a definition they require. For example, a model developer may require a different explanation than a regulator.  Model explainability vs interpretability  Some people use model explainability and interpretability interchangeably. But when the two terms are distinguished, model interpretability may refer to how easily a person can understand and explain a model's decisions.2 We might call a model interpretable if a person can clearly understand:  The features or inputs that the model uses to make a decision.  The relative importance of the features in determining the outputs.  What conditions can lead to specific outputs.  Both explainability and interpretability are important, especially for credit risk models used in credit underwriting. However, we will use model explainability as an overarching term that encompasses an explanation of a model's outputs and interpretability of its internal workings below.  ML models highlight the need for explainability in finance  Lenders have used credit risk models for decades. Many of these models have a clear set of rules and limited inputs, and they might be described as self-explanatory. These include traditional linear and logistic regression models, scorecards and small decision trees.3  AI analytics solutions, such as ML-powered credit models, have been shown to better predict credit risk. And most financial institutions are increasing their budgets for advanced analytics solutions and see their implementation as a top priority.4  However, ML models can be more complex than traditional models and they introduce the potential of a “black box." In short, even if someone knows what goes into and comes out of the model, it's difficult to explain what's happening without an in-depth analysis.  Lenders now have to navigate a necessary trade-off. ML-powered models may be more predictive, but regulatory requirements and fair lending goals require lenders to use explainable models.  READ MORE: Explainability: ML and AI in credit decisioning  Why is model explainability required?  Model explainability is necessary for several reasons:  To comply with regulatory requirements: Decisions made using ML models need to comply with lending and credit-related, including the Fair Credit Reporting Act (FCRA) and Equal Credit Opportunity Act (ECOA). Lenders may also need to ensure their ML-driven models comply with newer AI-focused regulations, such as the AI Bill of Rights in the U.S. and the E.U. AI Act.  To improve long-term credit risk management: Model developers and risk managers may want to understand why decisions are being made to audit, manage and recalibrate models.  To avoid bias: Model explainability is important for ensuring that lenders aren't discriminating against groups of consumers.  To build trust: Lenders also want to be able to explain to consumers why a decision was made, which is only possible if they understand how the model comes to its conclusions.  There's a real potential for growth if you can create and deploy explainable ML models. In addition to offering a more predictive output, ML models can incorporate alternative credit data* (also known as expanded FCRA-regulated data) and score more consumers than traditional risk models. As a result, the explainable ML models could increase financial inclusion and allow you to expand your lending universe.  READ MORE: Raising the AI Bar  How can you implement ML model explainability?  Navigating the trade-off and worries about explainability can keep financial institutions from deploying ML models. As of early 2023, only 14 percent of banks and 19 percent of credit unions have deployed ML models. Over a third (35 percent) list explainability of machine learning models as one of the main barriers to adopting ML.5  Although a cautious approach is understandable and advisable, there are various ways to tackle the explainability problem. One major differentiator is whether you build explainability into the model or try to explain it post hoc—after it's trained.  Using post hoc explainability  Complex ML models are, by their nature, not self-explanatory. However, several post hoc explainability techniques are model agnostic (they don't depend on the model being analyzed) and they don't require model developers to add specific constraints during training.  Shapley Additive Explanations (SHAP) is one used approach. It can help you understand the average marginal contribution features to an output. For instance, how much each feature (input) affected the resulting credit score.  The analysis can be time-consuming and expensive, but it works with black box models even if you only know the inputs and outputs. You can also use the Shapley values for local explanations, and then extrapolate the results for a global explanation.  Other post hoc approaches also might help shine a light into a black box model, including partial dependence plots and local interpretable model-agnostic explanations (LIME).  READ MORE: Getting AI-driven decisioning right in financial services  Build explainability into model development  Post hoc explainability techniques have limitations and might not be sufficient to address some regulators' explainability and transparency concerns.6 Alternatively, you can try to build explainability into your models. Although you might give up some predictive power, the approach can be a safer option.  For instance, you can identify features that could potentially lead to biased outcomes and limit their influence on the model. You can also compare the explainability of various ML-based models to see which may be more or less inherently explainable. For example, gradient boosting machines (GBMs) may be preferable to neural networks for this reason.7  You can also use ML to blend traditional and alternative credit data, which may provide a significant lift — around 60 to 70 percent compared to traditional scorecards — while maintaining explainability.8  READ MORE: Journey of an ML Model  How Experian can help  As a leader in machine learning and analytics, Experian partners with financial institutions to create, test, validate, deploy and monitor ML-driven models. Learn how you can build explainable ML-powered models using credit bureau, alternative credit, third-party and proprietary data. And monitor all your ML models with a web-based platform that helps you track performance, improve drift and prepare for compliance and audit requests. *When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply and can be used interchangeably.  1-3. FinRegLab (2021). The Use of Machine Learning for Credit Underwriting  4. Experian (2022). Explainability: ML and AI in credit decisioning  5. Experian (2023). Finding the Lending Diamonds in the Rough  6. FinRegLab (2021). The Use of Machine Learning for Credit Underwriting  7. Experian (2022). Explainability: ML and AI in credit decisioning  8. Experian (2023). Raising the AI Bar 

In today's fast-paced digital world, the risk of fraud across all industries is a constant threat. The traditional methods of fraud detection are no longer sufficient, as fraudsters become increasingly sophisticated in their attacks. However, with artificial intelligence (AI) and machine learning (ML) solutions, financial institutions can stay one step ahead of fraudsters. AI and machine learning-equipped fraud detection tools have the ability to identify suspicious activity and patterns of fraud that are imperceptible to the human brain. In this blog post, we’ll dive into the significance of AI and machine learning in fraud detection and how these solutions are uniquely equipped to handle the demands of modern-day risk management. Understanding artificial intelligence and machine learning AI and machine learning solutions are transformative technologies that are reshaping the landscape of many industries. AI, at its core, is a field of computer science that simulates human intelligence in machines, enabling them to learn from experience and perform tasks that normally require human intellect. Machine learning, a subset of AI, is the science of getting computers to learn and act like humans do, but with minimal human intervention. They can analyze vast amounts of data within seconds, identifying patterns and trends that would be impossible for a human to recognize. When it comes to fraud detection, this ability is invaluable.  Advantages of fraud detection using machine learning AI and machine learning have several benefits that make them valuable in fraud detection. One significant advantage is that these technologies can recognize patterns that are too complex for humans to identify. By running through a vast set of data points, these solutions can pinpoint anomalous behavior, and thereby prevent financial losses. AI analytics tools are adept at monitoring complex networks, detecting the dispersion of attacks that may involve multiple individuals and entities, and correlating activity patterns that would otherwise be hidden. Machine learning algorithms can take these patterns and turn them into mathematical models that help identify instances of fraud before the damage takes place. Secondly, they continuously learn from new data, which allows them to become more efficient in identifying fraud as they process more data. Thirdly, they automate fraud mitigation processes, which significantly reduces the need for manual interventions that may consume valuable time and resources. Another significant benefit of machine learning is its analytics capabilities, which allow organizations to gain valuable insights into customer behavior and fraud patterns. With AI analytics, they can detect and investigate fraudulent activities in real-time, and combine it with other tools to help detect and mitigate fraud risk. For example, in financial services, AI fraud detection can help banks and financial service providers detect and prevent fraud in their systems, add value to their services and improve customer satisfaction. The future of fraud detection and machine learning The rate at which technology is evolving means that machine learning and AI fraud detection will become increasingly important in the future. In the next few years, we can expect a more sophisticated level of fraud detection using unmanned machine systems, robotics process automation, and more. Ultimately, this will improve the efficiency and effectiveness of fraud detection.AI-based fraud management solutions are taking center stage. Organizations must leverage advanced machine learning and AI analytics solutions to prevent and mitigate cyber risks and comply with regulatory mandates. The benefits extend far beyond the financial bottom line to improving the safety and security of customers. AI and machine learning solutions offer accurate, efficient and proactive routes to managing the risk of fraud in an ever-changing environment. How can Experian® help Integrating machine learning for fraud detection represents a significant advancement in cybersecurity. Fraud management solutions detect, prevent and manage fraud across all industries, including financial services, healthcare and telecommunications. With the advancement of technology, fraud management solutions now integrate machine learning to improve their processes. Experian® provides fraud prevention solutions, including machine learning models and AI analytics, which can help more effectively mitigate fraud risk, streamline fraud investigations and create a more secure digital environment for all. With Experian’s AI analytics, risk mitigation tools and fraud management solutions, organizations can stay one step ahead of fraudsters and protect their brand reputation, customer trustworthiness and corporate data. Embracing these solutions can save organizations from significant losses, reputational damage and regulatory scrutiny. To learn more about how to future-proof your business and safeguard your customers from fraud, check out Experian’s robust suite of fraud prevention solutions. Want to hear what our industry experts think? Check out this on-demand webinar on artificial intelligence and machine learning strategies. *This article includes content created by an AI language model and is intended to provide general information.

With great risk comes great reward, as the saying goes. But when it comes to business, there's huge value in reducing and managing that risk as much as possible to maximize benefits — and profits. In today's high-tech strategic landscape, financial institutions and other organizations are increasingly using risk modeling to map out potential scenarios and gain a clearer understanding of where various paths may lead. But what are risk models really, and how can you ensure you're creating and using them correctly in a way that actually helps you optimize decision-making? Here, we explore the details. What is a risk model? A risk model is a representation of a particular situation that's created specifically for the purpose of assessing risk. That risk model is then used to evaluate the potential impacts of different decisions, paths and events. From assigning interest rates and amortization terms to deciding whether to begin operating in a new market, risk models are a safe way to analyze data, test assumptions and visualize potential scenarios. Risk models are particularly valuable in the credit industry. Credit risk models and credit risk analytics allow lenders to evaluate the pluses and minuses of lending to clients in specific ways. They are able to consider the larger economic environment, as well as relevant factors on a micro level. By integrating risk models into their decision-making process, lenders can refine credit offerings to fit the assessed risk of a particular situation. It goes like this: a team of risk management experts builds a model that brings together comprehensive datasets and risk modeling tools that incorporate mathematics, statistics and machine learning. This predictive modeling tool uses advanced algorithmic techniques to analyze data, identify patterns and make forecasts about future outcomes. Think of it as a crystal ball — but with science behind it. Your team can then use this risk model for a wide range of applications: refining marketing targets, reworking product offerings or reshaping business strategies. How can risk models be implemented? Risk models consolidate and utilize a wide variety of data sets, historical benchmarks and qualitative inputs to model risk and allow business leaders to test assumptions and visualize the potential results of various decisions and events. Implementing risk modeling means creating models of systems that allow you to adjust variables to imitate real-world situations and see what the results might be. A mortgage lender, for example, needs to be able to predict the effects of external and internal policies and decisions. By creating a risk model, they can test how scenarios such as falling interest rates, rising unemployment or a shift in loan acceptance rates might affect their business — and make moves to adjust their strategies accordingly. One aspect of risk modeling that can't be underestimated is the importance of good data, both quantitative and qualitative. Efforts to implement or expand risk modeling should begin with refining your data governance strategy. Maximizing the full potential of your data also requires integrating data quality solutions into your operations in order to ensure that the building blocks of your risk model are as accurate and thorough as possible. It's also important to ensure your organization has sufficient model risk governance in place. No model is perfect, and each comes with its own risks. But these risks can be mitigated with the right set of policies and procedures, some of which are part of regulatory compliance. With a comprehensive model risk management strategy, including processes like back testing, benchmarking, sensitivity analysis and stress testing, you can ensure your risk models are working for your organization — not opening you up to more risk. How can risk modeling be used in the credit industry? Risk modeling isn't just for making credit decisions. For instance, you might model the risk of opening or expanding operations in an underserved country or the costs and benefits of existing one that is underperforming. In information technology, a critical branch of virtually every modern organization, risk modeling helps security teams evaluate the risk of malicious attacks. Banking and financial services is one industry for which understanding and planning for risk is key — not only for business reasons but to align with relevant regulations. The mortgage lender mentioned above, for example, might use credit risk models to better predict risk, enhance the customer journey and ensure transparency and compliance. It's important to highlight that risk modeling is a guide, not a prophecy. Datasets can contain flaws or gaps, and human error can happen at any stage.. It's also possible to rely too heavily on historical information — and while they do say that history repeats itself, they don't mean it repeats itself exactly. That's especially true in the presence of novel challenges, like the rise of artificial intelligence. Making the best use of risk modeling tools involves not just optimizing software and data but using expert insight to interpret predictions and recommendations so that decision-making comes from a place of breadth and depth. Why are risk models important for banks and financial institutions? In the world of credit, optimizing risk assessment has clear ramifications when meeting overall business objectives. By using risk modeling to better understand your current and potential clients, you are positioned to offer the right credit products to the right audience and take action to mitigate risk. When it comes to portfolio risk management, having adequate risk models in place is paramount to meet targets. And not only does implementing quality portfolio risk analytics help maximize sales opportunities, but it can also help you identify risk proactively to avoid costly mistakes down the road. Risk mitigation tools are a key component of any risk modeling strategy and can help you maintain compliance, expose potential fraud, maximize the value of your portfolio and create a better overall customer experience. Advanced risk modeling techniques In the realm of risk modeling, the integration of advanced techniques like machine learning (ML) and artificial intelligence (AI) is revolutionizing how financial institutions assess and manage risk. These technologies enhance the predictive power of risk models by allowing for more complex data processing and pattern recognition than traditional statistical methods. Machine learning in risk modeling: ML algorithms can process vast amounts of unstructured data — such as market trends, consumer behavior and economic indicators — to identify patterns that may not be visible to human analysts. For instance, ML can be used to model credit risk by analyzing a borrower’s transaction history, social media activities and other digital footprints to predict their likelihood of default beyond traditional credit scoring methods. Artificial intelligence in decisioning: AI can automate the decisioning process in risk management by providing real-time predictions and risk assessments. AI systems can be trained to make decisions based on historical data and can adjust those decisions as they learn from new data. This capability is particularly useful in credit underwriting where AI algorithms can make rapid decisions based on market conditions. Financial institutions looking to leverage these advanced techniques must invest in robust data infrastructure, skilled personnel who can bridge the gap between data science and financial expertise, and continuous monitoring systems to ensure the models perform as expected while adhering to regulatory standards. Challenges in risk model validation Validating risk models is crucial for ensuring they function appropriately and comply with regulatory standards. Validation involves verifying both the theoretical foundations of a model and its practical implementation. Key challenges in model validation: Model complexity: As risk models become more complex, incorporating elements like ML and AI, they become harder to validate. Complex models can behave in unpredictable ways, making it difficult to understand why they are making certain decisions (the so-called "black box" issue). Data quality and availability: Effective validation requires high-quality, relevant data. Issues with data completeness, accuracy or relevance can lead to incorrect model validations. Regulatory compliance: With regulations continually evolving, keeping risk models compliant can be challenging. Different jurisdictions may have varying requirements, adding to the complexity of validation processes. Best practices: Regular reviews: Continuous monitoring and periodic reviews help ensure that models remain accurate over time and adapt to changing market conditions. Third-party audits: Independent reviews by external experts can provide an unbiased assessment of the risk model’s performance and compliance. These practices help institutions maintain the reliability and integrity of their risk models, ensuring that they continue to function as intended and comply with regulatory requirements. Read more: Blog post: What is model governance? How Experian can help Risk is inherent to business, and there's no avoiding it entirely. But integrating credit risk modeling into your operations can ensure stability and profitability in a rapidly evolving business landscape. Start with Experian's credit modeling services, which use expansive data, analytical expertise and the latest credit risk modeling methodologies to better predict risk and accelerate growth. Learn more *This article includes content created by an AI language model and is intended to provide general information.

Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

From science fiction-worthy image generators to automated underwriting, artificial intelligence (AI), big data sets and advances in computing power are transforming how we play and work. While the focus in the lending space has often been on improving the AI models that analyze data, the data that feeds into the models is just as important. Enter: data-centric AI. What is a data-centric AI? Dr. Andrew Ng, a leader in the AI field, advocates for data-centric AI and is often credited with coining the term. According to Dr. Ng, data-centric AI is, ‘the discipline of systematically engineering the data used to build an AI system.’1 To break down the definition, think of AI systems as a combination of code and data. The code is the model or algorithm that analyzes data to produce a result. The data is the information you use to train the model or later feed into the model to request a result. Traditional approaches to AI focus on the code — the models. Multiple organizations download and use the same data sets to create and improve models. But today, continued focus on model development may offer a limited return in certain industries and use cases. A data-centric AI approach focuses on developing tools and practices that improve the data. You may still need to pay attention to model development but no longer treat the data as constant. Instead, you try to improve a model's performance by increasing data quality. This can be achieved in different ways, such as using more consistent labeling, removing noisy data and collecting additional data.2 Data-centric AI isn't just about improving data quality when you build a model — it's also part of the ongoing iterative process. The data-focused approach should continue during post-deployment model monitoring and maintenance. Data-centric AI in lending Organizations in multiple industries are exploring how a data-centric approach can help them improve model performance, fairness and business outcomes. For example, lenders that take a data-centric approach to underwriting may be able to expand their lending universe, drive growth and fulfill financial inclusion goals without taking on additional risk. Conventional credit scoring models have been trained on consumer credit bureau data for decades. New versions of these models might offer increased performance because they incorporate changes in the economic landscape, consumer behavior and advances in analytics. And some new models are built with a more data-centric approach that considers additional data points from the existing data sets — such as trended data — to score consumers more accurately. However, they still solely rely on credit bureau data. Explainability and transparency are essential components of responsible AI and machine learning (a type of AI) in underwriting. Organizations need to be able to explain how their models come to decisions and ensure they are behaving as expected. Model developers and lenders that use AI to build credit risk models can incorporate new high-quality data to supplement existing data sets. Alternative credit data can include information from alternative financial services, public records, consumer-permissioned data, and buy now, pay later (BNPL) data that lenders can use in compliance with the Fair Credit Reporting Act (FCRA).* The resulting AI-driven models may more accurately predict credit risk — decreasing lenders' losses. The models can also use alternative credit data to score consumers that conventional models can't score. Infographic: From initial strategy to results — with stops at verification, decisioning and approval — see how customers travel across an Automated Loan Underwriting Journey. Business benefit of using data-centric AI models Financial services organizations can benefit from using a data-centric AI approach to create models across the customer lifecycle. That may be why about 70 percent of businesses frequently discuss using advanced analytics and AI within underwriting and collections.3 Many have gone a step further and implemented AI. Underwriting is one of the main applications for machine learning models today, and lenders are using machine learning to:4 More accurately assess credit risk models. Decrease model development, deployment and recalibration timelines. Incorporate more alternative credit data into credit decisioning. AI analytics solutions may also increase customer lifetime value by helping lenders manage credit lines, increase retention, cross-sell products and improve collection efforts. Additionally, data-centric AI can assist with fraud detection and prevention. Case study: Learn how Atlas Credit, a small-dollar lender, used a machine learning model and loan automation to nearly doubled its loan approval rates while decreasing its credit risk losses. How Experian helps clients leverage data-centric AI for better business outcomes During a presentation in 2021, Dr. Ng used the 80-20 rule and cooking as an analogy to explain why the shift to data-centric AI makes sense.5 You might be able to make an okay meal with old or low-quality ingredients. However, if you source and prepare high-quality ingredients, you're already 80% of the way toward making a great meal. Your data is the primary ingredient for your model — do you want to use old and low-quality data? Experian has provided organizations with high-quality consumer and business credit solutions for decades, and our industry-leading data sources, models and analytics allow you to build models and make confident decisions. If you need a sous-chef, Experian offers services and has data professionals who can help you create AI-powered predictive analytics models using bureau data, alternative data and your in-house data. Learn more about our AI analytics solutions and how you can get started today. 1DataCentricAI. (2023). Data-Centric AI.2Exchange.scale (2021). The Data-Centric AI Approach With Andrew Ng.3Experian (2021). Global Insights Report September/October 2021.4FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context. 5YouTube (2021). A Chat with Andrew on MLOps: From Model-Centric to Data-Centric AI *Disclaimer: When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.