Credit & Risk

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

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

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

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More than half of U.S. businesses say they discuss fraud management often, making fraud detection in banking top-of-mind. Banking fraud prevention can seem daunting, but with the proper tools, banks, credit unions, fintechs, and other financial institutions can frustrate and root out fraudsters while maintaining a positive experience for good customers. What is banking fraud? Banking fraud is a type of financial crime that uses illegal means to obtain money, assets, or other property owned or held by a bank, other financial institution, or customers of the bank. This type of fraud can be difficult to detect when misclassified as credit risk or written off as a loss rather than investigated and prevented in the future. Fraud that impacts financial institutions consists of small-scale one-off events or larger efforts perpetrated by fraud rings. Not long ago, many of the techniques utilized by fraudsters required in-person or phone-based activities. Now, many of these activities are online, making it easier for fraudsters to disguise their intent and perpetrate multiple attacks at once or in sequence. Banking fraud can include: Identity theft: When a bad actor steals a consumer’s personal information and uses it to take money, open credit accounts, make purchases, and more. Check fraud: This type of fraud occurs when a fraudster writes a bad check, forges information, or steals and alters someone else’s check. Credit card fraud: A form of identity theft where a bad actor makes purchases or gets a cash advance in the name of an unsuspecting consumer. The fraudster may takeover an existing account by gaining access to account numbers online, steal a physical card, or open a new account in someone else’s name.  Phishing: These malicious efforts allow scammers to steal personal and account information through use of email, or in the case of smishing, through text messages. The fraudster often sends a link to the consumer that looks legitimate but is designed to steal login information, personally identifiable information, and more. Direct deposit account fraud: Also known as DDA fraud, criminals monetize stolen information to open new accounts and divert funds from payroll, assistance programs, and more. Unfortunately, this type of fraud doesn’t just lead to lost funds – it also exposes consumer data, impacts banks’ reputations, and has larger implications for the financial system. Today, top concerns for banks include generative AI (GenAI) fraud, peer-to-peer (P2P) payment scams, identity theft and transaction fraud. Without the proper detection and prevention techniques, it’s difficult for banks to keep fraudsters perpetrating these schemes out. What is banking fraud prevention? Detecting and preventing banking fraud consists of a set of techniques and tasks that help protect customers, assets and systems from those with malicious intent. Risk management solutions for banks identify fraudulent access attempts, suspicious transfer requests, signs of false identities, and more. The financial industry is constantly evolving, and so are fraudsters. As a result, it’s important for organizations to stay ahead of the curve by investing in new fraud prevention technologies. Depending on the size and sophistication of your institution, the tools and techniques that comprise your banking fraud prevention solutions may look different. However, every strategy should include multiple layers of friction designed to trip up fraudsters enough to abandon their efforts, and include flags for suspicious activity and other indicators that a user or transaction requires further scrutiny.   Some of the emerging trends in banking fraud prevention include: Use of artificial intelligence (AI) and machine learning (ML). While these technologies aren’t new, they are finding footing across industries as they can be used to identify patterns consistent with fraudulent activity – some of which are difficult or time-consuming to detect with traditional methods. Behavioral analytics and biometrics. By noting standard customer behaviors — e.g., which devices they use and when — and how they use those devices — looking for markers of human behavior vs. bot or fraud ring activity — organizations can flag riskier users for additional authentication and verification. Leveraging additional data sources. By looking beyond standard credit reports when opening credit accounts, organizations can better detect signs of identity theft, synthetic identities, and even potential first-party fraud.     With real-time fraud detection tools in place, financial institutions can more easily identify good consumers and allow them to complete their requests while applying the right amount and type of friction to detect and prevent fraud.   How to prevent and detect banking fraud In order to be successful in the fight against fraud and keep yourself and your customers safe, financial institutions of all sizes and types must: Balance risk mitigation with the customer experience Ensure seamless interactions across platforms for known consumers who present little to no risk Leverage proper identity resolution and verification tools Recognize good consumers and apply the proper fraud mitigation techniques to riskier scenarios With Experian’s interconnected approach to fraud detection in banking, incorporating data, analytics, fraud risk scores, device intelligence, and more, you can track and assess various activities and determine where additional authentication, friction, or human intervention is required. 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