Machine Learning Underwriting Models & Cash-Flow Data

Overview

FinRegLab is launching a ground-breaking comparison of the financial inclusion benefits of using machine learning underwriting models with and without cash-flow data to increase responsible access to credit for consumers who may otherwise find it difficult to obtain safe and affordable loans.

Large banks and fintechs are increasingly working to improve their underwriting models by using machine learning techniques, alternative data sources such as bank transaction account records, or a combination of the two innovations.  However, no publicly available research directly compares each innovation’s separate and combined effects on predictiveness, inclusion, and fairness.

While stakeholders are interested in the potential of new data sources and analytical techniques to increase credit access, these more complex analytics also raise concerns about reliability, bias, and the ability to understand and manage more sophisticated models. The new project could help to inform the entire lending ecosystem and be particularly helpful to smaller institutions, advocates, and policymakers in prioritizing resources and new initiatives.

The first phase of the project will involve building two groups of credit underwriting models using logistic regression techniques and a form of machine learning called XGBoost. The models will be trained on credit bureau data, consumer-permissioned bank account information, or a combination of the two sources.

Later phases of the project will include additional workstreams focusing on alternative approaches to managing concerns about model explainability and fairness, such as building additional models using alternative techniques and comparing them to the original set to evaluate potential tradeoffs between model performance, simplicity, and fairness metrics.

The research aims to inform policymakers, lenders, other industry actors, advocates, and researchers in prioritizing initiatives to advance the responsible, fair, and inclusive use of new data sources and machine learning in credit underwriting.