Fair Lending: Using AI to democratize compliance - CUInsight

In its most recent advisory, the CFPB addressed a critical question – "When creditors make credit decisions based on complex algorithms that prevent creditors from accurately identifying the specific reasons for denying credit or taking other adverse actions, do these creditors need to comply with the Equal Credit Opportunity Act's requirement to provide a statement of specific reasons to applicants against whom adverse action is taken? The answer is an obvious'Yes'. With the CFPB's circular reminding everyone of adverse action notice requirements under the ECOA Act, some credit unions find themselves in a quandary when it comes to explaining their credit decisions, which is perceived to be difficult when they use state of the art decisioning algorithms. However, modern AI solutions have moved beyond mere aspects of explainability to enable fair lending, and have gone the extra mile to remove inherent biases that may arise in data based models. Nonetheless, it is necessary to understand the CFPB's guidance and how AI can effectively be a solution itself. The use of algorithms in making lending decisions is not something novel or new. Credit risk assessment naturally requires getting your arms around as much relevant data as you can. A mix of models and algorithms have been the backbone of credit decisions for around 4 decades now, with credit analysts using financial statements, credit histories, and other data sources to estimate credit risk, set credit limits and recommend payment plans. With time, the datasets in question have become so voluminous that lenders had to move from manual methodologies to computational models for analysis of data using analytics. Recent advancements in computational methods have introduced the "AI" element in lending processes to make credit risk assessments much more accurate. Artificial Intelligence and Machine Learning models leverage a diverse set of alternate data sources beyond bureau, and use historical training data to determine non-linear correlations between data points, and provide advanced predictive signals on member behavior and lending outcomes. The unique proposition here is the ability of AI/ML models to analyze voluminous quantities of data, detect hitherto unknown correlations, and keep self-learning and adapting the models with little or no manual interventions. AI enabled technologies have helped put the spotlight on the increasingly visible disparities in existing lending processes. A 2019 paper by Robert Bartlett & Co. helps quantify this disparity: "Black and Latino applicants receive higher rejection rates of 61% compared to 48% for other races.