Tag: data

We are squarely in the holiday shopping season. From the flurry of promotional emails to the endless shopping lists, there are many to-dos and even more opportunities for financial institutions at this time of year. The holiday shopping season is not just a peak period for consumer spending; it’s also a critical time for financial institutions to strategize, innovate, and drive value. According to the National Retail Federation, U.S. holiday retail sales are projected to approach $1 trillion in 2024, , and with an ever-evolving consumer behavior landscape, financial institutions need actionable strategies to stand out, secure loyalty, and drive growth during this period of heightened spending. Download our playbook: "How to prepare for the Holiday Shopping Season" Here’s how financial institutions can capitalize on the holiday shopping season, including key insights, actionable strategies, and data-backed trends. 1. Understand the holiday shopping landscape Key stats to consider: U.S. consumers spent $210 billion online during the 2022 holiday season, according to Adobe Analytics, marking a 3.5% increase from 2021. Experian data reveals that 31% of all holiday purchases in 2022 occurred in October, highlighting the extended shopping season. Cyber Week accounted for just 8% of total holiday spending, according to Experian’s Holiday Spending Trends and Insights Report, emphasizing the importance of a broad, season-long strategy. What this means for financial institutions: Timing is crucial. Your campaigns are already underway if you get an early start, and it’s critical to sustain them through December. Focus beyond Cyber Week. Develop long-term engagement strategies to capture spending throughout the season. 2. Leverage Gen Z’s growing spending power With an estimated $360 billion in disposable income, according to Bloomberg, Gen Z is a powerful force in the holiday market​. This generation values personalized, seamless experiences and is highly active online. Strategies to capture Gen Z: Offer digital-first solutions that enhance the holiday shopping journey, such as interactive portals or AI-powered customer support. Provide loyalty incentives tailored to this demographic, like cash-back rewards or exclusive access to services. Learn more about Gen Z in our State of Gen Z Report. To learn more about all generations' projected consumer spending, read new insights from Experian here, including 45% of Gen X and 52% of Boomers expect their spending to remain consistent with last year. 3. Optimize pre-holiday strategies Portfolio Review: Assess consumer behavior trends and adjust risk models to align with changing economic conditions. Identify opportunities to engage dormant accounts or offer tailored credit lines to existing customers. Actionable tactics: Expand offerings. Position your products and services with promotional campaigns targeting high-value segments. Personalize experiences. Use advanced analytics to segment clients and craft offers that resonate with their holiday needs or anticipate their possible post-holiday needs. 4. Ensure top-of-mind awareness During the holiday shopping season, competition to be the “top of wallet” is fierce. Experian’s data shows that 58% of high spenders shop evenly across the season, while 31% of average spenders do most of their shopping in December​. Strategies for success: Early engagement: Launch educational campaigns to empower credit education and identity protection during this period of increased transactions. Loyalty programs: Offer incentives, such as discounts or rewards, that encourage repeat engagement during the season. Omnichannel presence: Utilize digital, email, and event marketing to maintain visibility across platforms. 5. Combat fraud with multi-layered strategies The holiday shopping season sees an increase in fraud, with card testing being the number one attack vector in the U.S. according to Experian’s 2024 Identity and Fraud Study. Fraudulent activity such as identity theft and synthetic IDs can also escalate​. Fight tomorrow’s fraud today: Identity verification: Use advanced fraud detection tools, like Experian’s Ascend Fraud Sandbox, to validate accounts in real-time. Monitor dormant accounts: Watch these accounts with caution and assess for potential fraud risk. Strengthen cybersecurity: Implement multi-layered strategies, including behavioral analytics and artificial intelligence (AI), to reduce vulnerabilities. 6. Post-holiday follow-up: retain and manage risk Once the holiday rush is over, the focus shifts to managing potential payment stress and fostering long-term relationships. Post-holiday strategies: Debt monitoring: Keep an eye on debt-to-income and debt-to-limit ratios to identify clients at risk of defaulting. Customer support: Offer tailored assistance programs for clients showing signs of financial stress, preserving goodwill and loyalty. Fraud checks: Watch for first-party fraud and unusual return patterns, which can spike in January. 7. Anticipate consumer trends in the New Year The aftermath of the holidays often reveals deeper insights into consumer health: Rising credit balances: January often sees an uptick in outstanding balances, highlighting the need for proactive credit management. Shifts in spending behavior: According to McKinsey, consumers are increasingly cautious post-holiday, favoring savings and value-based spending. What this means for financial institutions: Align with clients’ needs for financial flexibility. The holiday shopping season is a time that demands precise planning and execution. Financial institutions can maximize their impact during this critical period by starting early, leveraging advanced analytics, and maintaining a strong focus on fraud prevention. And remember, success in the holiday season extends beyond December. Building strong relationships and managing risk ensures a smooth transition into the new year, setting the stage for continued growth. Ready to optimize your strategy? Contact us for tailored recommendations during the holiday season and beyond. Download the Holiday Shopping Season Playbook

In this article...What is fair lending?Understanding machine learning modelsThe pitfalls: bias and fairness in ML modelsFairness metricsRegulatory frameworks and complianceHow Experian® can help As the financial sector continues to embrace technological innovations, machine learning models are becoming indispensable tools for credit decisioning. These models offer enhanced efficiency and predictive power, but they also introduce new challenges. These challenges particularly concern fairness and bias, as complex machine learning models can be difficult to explain. Understanding how to ensure fair lending practices while leveraging machine learning models is crucial for organizations committed to ethical and compliant operations. What is fair lending? Fair lending is a cornerstone of ethical financial practices, prohibiting discrimination based on race, color, national origin, religion, sex, familial status, age, disability, or public assistance status during the lending process. This principle is enshrined in regulations such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA). Overall, fair lending is essential for promoting economic opportunity, preventing discrimination, and fostering financial inclusion. Key components of fair lending include: Equal treatment: Lenders must treat all applicants fairly and consistently throughout the lending process, regardless of their personal characteristics. This means evaluating applicants based on their creditworthiness and financial qualifications rather than discriminatory factors. Non-discrimination: Lenders are prohibited from discriminating against individuals or businesses on the basis of race, color, religion, national origin, sex, marital status, age, or other protected characteristics. Discriminatory practices include redlining (denying credit to applicants based on their location) and steering (channeling applicants into less favorable loan products based on discriminatory factors). Fair credit practices: Lenders must adhere to fair and transparent credit practices, such as providing clear information about loan terms and conditions, offering reasonable interest rates, and ensuring that borrowers have the ability to repay their loans. Compliance: Financial institutions are required to comply with fair lending laws and regulations, which are enforced by government agencies such as the Consumer Financial Protection Bureau (CFPB) in the United States. Compliance efforts include conducting fair lending risk assessments, monitoring lending practices for potential discrimination, and implementing policies and procedures to prevent unfair treatment. Model governance: Financial institutions should establish robust governance frameworks to oversee the development, implementation and monitoring of lending models and algorithms. This includes ensuring that models are fair, transparent, and free from biases that could lead to discriminatory outcomes. Data integrity and privacy: Lenders must ensure the accuracy, completeness, and integrity of the data used in lending decisions, including traditional credit and alternative credit data. They should also uphold borrowers’ privacy rights and adhere to data protection regulations when collecting, storing, and using personal information. Understanding machine learning models and their application in lending Machine learning in lending has revolutionized how financial institutions assess creditworthiness and manage risk. By analyzing vast amounts of data, machine learning models can identify patterns and trends that traditional methods might overlook, thereby enabling more accurate and efficient lending decisions. However, with these advancements come new challenges, particularly in the realms of model risk management and financial regulatory compliance. The complexity of machine learning models requires rigorous evaluation to ensure fair lending. Let’s explore why. The pitfalls: bias and fairness in machine learning lending models Despite their advantages, machine learning models can inadvertently introduce or perpetuate biases, especially when trained on historical data that reflects past prejudices. One of the primary concerns with machine learning models is their potential lack of transparency, often referred to as the "black box" problem. Model explainability aims to address this by providing clear and understandable explanations of how models make decisions. This transparency is crucial for building trust with consumers and regulators and for ensuring that lending practices are fair and non-discriminatory. Fairness metrics Key metrics used to evaluate fairness in models can include standardized mean difference (SMD), information value (IV), and disparate impact (DI). Each of these metrics offers insights into potential biases but also has limitations. Standardized mean difference (SMD). SMD quantifies the difference between two groups' score averages, divided by the pooled standard deviation. However, this metric may not fully capture the nuances of fairness when used in isolation. Information value (IV). IV compares distributions between control and protected groups across score bins. While useful, IV can sometimes mask deeper biases present in the data. Disparate impact (DI). DI, or the adverse impact ratio (AIR), measures the ratio of approval rates between protected and control classes. Although DI is widely used, it can oversimplify the complex interplay of factors influencing credit decisions. Regulatory frameworks and compliance in fair lending Ensuring compliance with fair lending regulations involves more than just implementing fairness metrics. It requires a comprehensive end-to-end approach, including regular audits, transparent reporting, and continuous monitoring and governance of machine learning models. Financial institutions must be vigilant in aligning their practices with regulatory standards to avoid legal repercussions and maintain ethical standards. Read more: Journey of a machine learning model How Experian® can help By remaining committed to regulatory compliance and fair lending practices, organizations can balance technological advancements with ethical responsibility. Partnering with Experian gives organizations a unique advantage in the rapidly evolving landscape of AI and machine learning in lending. As an industry leader, Experian offers state-of-the-art analytics and machine learning solutions that are designed to drive efficiency and accuracy in lending decisions while ensuring compliance with regulatory standards. Our expertise in model risk management and machine learning model governance empowers lenders to deploy robust and transparent models, mitigating potential biases and aligning with fair lending practices. When it comes to machine learning model explainability, Experian’s clear and proven methodology assesses the relative contribution and level of influence of each variable to the overall score — enabling organizations to demonstrate transparency and fair treatment to auditors, regulators, and customers. Interested in learning more about ensuring fair lending practices in your machine learning models?    Learn More This article includes content created by an AI language model and is intended to provide general information.

Experian’s award-winning platform now brings together market-leading data, generative AI and cutting-edge machine learning solutions for analytics, credit decisioning and fraud into a single interface — simplifying the deployment of analytical models and enabling businesses to optimize their practices. The platform updates represent a notable milestone, fueled by Experian’s significant investments in innovation over the last eight years as part of its modern cloud transformation.  “The evolution of our platform reaffirms our commitment to drive innovation and empower businesses to thrive. Its capabilities are unmatched and represent a significant leap forward in lending technology, democratizing access to data in compliant ways while enabling lenders of all sizes to seamlessly validate their customers’ identities with confidence, help expand fair access to credit and offer awesome user and customer experiences,” said Alex Lintner CEO Experian Software Solutions. The enhanced Experian Ascend Platform dramatically reduces time to install and offers streamlined access to many of Experian's award-winning integrated solutions and tools through a single sign-on and a user-friendly dashboard. Leveraging generative AI, the platform makes it easy for organizations of varying sizes and experience levels to pivot between applications, automate processes, modernize operations and drive efficiency. In addition, existing clients can easily add new capabilities through the platform to enhance business outcomes. Read Press Release Learn More Check out Experian Ascend Platform in the media: Transforming Software for Credit, Fraud and Analytics with Experian Ascend Platform™ (Episode 160) Reshaping the Future of Financial Services with Experian Ascend Platform Introducing Experian’s Cloud-based Ascend Technology Platform with GenAI Integration 7 enhancements of Experian Ascend Platform

Open banking is revolutionizing the financial services industry by encouraging a shift from a closed model to one with greater transparency, competition, and innovation. But what does this mean for financial institutions, and how can you adapt to this new landscape, balancing opportunity against risk? In this article, we will define open banking, illustrate how it operates, and weigh the challenges and benefits for financial institutions. What is open banking? Open banking stands at the forefront of financial innovation, embodying a shift toward a more inclusive, transparent, and consumer-empowered system. At its core, open banking relies on a simple yet powerful premise: it uses consumer-permissioned data to create a networked banking ecosystem that benefits both financial institutions and consumers alike.  By having secure, standardized access to consumer financial data — granted willingly by the customers themselves — lenders can gain incredibly accurate insights into consumer behavior, enabling them to personalize services and offers like never before. How does open banking work? Open banking is driven by Application Programming Interfaces (APIs), which are sets of protocols that allow different software components to communicate with each other and share data seamlessly and securely. In the context of open banking, these APIs enable: Account Information Services (AIS): These services allow third-party providers (TPPs) to access account information from financial institutions (with customer consent) to provide budgeting and financial planning services. Payment Initiation Services (PIS): These services permit TPPs to initiate payments on behalf of customers, often offering alternative, faster, or cheaper payment solutions compared to traditional banking methods. Financial institutions must develop and maintain robust and secure APIs that TPPs can integrate with. This requires significant investment in technology and cybersecurity to protect customer data and financial assets. There must also be clear customer consent procedures and data-sharing agreements between financial institutions and TPPs. Benefits of open banking Open banking is poised to create a wave of innovation in the financial sector. One of the most significant benefits is the ability to gain a more comprehensive view of a consumer’s financial situation. With a deeper view of consumer cashflow data and access to actionable insights, you can improve your underwriting strategy, optimize account management and make smarter decisions to safely grow your portfolio.  Additionally, open banking promotes financial inclusion by enabling financial institutions to offer more tailored products that suit the needs of previously underserved or unbanked populations. This inclusivity can help bridge the gap in financial services, making them accessible to a broader segment of the population. Furthermore, open banking fosters competition among financial institutions and fintech companies, leading to the development of better products, services, and competitive pricing. This competitive environment not only benefits consumers but also challenges banks to innovate, improve their services, and operate more efficiently. The collaborative nature of open banking encourages an ecosystem where traditional banks and fintech startups co-create innovative open banking solutions. This synergy can accelerate the pace of digital transformation within the banking sector, leading to the development of cutting-edge technologies and platforms that address specific market gaps or consumer demands.  Challenges of open banking While open banking presents a plethora of opportunities, its adoption is not without challenges. Financial institutions must grapple with several hurdles to fully leverage the benefits open banking offers. One of the most significant challenges is fraud detection in banking and ensuring data security and privacy. The sharing of financial data through APIs necessitates robust cybersecurity measures to protect sensitive information from breaches and fraud. Banks and TPPs alike must invest in advanced security technologies and protocols to safeguard customer data. Additionally, regulatory compliance poses a considerable challenge. Open banking regulations vary widely across different jurisdictions, requiring banks to adapt their operations to comply with diverse legal frameworks. Staying abreast of evolving regulations and ensuring compliance can be resource-intensive and complex. Furthermore, customer trust and awareness are crucial to the success of open banking. Many consumers are hesitant to share their financial data due to privacy concerns. Educating customers on the benefits of open banking and the measures taken to ensure their data’s security is essential to overcoming this obstacle. Despite these challenges, the strategic implementation of open banking can unlock remarkable opportunities for innovation, efficiency, and service enhancement in the financial sector. Banks that can successfully navigate these hurdles and capitalize on the advantages of open banking are likely to emerge as leaders in the new era of financial services. Our open banking strategy Our newly introduced open banking solution, Cashflow Attributes, powered by Experian’s proprietary data from millions of U.S. consumers, offers unrivaled categorization and valuable consumer insights. The combination of credit and cashflow data empowers lenders with a deeper understanding of consumers. Furthermore, it harnesses our advanced capabilities to categorize 99% of transaction Demand Deposit Account (DDA) and credit card data, guaranteeing dependable inputs for robust risk assessment, targeted marketing and proactive fraud detection.  Watch open banking webinar Learn more about Cashflow Attributes

Know Your Customer (KYC) procedures are a requirement for banks and other financial institutions to collect and verify the identity of their customers. When a bank verifies the identity of another organization or its owners, the process may be called Know Your Business (KYB) instead.  As part of banks’ anti-money laundering (AML) programs, KYC can help stop corruption, money laundering and terrorist financing. Creating and maintaining KYC programs is also important for regulatory compliance, reputation management and fraud prevention.  READ: How to Build a Know Your Customer Checklist – Everything You Need to Know The three components of KYC programs Banks can largely determine how to set up their KYC and AML programs within the applicable regulatory guidelines. In the United States, KYC needs to happen when banks initially onboard a new customer. But it’s not a one-and-done event—ongoing customer and transaction monitoring is also important.  Customer Identification Program (CIP) Creating a robust Customer Identification Program (CIP) is an essential part of KYC. At a minimum, a bank’s CIP requires it to collect the following information from new customers: Name Date of birth Address Identification number, such as a Social Security number (SSN) or Employer Identification Number (EIN) Banks' CIPs also have to use risk-based procedures to verify customers’ identities and form a reasonable belief that they know the customer's true identity.1 This might involve comparing the information from the application to the customer’s government-issued ID, other identifying documents and authoritative data sources, such as credit bureau databases. Additionally, the bank's CIP will govern how the bank:  Retains the customer’s identifying information Compares customer to government lists  Provides customers with adequate notices Banks can create CIPs that meet all the requirements in various ways, and many use third-party solutions to quickly collect data, detect forged or falsified documents and verify the provided information.  INFOGRAPHIC: Streamlining the Digital Onboarding Process: Beating Fraud at its Game Customer due diligence (CDD)  CIP and CDD overlap, but the CIP primarily verifies a customer’s identity while customer due diligence (CDD) helps banks understand the risk that each customer poses. To do this, banks try to understand what various types of customers do, what those customers’ normal banking activity looks like, and in contrast, what could be unusual or suspicious activity.  Financial institutions can use risk ratings and scores to evaluate customers and then use simplified, standard or enhanced due diligence (EDD) processes based on the results. For example, customers who might pose a greater risk of laundering money or financing terrorism may need to undergo additional screenings and clarify the source of their funds. Ongoing monitoring Ongoing or continuous monitoring of customers’ identities and transactions is also important for staying compliant with AML regulations and stopping fraud.  The monitoring can help banks spot a significant change in the identity of the customer, beneficial owner or account, which may require a new KYC check. Unusual transactions can also be a sign of money laundering or fraud, and they may require the bank to file a suspicious activity report (SAR). Why is KYC important in banking? Understanding and implementing KYC processes can be important for several reasons:  Regulatory compliance: Although the specific laws and rules can vary by country or region, many banks are required to have AML procedures, including KYC. The fines for violating AML regulations can be in the hundreds of millions— a few banks have been fined over $1 billion for lax AML enforcement and sanctions breaching. Reputation management: In some cases, enforcement actions and fines were headline news. Banks that don’t have robust KYC procedures in place risk losing their customers' trust and respect.  Fraud prevention: In addition to the regulatory requirements, KYC policies and systems can also work alongside fraud management solutions for banks. Identity verification at onboarding can help banks identify synthetic identities attempting to open money mule accounts or take out loans. Ongoing monitoring can also be important for identifying long-term fraud schemes and large fraud rings.  ON-DEMAND WEBINAR: Fraud Strategies for a Positive Customer Experience KYC in a digital-first world Many financial institutions have been going through digital transformations. Part of that journey is updating the systems and tools in place to meet the expectations of customers and regulators.  An Experian survey found that about half of consumers (51 percent) consider abandoning the creation of a new account because of friction or a less-than-positive experience — that increased to 69 percent for high-income households.2 The survey wasn’t specific to financial services, but friction could be a problem for banks wanting to attract new account holders. Just as access to additional data sources and machine learning help automate underwriting, financial institutions can use technological advances to add an appropriate amount of friction based on various risk signals. Some of these can be run in the background, such as an electronic Consent Based Social Security Number Verification (eCBSV) check to verify the customer’s name, SSN and date of birth match the Social Security Administration’s records. Others may require more customer involvement, such as taking a selfie that’s then compared to the image on their photo ID — Experian CrossCore® Doc Capture enables this type of verification.  Experian is a leader in identity and data management  Experian's identity verification solutions use proprietary and third-party data to help banks manage their KYC procedures, including identity verification and Customer Identification Programs. By bundling identity verification with fraud assessment, banks can stop fraudsters while quickly resolving identity discrepancies. The automated processes also allow you to offer a low-friction identity verification experience and use step-up authentications as needed.  Learn more about Experian’s identity solutions.  1FDIC (2021). Customer Identification Program 2Experian (2023). Experian's 2023 Identity and Fraud Report

This article was updated on February 23, 2024. First impressions are always important – whether it’s for a job interview, a first date or when pitching a client. The same goes for financial services onboarding as it’s an opportunity for organizations to foster lifetime loyalty with customers. As a result, financial institutions are on the hunt now more than ever for frictionless online identity verification methods to validate genuine customers and maintain positive experiences during the online onboarding process. In a predominantly digital-first world, financial companies are increasingly focused on the customer experience and creating the most seamless online onboarding process. However, according to Experian’s 2023 Identity and Fraud Report, more than half of U.S. consumers considered dropping out during account opening due to friction and a less-than positive experience. And as technology continues to advance, digital financial services onboarding, not surprisingly, increases the demand for fraud protection and authentication methods – namely with digital identity (ID) verification processes. According to Experian’s report, 64% of consumers are very or somewhat concerned with online security, with identity theft being their top concern. So how can financial institutions guarantee a frictionless online onboarding experience while executing proper authentication methods and maintaining security and fraud detection? The answer? While a “frictionless” experience can seem like a bit of a unicorn, there are some ways to get close: Utilizing better data - Digital devices offer an extensive amount of data that’s useful in determining risk. Characteristics that allow the identification of a specific device, the behaviors associated with the device and information about a device’s owner can be captured without adding friction for the user. Analytics – Once the data is collected, advanced analytics uses information based on behavioral data, digital intelligence, phone intelligence and email intelligence to analyze for risk. While there’s friction in the initial ask for the input data, the risk prediction improves with more data. Document verification and biometric identity verification – Real-time document verification used in conjunction with facial biometrics, behavioral biometrics and other physical characteristics allows for rapid onboarding and helps to maintain a low friction customer journey. Financial institutions can utilize document verification to replace manual long-form applications for rapid onboarding and immediately verify new data at the point of entry. Using their mobile phones, consumers can photograph and upload identity documents to pre-fill applications. Document authenticity can be verified in real-time. Biometrics, including facial, behavioral, or other physical characteristics (like fingerprints), are low-touch methods of customer authentication that can be used synchronously with document verification. Optimize your financial services onboarding process Experian understands how critical identity management and fraud protection is when it comes to the online onboarding process and identity verification. That’s why we created layered digital identity verification and risk segmentation solutions to help legitimize your customers with confidence while improving the customer experience. Our identity verification solutions use advanced technology and capabilities to correctly identify and verify real customers while mitigating fraud and maintaining frictionless customer experiences. Learn more

This article was updated on February 21, 2024. With the rise of technology and data analytics in the financial industry today, it's no longer enough for companies to rely solely on traditional marketing methods. Data-driven marketing insights provide a more sophisticated and comprehensive view of shifting customer preferences and behaviors. With this in mind, this blog post will highlight the importance of data-driven marketing insights, particularly for financial institutions. The importance of data-driven marketing insights 30% of companies say poor data quality is a key challenge to delivering excellent customer experiences. Today’s consumers want personalized experiences built around their individual needs and preferences. Data-driven marketing insights can help marketers meet this demand, but only if it is fresh and accurate. When extending firm credit offers to consumers, lenders must ensure they reach individuals who are both creditworthy and likely to respond. Additionally, their message must be relevant and delivered at the right time and place. Without comprehensive data insights, it can be difficult to gauge whether a consumer is in the market for credit or determine how to best approach them. READ: Case study: Deliver timely and personalized credit offers The benefits of data-driven marketing insights By drawing data-driven marketing insights, you can reach and engage the best customers for your business. This means: Better understanding current and potential customers To increase response and conversion rates, organizations must identify high-propensity consumers and create personalized messaging that resonates. By leveraging customer data that is valid, fresh, and regularly updated, you’ll gain deeper insights into who your customers are, what they’re looking for and how to effectively communicate with them. Additionally, you can analyze the performance of your campaigns and better predict future behaviors. Utilizing technology to manage your customer data With different sources of information, it’s imperative to consolidate and optimize your data to create a single customer view. Using a data-driven technology platform, you can break down data silos by collecting and connecting consumer information across multiple sources and platforms. This way, you can make data available and accessible when and where needed while providing consumers with a cohesive experience across channels and devices. Monitoring the accuracy of your data over time Data is constantly changing, so implementing processes to effectively monitor and control quality over time is crucial. This means leveraging data quality tools that perform regular data cleanses, spot incomplete or duplicated data, and address common data errors. By monitoring the accuracy of your data over time, you can make confident decisions and improve the customer experience. Turning insights into action With data-driven marketing insights, you can level up your campaigns to find the best customers while decreasing time and dollars wasted on unqualified prospects. Visit us to learn more about how data-driven insights can power your marketing initiatives. Learn more Enhance your marketing strategies today This article includes content created by an AI language model and is intended to provide general information.

Spoiler alert: Gen AI is everywhere, including the top of Experian’s list of fraud trends 2024. “The speed and complexity of fraud attacks due to new technology and sophisticated fraudsters is leaving both businesses and consumers at risk in 2024,” said Kathleen Peters, chief innovation officer at Experian Decision Analytics in North America. “At Experian, we’re constantly innovating to deliver data-driven solutions to help our customers fight fraud and to protect the consumers they serve.” To deter fraudulent activity in 2024, businesses and consumers must get tactical for their fraud fighting strategies. And for businesses, the need for more sophisticated fraud protection solutions leveraging data and technology is greater than ever before. Experian suggests consumers and businesses watch out for these big five rounding out our fraud trends 2024. Generative AI: Generative AI accelerates DIY fraud: Experian predicts fraudsters will use generative AI to accelerate “do-it-yourself” fraud ranging from deepfake content – think emails, voice and video – as well as code creation to set up scam websites. A previous blog post of ours highlighted four types of generative AI used for fraud, including fraud automation at scale, text content generation, image and video manipulation and human voice generation. The way around it? Fight AI fraud with AI as part of a multilayered fraud prevention solution. Fraud at bank branches: Bank branches are making a comeback. A growing number of consumers prefer visiting bank branches in person to open new accounts or get financial advice with the intent to conduct safer transactions. However, face-to-face verification is not flawless and is still susceptible to human error or oversight. According to an Experian report, 85% of consumers report physical biometrics as the most trusted and secure authentication method they’ve recently encountered, but the measure is only currently used by 32% of businesses to detect and protect against fraud. Retailers, beware: Not all returns are as they appear. Experian predicts an uptick in cases where customers claim to return their purchases, only for the business to receive an empty box in return. Businesses must be vigilant with their fraud strategy in order to mitigate risk of lost goods and revenue. Synthetic identity fraud will surge: Pandemic-born synthetic identities may have been dormant, but now have a few years of history, making it easier to elude detection leading to fraudsters using those dormant accounts to “bust out” over the next year. Cause-related and investment deception: Fraudsters are employing new methods that strike an emotional response from consumers with cause-related asks to gain access to consumers’ personal information. Experian predicts that these deceptive cause-related methods will surge in 2024 and beyond. How businesses and consumers feel about fraud in 2024 According to an Experian report, over half of consumers feel they’re more of a fraud target than a year ago and nearly 70% of businesses report that fraud losses have increased in recent years. Business are facing mounting challenges – from first-party fraud and credit washing to synthetic identity and the yet-to-be-known impacts generative AI may have on fraud schemes. Synthetic identity fraud has been mentioned in multiple Experian Fraud Forecasts and the threat is ever growing. As technology continues to enhance consumers’ connectedness, it also heightens the stakes for various fraud attacks. As highlighted by this list of fraud trends 2024, the ways that fraudsters are looking to deceive is increasing from all angles. “Now more than ever, businesses need to implement a multilayered approach to their identity verification and fraud prevention strategies that leverages the latest technology available,” said Peters. Consumers are increasingly at risk from sophisticated fraud schemes. Increases in direct deposit account and check fraud, as well as advanced technologies like deepfakes and AI-generated phishing emails, put consumers in a precarious position. The call to action for consumers is to remain vigilant of seemingly authentic interactions. Experian can help with your fraud strategy To learn more about Experian’s fraud prevention solutions, please visit https://www.experian.com/business/solutions/fraud-management.  Download infographic Watch Future of Fraud webinar

The threat of data breach is constant in our modern, digital world. And as technology advances, so do the strategies and tactics of malicious actors seeking ways to monetize the vulnerabilities of organizations. It’s not a matter of if, but when, a data breach could impact your organization, and it is important for businesses to understand how to operate in it. What is a Data Breach? For many organizations, a data breach is arguably one of the greatest threats to prevent. What is a data breach? Imagine your organization as a fortress, safeguarding a treasure trove of sensitive information—customer data, financial records, proprietary algorithms. A data breach is the unwelcome intrusion into this fortress, where unauthorized individuals gain access to confidential information, often with malicious intent. This can encompass many types of data, including personal identification information (PII), financial data, and intellectual property. Classifications of breaches can vary from intentional cyberattacks to inadvertent exposure due to system vulnerabilities or human error. To grasp the gravity of data breaches, Businesses face tangible consequences when their defenses are breached, and there are no signs of it slowing down. The frequency and severity of data breaches are alarming. According to recent studies¹, the healthcare sector experienced a 55% increase in data breaches in 2022. No business is immune to the evolving threat landscape especially companies that capture customer data and are also inherently the stewards of this data. Understanding the landscape of data breaches will help you better fortify your business against a breach. In the next sections, we’ll explore the causes, impacts, post-breach response strategies, and preventative tactics businesses can employ to safeguard their data. Causes of Data Breaches Human error Even the most well-intentioned employees can become the weak link in an organization’s security chain. According to the “2023 Verizon Data Breach Investigations Report,” 74% of data breaches involve a human element². Investing in comprehensive training programs is essential to foster a culture of cybersecurity awareness and mitigate the risk of employee-related mistakes. Cybersecurity vulnerabilities The digital landscape is rife with potential vulnerabilities, and cybercriminals are adept at exploiting them. Regular cybersecurity assessments, prompt system updates, and the implementation of robust security protocols are recommended proactive measures to fortify against breaches that capitalize on system vulnerabilities. Insider threats Data breaches can originate from within, whether through disgruntled employees with malicious intent or well-meaning staff who inadvertently compromise security. Gurucul’s “2023 Insider Threat Report” highlights that 60% of organizations experienced insider-related incidents in the past year³. Establishing stringent access controls, closely monitoring user activities, and implementing employee education programs are vital steps to mitigate the risks associated with insider threats. Weak and Stolen Passwords Weak and stolen passwords stand as one of the most common gateways for data breaches. Cybercriminals exploit individuals who use easily guessable passwords or recycle them across multiple platforms. This creates a vulnerability that can be easily exploited through automated attacks. Ensuring robust password policies, employing multi-factor authentication, and regularly updating credentials are necessary measures to thwart these breaches and safeguard sensitive information. Malware The insidious world of malware is a persistent threat to data security. Malicious software, often disguised as innocuous files or links, infiltrates systems, and wreak havoc by compromising data integrity and confidentiality. Malware can then swiftly spread, leading to unauthorized access and data exfiltration. Regularly updating antivirus software, conducting thorough system scans, and educating employees about the dangers of clicking on suspicious links are pivotal defenses against malware-driven breaches. Social Engineering Social engineering has emerged as a cunning and effective tactic in data breaches, such as manipulating individuals to divulge confidential information willingly. Whether through phishing emails, deceptive phone calls, or impersonation, cybercriminals exploit human trust to gain unauthorized access. Raising awareness among employees about the dangers of social engineering, implementing rigorous verification processes, and fostering a culture of skepticism can fortify an organization’s defenses against these subtle yet potent attacks. Physical Attacks While the digital realm often takes center stage, physical attacks on data infrastructure remain a tangible and underestimated risk. Breaches can occur through unauthorized access to servers, theft of physical storage devices, or tampering with network equipment. Implementing stringent access controls, employing surveillance systems, and securing physical infrastructure are crucial steps to mitigate the threat of data breaches stemming from physical incursions. Building digital and physical protective measures can help with your defense against the multifaceted landscape of data breaches. Impacts on Businesses Financial repercussions Data breaches are costly to businesses with immediate and enduring consequences. The “Cost of a Data Breach Report 2023” by IBM reported that the average cost of a data breach was $4.45 million per organization⁴. Long-term financial implications include loss of customers, diminished revenue streams, and increased cybersecurity investments to rebuild trust and fortify defenses against future breaches. Reputational damage The fallout from a data breach extends beyond the balance sheet, leaving an indelible mark on a business’s reputation. According to a 2023 survey by Vercara, 66% of U.S. consumers would not trust a company that falls victim to a data breach with their data. Rebuilding trust with transparent communication, swift remediation, and proactive measures to prevent future breaches is essential, demonstrating a commitment to safeguarding sensitive information. Operational disruptions Data breaches causes disruptions in the operations of daily business activities. It takes an average of 73 days to contain a cyber-attack according to the Cost of a Data Breach Report 2023 from IBM⁴. Swift recovery requires a meticulous balance between addressing the breach’s immediate impact and resuming normal operations to minimize further operational strain. Legal and regulatory implications The legal aftermath of a data breach involves navigating a complex landscape of regulations and compliance standards. In the United States, data breaches may trigger legal consequences under various state laws. For instance, the California Consumer Privacy Act (CCPA) allows for fines ranging from $100 to $750 per consumer per incident⁵. Ensuring adherence to data protection laws, promptly reporting breaches to regulatory authorities, and implementing robust security measures become top priorities in avoiding the legal quagmire that often follows a data breach. Notable data breaches Yahoo! (2014): The personal information of 3 billion people was exposed, including names, birth dates, passwords, and phone numbers. Cause: It is believed that the hack originated through a phishing email sent to a Yahoo! employee. Through this phishing email, it’s believed the hackers were able to access user databases and tools.⁶ Cost: $117.5 million in settlements and $350 million off its sale price to Verizon⁷ Marriott International (2018): Information of approximately 500 million guests was compromised, including names, contact details, passport numbers, and travel details. Cause: A cyber-espionage campaign linked to a state-sponsored actor. Attackers gained access to Marriott’s Starwood guest reservation database due to vulnerabilities in the system.⁸ Cost: Over $100 million for remediation efforts and regulatory fines.⁹ Capital One (2019): 106 million customers’ personal information, including credit card applications and Social Security numbers, was exposed. Cause: A misconfigured web application firewall that allowed a hacker to exploit a server-side request forgery vulnerability, leading to unauthorized access and the theft of sensitive customer data.¹⁰ Cost: Estimated between $100 million and $150 million in 2019 alone.¹¹ SolarWinds (2020): Hackers compromised the software supply chain, affecting numerous government agencies and major corporations globally. Cause: The SolarWinds breach was a sophisticated supply chain attack where malicious actors compromised the software update process, injecting malware into software updates distributed by SolarWinds, allowing them access to numerous government and corporate networks.¹² Cost: At least $18 million¹³ JBS USA (2021): The ransomware attack on the world’s largest meat processor disrupted operations and impacted the company’s IT systems. Cause: A ransomware attack, where cybercriminals exploited vulnerabilities in the company’s IT systems to encrypt data and demand a ransom for its release, causing significant disruptions to operations.¹⁴ Cost: $11 million ransom paid to hackers from JBS to restore their IT systems. Post-breach response Assessment and Damage Control Immediate Action Steps In the event of a data breach, the immediacy of response becomes one factor in determining the outcome. Swift and decisive actions during the initial moments can be instrumental in preventing the situation from escalating. The primary focus at this stage is isolating the affected systems, swiftly disconnecting compromised servers and devices from the network. This can help stop unauthorized access and establishes the foundation for a more concentrated and effective response. Alerting the incident response team, IT personnel, and relevant stakeholders promptly is also worth considering to help gain control over the situation. Forensic Analysis Understanding the who, what, and how of an incident is also an important step following a breach. In this context, involving forensic experts in a meticulous analysis is prudent. These professionals specialize in unraveling the intricacies of the breach, identifying entry points, and tracing the movements of attackers within your systems. The significance of forensic analysis extends beyond mere identification; it serves as the groundwork for prevention. Through a comprehensive study of the employed attack vectors and techniques, organizations can enhance their cybersecurity infrastructure. This process of gathering critical information about the breach contributes to the ability to preempt similar incidents, fostering a more resilient stance against evolving cyber threats. Communication Strategy Internal Communication Effective internal communication plays a pivotal role in building a resilient response framework. In the early stages of a crisis, employees emerge as the initial line of defense. Clearly conveying the severity of the situation provides them with a comprehensive understanding of the impact and the organization’s devised response plan. This also empowers the workforce, fostering a sense of unity within the organization and help the organization navigate challenges ahead cohesively, reinforcing its resilience in the face of adversity. External Communication External communication holds equal importance, reaching beyond the organization to customers, partners, and stakeholders. It’s essential to recognize the significance of constructing messages with transparency, honesty, and a proactive stance. Silence or ambiguity can intensify the repercussions, so prioritizing openness becomes foundational for rebuilding trust. Being timely and forthright in sharing information about the breach and the steps taken to rectify the situation is generally a good strategy when engaging with partners and stakeholders. This approach not only informs but can also mold the perception of the organization’s dedication to security and integrity following the aftermath of a breach with a strategic and forward-thinking mindset. Legal and Regulatory Compliance Notification Requirements Within the regulatory framework, a prompt response is an important post-breach step for organizations. It may first involve comprehensively detailing the legal obligations surrounding breach notifications to both regulatory authorities and affected individuals. It’s essential to recognize the variability in requirements across different regions and industries, underscoring the importance of remaining well-informed about these specific nuances. Timeliness of notifications is also factor for organizations to consider. Numerous jurisdictions impose substantial fines for delays in reporting, making it essential for organizations to adhere to strict timelines. Transparency holds equal weight, necessitating clear communication about the extent of the breach, the nature of compromised information, and the specific measures being implemented to address the situation. This approach can help in being compliant with legal standards and plays a vital role in fostering trust among those directly impacted by the breach. Legal Counsel Engagement Organizations generally seek the support of legal counsel to help navigate the intricate legal aftermath of a data breach. Legal experts can help an organization through potential lawsuits and regulatory fines. Engaging legal experts early allows their insights to guide the overall strategy, shaping everything from the communication plan to the recovery efforts. With early legal counsel support, the organization can be proactive in addressing legal challenges, potentially mitigating the severity of consequences that may arise. Recovery and Remediation IT System Restoration The intricacies of IT system restoration mirror the reconstruction of a fortress following an intrusion. Restoring affected IT systems to normal functionality involves comprehensive measures such as thorough system checks, vulnerability assessments, and the eradication of any residual traces left by a breach. Additionally, organizations generally look to enhance security measures during the recovery phase. Simply reverting to the pre-breach state is not enough; instead, the recovery process serves as an opportunity to accept vulnerabilities in old systems and bolster defenses. This entails updating and patching systems, reassessing access controls, and contemplating the incorporation of advanced threat detection tools. Such measures collectively work to minimize the risk of a recurrence and contribute to an overall fortified cybersecurity posture. Prevention Strategies Best practices for securing sensitive data Securing sensitive data is important in the age of relentless cyber threats. Employing encryption protocols, conducting regular security audits, and limiting access privileges are foundational best practices. These proactive measures help create a robust defense, forming an intricate web that shields critical information from potential breaches. Employee training programs to mitigate human error Human error remains a significant contributor to data breaches. Implementing comprehensive employee training programs can be helpful in cultivating a security-conscious workforce and mitigating human error-caused vulnerabilities. From recognizing phishing attempts to practicing proper password hygiene, a well-informed staff acts as the first line of defense and can significantly reduce the likelihood of unintentional security lapses. Implementing robust cybersecurity measures The cornerstone of any data breach prevention strategy is the implementation of robust cybersecurity measures. This includes advanced intrusion detection systems, firewalls, and regular software updates. Proactively addressing vulnerabilities and staying abreast of the latest cybersecurity advancements help fortify an organization’s digital perimeter, creating an environment that is inherently resistant to malicious infiltrations. Staying abreast of emerging trends Staying ahead of data breach threats requires a keen awareness of emerging trends. From sophisticated phishing techniques to novel forms of malware, businesses should continuously adapt their cybersecurity strategies against evolving tactics employed by cybercriminals. The dynamic nature of the cybersecurity landscape demands constant innovation. Adopting cutting-edge technologies like artificial intelligence for threat detection and investing in predictive analytics allows businesses to stay one step ahead, proactively identifying and neutralizing potential threats before they escalate. Collaboration and information-sharing within industries In the face of evolving cyber threats, collaboration is a powerful defense. Establishing networks for information-sharing within industries enables businesses to benefit from collective intelligence. By sharing best practices and threat intelligence, organizations can collectively strengthen their defenses against the ever-changing data breach landscape. Takeaway Data breaches are a persistent threat for all businesses capturing and storing personal identifiable information. Such businesses are inherently the stewards of this data and must protect that data to avoid bad actors gaining access for malicious intent. Knowing what a data breach is just the first step of protecting that data, and it is key to take action. From securing sensitive data to fostering a cybersecurity-aware workforce, businesses must not merely react to the escalating threat of data breaches but proactively strive to create an impenetrable shield around their valuable information. Visit our website for more information about our offerings and how Experian can help you prepare and respond to data breaches. ¹Hippa Journal, 55% of Healthcare Organizations Suffered a Third-Party Data Breach in the Past Year [2022]²Verizon, 2023 Verizon Data Breach Investigations Report³Gurucul, 2023 Insider Threat Report⁴IBM, Cost of a Data Breach Report 2023⁵Office of the Attorney General, California Consumer Privacy Act (CCPA)⁶CSO, INside the Russian hack of Yahoo: How they did it⁷BPB Online, Yahoo Data Breach: What Actually Happened?⁸CSO, Marriott data breach FAQ: How did it happen and what was the impact?⁹Cybersecurity Dive, Marriott finds financial reprieve in reduced GDPR penalty¹⁰Investopedia, Capital One Data Breach Impacts 106 Million Customers¹¹CNET, Capital One $190 Million Data Breach Settlement: Today Is the Last Day to Claim Money¹²Tech Target, SolarWinds hack explained: Everything you need to know¹³Reuters, SolarWinds says dealing with hack fallout cost at least $18 million¹⁴BBC, Meat giant JBS pays $11m in ransom to resolve cyber-attack

Meeting Know Your Customer (KYC) regulations and staying compliant is paramount to running your business with ensured confidence in who your customers are, the level of risk they pose, and maintained customer trust. What is KYC?KYC is the mandatory process to identify and verify the identity of clients of financial institutions, as required by the Financial Conduct Authority (FCA). KYC services go beyond simply standing up a customer identification program (CIP), though that is a key component. It involves fraud risk assessments in new and existing customer accounts. Financial institutions are required to incorporate risk-based procedures to monitor customer transactions and detect potential financial crimes or fraud risk. KYC policies help determine when suspicious activity reports (SAR) must be filed with the Department of Treasury’s FinCEN organization. According to the Federal Financial Institutions Examinations Council (FFIEC), a comprehensive KYC program should include:• Customer Identification Program (CIP): Identifies processes for verifying identities and establishing a reasonable belief that the identity is valid.• Customer due diligence: Verifying customer identities and assessing the associated risk of doing business.• Enhanced customer due diligence: Significant and comprehensive review of high-risk or high transactions and implementation of a suspicious activity-monitoring system to reduce risk to the institution. The following organizations have KYC oversight: Federal Financial Institutions Examinations Council (FFIEC), Federal Reserve Board, Federal Deposit Insurance Corporation (FDIC), national Credit Union Administration (NCUA), Office of the Comptroller of the Currency (OCC) and the Consumer Financial Protection Bureau (CFPB). How to get started on building your Know Your Customer checklist 1. Define your Customer Identification Program (CIP) The CIP outlines the process for gathering necessary information about your customers. To start building your KYC checklist, you need to define your CIP procedure. This may include the documentation you require from customers, the sources of information you may use for verification and the procedures for customer due diligence. Your CIP procedure should align with your organization’s risk appetite and be comply with regulations such as the Patriot Act or Anti-money laundering laws. 2. Identify the customer's information Identifying the information you need to gather on your customer is key in building an effective KYC checklist. Typically, this can include their first and last name, date of birth, address, phone number, email address, Social Security Number or any government-issued identification number. When gathering sensitive information, ensure that you have privacy and security controls such as encryption, and that customer data is not shared with unauthorized personnel. 3. Determine the verification method There are various methods to verify a customer's identity. Some common identity verification methods include document verification, facial recognition, voice recognition, knowledge-based authentication, biometrics or database checks. When selecting an identity verification method, consider the accuracy, speed, cost and reliability. Choose a provider that is highly secure and offers compliance with current regulations. 4. Review your checklist regularly Your KYC checklist is not a one and done process. Instead, it’s an ongoing process that requires periodic review, updates and testing. You need to periodically review your checklist to ensure your processes are up to date with the latest regulations and your business needs. Reviewing your checklist will help your business to identify gaps or outdated practices in your KYC process. Make changes as needed and keep management informed of any changes. 5. Final stage: quality control As a final step, you should perform a quality control assessment of the processes you’ve incorporated to ensure they’ve been carried out effectively. This includes checking if all necessary customer information has been collected, whether the right identity verification method was implemented, if your checklist matches your CIP and whether the results were recorded correctly. KYC is a vital process for your organization in today's digital age. Building an effective KYC checklist is essential to ensure compliance with regulations and mitigate risk factors associated with fraudulent activities. Building a solid checklist requires a clear understanding of your business needs, a comprehensive definition of your CIP, selection of the right verification method, and periodic reviews to ensure that the process is up to date. Remember, your customers' trust and privacy are at stake, so iensuring that your security processes and your KYC checklist are in place is essential. By following these guidelines, you can create a well-designed KYC checklist that reduces risk and satisfies your regulatory needs. Taking the next step Experian offers identity verification solutions as well as fully integrated, digital identity and fraud platforms. Experian’s CrossCore & Precise ID offering enables financial institutions to connect, access and orchestrate decisions that leverage multiple data sources and services. By combining risk-based authentication, identity proofing and fraud detection into a single, cloud-based platform with flexible orchestration and advanced analytics, Precise ID provides flexibility and solves for some of financial institutions’ biggest business challenges, including identity and fraud as it relates to digital onboarding and account take over; transaction monitoring and KYC/AML compliance and more, without adding undue friction. Learn more *This article includes content created by an AI language model and is intended to provide general information.

For companies that regularly engage in financial transactions, having a customer identification program (CIP) is mandatory to comply with the regulations around identity verification requirements across the customer lifecycle. In this blog post, we will delve into the essentials of a customer identification program, what it entails, and why it is important for businesses to implement one. What is a Customer Identification Program (CIP)? A CIP is a set of procedures implemented by financial institutions to verify the identity of their customers. The purpose of a CIP is to be a part of a financial institution’s fraud management solutions, with similar goals as to detect and prevent fraud like money laundering, identity theft, and other fraudulent activities. The program enables financial institutions to assess the risk level associated with a particular customer and determine whether their business dealings are legitimate. An effective CIP program should check the following boxes: Confidently verify customer identities Seamless authentication Understand and anticipate customer activities Where does Know Your Customer (KYC) fit in? KYC policies must include a robust CIP across the customer lifecycle from initial onboarding through portfolio management. KYC solutions encompass the financial institution’s customer identification program, customer due diligence and ongoing monitoring. What are the requirements for a CIP? Customer identification program requirements vary depending on the type of financial institution, the type of account opened, and other factors. However, the essential components of a CIP include verifying the customer's identity using government-issued identification, obtaining and verifying the customer's address, and checking the customer against a list of known criminals, terrorists, or suspicious individuals. These measures  help detect and prevent financial crimes. Why is a CIP important for businesses? CIP helps businesses mitigate risk by ensuring they have accurate and up-to-date information about their customers. This also helps financial institutions comply with laws and regulations that require them to monitor financial transactions for any suspicious activities. By having a robust CIP in place, businesses can establish trust and rapport with their customers. According to Experian’s 2023 U.S. Identity and Fraud Report, more than 85% of consumers expect businesses to respond to their identity and fraud concerns, and these expectations have risen over the past several years. Having an effective CIP in place is part of financial institutions showing their consumers that they have their best interests top of mind. Finding the right partner It’s important to find a partner you trust when working to establish processes and procedures for verifying customer identity, address, and other relevant information. Companies can also utilize specialized software that can help streamline the CIP process and ensure that it is being carried out accurately and consistently. Experian’s proprietary and partner data sources and flexible monitoring and segmentation tools allow you to resolve CIP discrepancies and fraud risk in a single step, all while keeping pace with emerging fraud threats with effective customer identification software. Putting consumers first is paramount. The security of their identity is priority one, but financial institutions must pay equal attention to their consumers’ preferences and experiences. It is not just enough to verify customer identities. Leading financial institutions will automate customer identification to reduce manual intervention and verify with a reasonable belief that the identity is valid and eligible to use the services you provide. Seamless experiences with the right amount of friction (I.e., step-up authentication) should also be pursued to preserve the quality of the customer experience. Putting it all together As cybersecurity threats are becoming more sophisticated, it is essential for financial institutions to protect their customerinformation and level up their fraud prevention solutions. Implementing a customer identification program is an essential component in achieving that objective. A robust CIP helps organizations detect, prevent, and deter fraudulent activities while ensuring compliance with regulatory requirements. While implementing a CIP can be complex, having a solid plan and establishing clear guidelines is the best way for companies to safeguard customer information and maintain their reputation. CIPs are an integral part of financial institutions security infrastructures and must be a business priority. By ensuring that they have accurate and up-to-date data on their customers, they can mitigate risk, establish trust, and comply with regulatory requirements. A sound CIP program can help financial institutions detect and prevent financial crimes and cyber threats while ensuring that legitimate business transactions are not disrupted, therefore safeguarding their customers' information and protecting their own reputation. Learn more

Data-driven machine learning model development is a critical strategy for financial institutions to stay ahead of their competition, and according to IDC, remains a strategic priority for technology buyers.  Improved operational efficiency, increased innovation, enhanced customer experiences and employee productivity are among the primary business objectives for organizations that choose to invest in artificial intelligence (AI) and machine learning (ML), according to IDC’s 2022 CEO survey.   While models have been around for some time, the volume of models and scale at which they are utilized has proliferated in recent years. Models are also now appearing in more regulated aspects of the business, which demand increased scrutiny and transparency.   Implementing an effective model development process is key to achieving business goals and complying with regulatory requirements. While ModelOps, the governance and life cycle management of a wide range of operationalized AI models, is becoming more popular, most organizations are still at relatively low levels of maturity. It's important for key stakeholders to implement best practices and accelerate the model development and deployment lifecycle.   Read the IDC Spotlight Challenges impeding machine learning model development  Model development involves many processes, from wrangling data, analysis, to building a model that is ready for deployment, that all need to be executed in a timely manner to ensure proper outcomes. However, it is challenging to manage all these processes in today’s complex environment.   Modeling challenges include:  Infrastructure: Necessary factors like storage and compute resources incur significant costs, which can keep organizations from evolving their machine learning capabilities.   Organizational: Implementing machine learning applications requires talent, like data scientists and data and machine learning engineers.  Operational: Piece meal approaches to ML tools and technologies can be cumbersome, especially on top of data being housed in different places across an organization, which can make pulling everything together challenging.  Opportunities for improvement are many While there are many places where individuals can focus on improving model development and deployment, there are a few key places where we see individuals experiencing some of the most time-consuming hang-ups.   Data wrangling and preparation   Respondents to IDC's 2022 AI StrategiesView Survey indicated that they spend nearly 22% of their time collecting and preparing data. Pinpointing the right data for the right purpose can be a big challenge. It is important for organizations to understand the entire data universe and effectively link external data sources with their own primary first party data. This way, stakeholders can have enough data that they trust to effectively train and build models.   Model building  While many tools have been developed in recent years to accelerate the actual building of models, the volume of models that often need to be built can be difficult given the many conflicting priorities for data teams within given institutions. Where possible, it is important for organizations to use templates or sophisticated platforms to ease the time to build a model and be able to repurpose elements that may already be working for other models within the business.   Improving Model Velocity Experian’s Ascend ML BuilderTM is an on-demand advanced model development environment optimized to support a specific project. Features include a dedicated environment, innovative compute optimization, pre-built code called ‘Accelerators’ that simply, guide, and speed data wrangling, common analyses and advanced modeling methods with the ability to add integrated deployment.  To learn more about Experian’s Ascend ML Builder, click here.   To read the full Technology Spotlight, download “Accelerating Model Velocity with a Flexible Machine Learning Model Development Environment for Financial Institutions” here.  Download spotlight *This article includes content created by an AI language model and is intended to provide general information. 

In today's fast-paced financial landscape, financial institutions must stay ahead of the curve when it comes to account opening and onboarding. Digital account opening, empowering a prospective client to securely and efficiently open a new account, is key to how banks, credit unions and other financial institutions grow their business and expand their portfolio. Regardless of the time, money and other resources a financial institution invests in marketing to the right target prospect and tailoring an attractive offer, it’s worthless if that prospective customer can’t complete the process due to a poor account opening experience. Unhappy customers vote with their feet. A recent Experian study found that of the more 2,000 consumers surveyed who’d opened a new account in the last six months, 37% took their business elsewhere due to a negative account opening experience.   The choice of a reliable partner can make all the difference to your account opening and onboarding experience. The right partner must provide your financial institution with access to the freshest credit data; advanced analytics, scores and models to empower you to say yes to the right customers that meet your lending criteria; and industry-leading decision engines that make the best decisions and enable you to provide a seamless customer experience.  Moreover, the right partner will also help you in maintaining high levels of security without compromising user experience, all while adhering to regulatory compliance.  Recently, Liminal, a leading advisory and market intelligence firm specializing in the digital identity, cybersecurity, and fintech markets, released its highly anticipated Link™ Index Report for Account Opening in Financial Services, which evaluates solution providers in the financial sector, in the areas of compliance and fraud prevention for account opening. The report recognized Experian as a market leader for compliance and fraud prevention capabilities and market execution. Experian’s identity verification and fraud prevention solutions, including CrossCore® and Precise ID®, received the highest score out of the 32 companies highlighted in the report. It found that Experian was recognized by 94% of buyers and 89% identified Experian as a market leader.   “We’re thrilled to be named the top market leader in compliance and fraud prevention capabilities and execution by Liminal’s Link Index Report,” said Kathleen Peters, Chief Innovation Officer for Experian’s Decision Analytics business in North America. “We’re continually innovating to deliver the most effective identity verification and fraud prevention solutions to our clients so they can grow their business, mitigate risk and provide a seamless customer experience.”  You can access the full report here. To learn more about Experian’s award-winning fraud solutions, visit our identity fraud hub.  Download Liminal Link Index Report

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.

The state of digital banking is a story of fragmentation and technology that's often outdated or poorly integrated. Customer journeys are often suboptimal, and multiple layers of technological solutions often translate to problems like poor data hygiene, lack of regulatory compliance and missed opportunities. In addition, the use of legacy software can make it challenging to integrate up-to-date methods such as AI analytics solutions. However, demand on both the front and back ends for better digital services and more-efficient processes is driving banks to take on digital transformations that will help them stay competitive in an evolving technological landscape. Customers expect a frictionless, personalized and highly functional digital experience. To match strength with digital-native competitors, banks and lenders must transform how their organizations do business. What is digital transformation, and what does it mean for banks and lenders? A comprehensive digital transformation strategy is more than just investing in new digital tools. It's about rebuilding the structure and infrastructure of your business so that online and digital services and processes form the core of your competencies and offerings. Digital transformation is an ongoing journey rather than an end goal. It's a continuous process that iterates as you steadily improve and streamline operations and integrate new and improved technologies. One of the key aspects of digital transformation in banking is better gathering and leveraging of data. Banks, especially larger ones with a longer business history, possess large quantities of data that may be siloed or poorly utilized. By improving how they collect, analyze and make use of data, banks and financial institutions can enhance their decision-making abilities and engage with consumers in a more authentic, personalized way. Perhaps most important, digital transformation is customer-centric. While upgrading, merging and integrating back-end technologies and data solutions is a key component of the process, it's all done with the customer experience top of mind. Centralizing, streamlining and modernizing digital operations help to create a seamless, secure and highly targeted customer journey. The core pillars of digital transformation Multiple core pillars are involved in undergoing a successful digital transformation. Each of these should be integrated into a comprehensive strategy that considers the transformation as an integrated process, rather than a series of individual projects. In fact, one common error banks make when upgrading their digital infrastructure and offerings is failing to coordinate digital initiatives. A true digital transformation is holistic, resulting in apps, infrastructure, digital systems and customer experience platforms that are all part of one coherent, consistent approach. Data: Data is at the heart of digital transformation. It's through maximizing and optimizing usable data that financial institutions can truly make an impact on their ability to reach and connect with target consumers. Using data the right way means prioritizing security and privacy while taking advantage of opportunities to improve consumer targeting and engagement and personalization of offers. Analytics: Data can't do its job if it's not interpreted in a way that makes sense for your business. Quality analytics software and comprehensive analysis are what turn a set of disparate data points into usable information that informs smart decision-making and improves KPIs. Automation: Machine learning is improving by leaps and bounds, and it's only going to get more useful for businesses looking to increase the efficiency of their sales, marketing and engagement efforts. AI solutions are no longer a fringe tool but are quickly becoming part of the mainstream and a key component of digital strategies. Customers: With the array of digital tools available today, it's easy to lose sight of the main purpose of your business — connecting with people. Customers today expect digital engagement experiences that feel personalized and real, which is why a consistent, appealing digital customer journey should be top of mind in any digital transformation strategy. How can banks benefit? New, digitally native fintech solutions abound in the contemporary landscape. Overall, they tend to be highly competent when it comes to making the most of state-of-the-art tools like artificial intelligence, mobile apps and blockchain. By combining their brand longevity with a well-executed digital transformation, traditional banks can capitalize on their established reputations by reaching consumers with compelling offerings that utilize and are based on best-in-class digital tools and data analysis. Digital transformation in banking can have numerous benefits. For one, operations will be more streamlined. For another, enhanced security will make customers feel more secure while minimizing losses from fraud. In addition, integrating top-of-the-line data and analysis will result in better overall decision-making. The ultimate goal? Boosting lead generation and conversion rates and improving customer onboarding while reducing churn, thereby maximizing the efficiency of budget spend across multiple departments, from marketing to customer service. Get started with Experian Implementing a digital transformation that truly improves your business can be a daunting task, but it's achievable with the right partner. Experian's connectable and configurable solutions and technology can help drive your digital transformation. With offerings like our cloud platform solutions, you'll be well-positioned to move forward and take advantage of up-to-date technologies to serve your customers better. Learn more about how you can benefit from the digital transformation in banking. 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