Apply DA Tag

First-party fraud can be detected and prevented by using robust fraud risk management strategies and solutions.

With the rise of digital services, the telecom industry faces the need to mitigate fraud while streamlining the onboarding process.

Authorized push payment fraud is a growing threat. Learn how to detect and prevent it in our latest blog article. Read more!

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

Ghost student fraud is a serious and alarming issue in the educational sector. Learn how to spot it and safeguard your institution.

Fraudsters have evolved their techniques to capitalize on homeowners and lenders by shifting their focus from home purchases to HELOC fraud.

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

Learn what automated ID verification is and tips on implementing automation identity verification solutions into your business practice.

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 Fed launched FedNow, a new instant payment service. While it offers advantages, there's concerns that fraudsters may exploit it with fraud schemes.

Explore credit card marketing trends and strategies to help you identify, engage, and acquire the right customers.

"Grandma, it’s me, Mike.” Imagine hearing the voice of a loved one (or what sounds like it) informing you they were arrested and in need of bail money. Panicked, a desperate family member may follow instructions to withdraw a large sum of money to provide to a courier. Suspicious, they even make a video call to which they see a blurry image on the other end, but the same voice. When the fight or flight feeling settles, reality hits. Sadly, this is not the scenario of an upcoming Netflix movie. This is fraud – an example of a new grandparent scam/family emergency scam happening at scale across the U.S. While generative AI is driving efficiencies, personalization and improvements in multiple areas, it’s also a technology being adopted by fraudsters. Generative AI can be used to create highly personalized and convincing messages that are tailored to a specific victim. By analyzing publicly available social media profiles and other personal information, scammers can use generative AI to create fake accounts, emails, or phone calls that mimic the voice and mannerisms of a grandchild or family member in distress. The use of this technology can make it particularly difficult to distinguish between real and fake communication, leading to increased vulnerability and susceptibility to fraud. Furthermore, generative AI can also be used to create deepfake videos or audio recordings that show the supposed family member in distress or reinforce the scammer's story. These deepfakes can be incredibly realistic, making it even harder for victims to identify fraudulent activity. What is Generative AI? Generative artificial intelligence (GenAI) describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Generative AI has the potential to revolutionize many industries by creating new and innovative content, but it also presents a significant risk for financial institutions. Cyber attackers can use generative AI to produce sophisticated malware, phishing schemes, and other fraudulent activities that can cause data breaches, financial losses, and reputational damage. This poses a challenge for financial organizations, as human error remains one of the weakest links in cybersecurity. Fraudsters capitalizing on emotions such as fear, stress, desperation, or inattention can make it difficult to protect against malicious content generated by generative AI, which could be used as a tactic to defraud financial institutions. Four types of Generative AI used for Fraud: Fraud automation at scale Fraudulent activities often involve multiple steps which can be complex and time-consuming. However, GenAI may enable fraudsters to automate each of these steps, thereby establishing a comprehensive framework for fraudulent attacks. The modus operandi of GenAI involves the generation of scripts or code that facilitates the creation of programs capable of autonomously pilfering personal data and breaching accounts. Previously, the development of such codes and programs necessitated the expertise of seasoned programmers, with each stage of the process requiring separate and fragmented development. Nevertheless, with the advent of GenAI, any fraudster can now access an all-encompassing program without the need for specialized knowledge, amplifying the inherent danger it poses. It can be used to accelerate fraudsters techniques such as credential stuffing, card testing and brute force attacks. Text content generation In the past, one could often rely on spotting typos or errors as a means of detecting such fraudulent schemes. However, the emergence of GenAI has introduced a new challenge, as it generates impeccably written scripts that possess an uncanny authenticity, rendering the identification of deceit activities considerably more difficult. But now, GenAI can produce realistic text that sounds as if it were from a familiar person, organization, or business by simply feeding GenAI prompts or content to replicate. Furthermore, the utilization of innovative Language Learning Model (LLM) tools enables scammers to engage in text-based conversations with multiple victims, skillfully manipulating them into carrying out actions that ultimately serve the perpetrators' interests. Image and video manipulation In a matter of seconds, fraudsters, regardless of their level of expertise, are now capable of producing highly authentic videos or images powered by GenAI. This innovative technology leverages deep learning techniques, using vast amounts of collected datasets to train artificial intelligence models. Once these models are trained, they possess the ability to generate visuals that closely resemble the desired target. By seamlessly blending or superimposing these generated images onto specific frames, the original content can be replaced with manipulated visuals. Furthermore, the utilization of AI text-to-image generators, powered by artificial neural networks, allows fraudsters to input prompts in the form of words. These prompts are then processed by the system, resulting in the generation of corresponding images, further enhancing the deceptive capabilities at their disposal. Human voice generation The emergence of AI-generated voices that mimic real people has created new vulnerabilities in voice verification systems. Firms that rely heavily on these systems, such as investment firms, must take extra precautions to ensure the security of their clients' assets. Criminals can also use AI chatbots to build relationships with victims and exploit their emotions to convince them to invest money or share personal information. Pig butchering scams and romance scams are examples of these types of frauds where AI chatbots can be highly effective, as they are friendly, convincing, and can easily follow a script. In particular, synthetic identity fraud has become an increasingly common tactic among cybercriminals. By creating fake personas with plausible social profiles, hackers can avoid detection while conducting financial crimes. It is essential for organizations to remain vigilant and verify the identities of any new contacts or suppliers before engaging with them. Failure to do so could result in significant monetary loss and reputational damage. Leverage AI to fight bad actors In today's digital landscape, businesses face increased fraud risks from advanced chatbots and generative technology. To combat this, businesses must use the same weapons than criminals, and train AI-based tools to detect and prevent fraudulent activities. Fraud prediction: Generative AI can analyze historical data to predict future fraudulent activities. By analyzing patterns in data and identifying potential risk factors, generative AI can help fraud examiners anticipate and prevent fraudulent behavior. Machine learning algorithms can analyze patterns in data to identify suspicious behavior and flag it for further investigation. Fraud Investigation: In addition to preventing fraud, generative AI can assist fraud examiners in investigating suspicious activities by generating scenarios and identifying potential suspects. By analyzing email communications and social media activity, generative AI can uncover hidden connections between suspects and identify potential fraudsters. To confirm the authenticity of users, financial institutions should adopt sophisticated identity verification methods that include liveness detection algorithms and document-centric identity proofing, and predictive analytics models. These measures can help prevent bots from infiltrating their systems and spreading disinformation, while also protecting against scams and cyberattacks. In conclusion, financial institutions must stay vigilant and deploy new tools and technologies to protect against the evolving threat landscape. By adopting advanced identity verification solutions, organizations can safeguard themselves and their customers from potential risks. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

Learn what identity verification is and how online identity verification services and methods help organizations address fraud.

Money mule fraud is a type of financial scam in which criminals exploit individuals, known as money mules, to transfer stolen money or the proceeds of illegal activities. Money mule accounts are becoming increasingly difficult to distinguish from legitimate customers, especially as criminals find new ways to develop hard-to-detect synthetic identities. How money mule fraud typically works: Recruitment: Fraudsters seek out potential money mules through various means, such as online job ads, social media, or email/messaging apps. They will often pose as legitimate employers offering job opportunities promising compensation or claiming to represent charitable organizations. Deception: Once a potential money mule is identified, the fraudsters use persuasive tactics to gain their trust. They may provide seemingly legitimate explanations like claiming the money is for investment purposes, charity donations or for facilitating business transactions. Money Transfer: The mule is instructed to receive funds to their bank or other financial account. The funds are typically transferred from other compromised bank accounts obtained through phishing or hacking. The mule is then instructed to transfer the money to another account, sometimes located overseas. Layering: To mask the origin of funds and make them difficult to trace, fraudsters will employ layering techniques. They may ask the mule to split funds into smaller amounts, make multiple transfers to different accounts, or use various financial platforms such as money services or crypto. Compensation: The money mule is often promised a percentage of transferred funds as payment.  However, the promised monies are lower than the dollars transferred, or sometimes the mule receives no payment at all. Legal consequences: Regardless whether mules know they are supporting a criminal enterprise or are unaware, they can face criminal charges. In addition, their personal information could be compromised leading to identity theft and financial loss. How can banks get ahead of the money mule curve: Know your beneficiaries Monitor inbound paymentsEngage identity verification solutionsCreate a “Mule Persona” behavior profileBeware that fraudsters will coach the mule, therefore confirmation of payee is no longer a detection solution Educate your customers to be wary of job offers that seem too good to be true and remain vigilant of requests to receive and transfer money, particularly from unknown individuals and organizations. How financial institutions can mitigate money mule fraud risk When new accounts are opened, a financial institution usually doesn’t have enough information to establish patterns of behavior with newly registered users and devices the way they can with existing users. However, an anti-fraud system should catch a known behavior profile that has been previously identified as malicious. In this situation, the best practice is to compare the new account holder’s behavior against a representative pool of customers, which will analyze things like: Spending behavior compared to the averagePayee profileSequence of actionsNavigation data related to machine-like or bot behaviorAbnormal or risky locationsThe account owner's relations to other users The risk engine needs to be able to collect and score data across all digital channels to allow the financial institution to detect all possible relationships to users, IP addresses and devices that have proven fraud behavior. This includes information about the user, account, location, device, session and payee, among others. If the system notices any unusual changes in the account holder’s personal information, the decision engine will flag it for review. It can then be actively monitored and investigated, if necessary. The benefits of machine learning This is a type of artificial intelligence (AI) that can analyze vast amounts of disparate data across digital channels in real time. Anti-fraud systems based on AI analytics and predictive analytics models have the ability to aggregate and analyze data on multiple levels. This allows a financial institution to instantly detect all possible relationships across users, devices, transactions and channels to more accurately identify fraudulent activity. When suspicious behavior is flagged via a high risk score, the risk engine can then drive a dynamic workflow change to step up security or drive a manual review process. It can then be actively monitored by the fraud prevention team and escalated for investigation. How Experian can help Experian’s fraud prevention solutions incorporate technology, identity-authentication tools and the combination of machine learning analytics with Experian’s proprietary and partner data to return optimal decisions to protect your customers and your business. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

Learn how AI analytics helps lenders improve their underwriting.