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AI Case Study

FICO reduces bank's losses on delinquent customers by up to 25% with machine learning that predicts consumer credit risk

FICO reduces bank's losses on delinquent customers by up to 25% with machine learning that predicts consumer credit risk. They used random forest and support vector machine learning algorithms that analysed debt-to-income levels combined with banking transactions.


Professional Services

Consultancy And Business Services

Project Overview

"We propose a cardinal measure of consumer credit risk that combines traditional credit factors such as debt-to-income ratios with consumer banking transactions, which greatly enhances the predictive power of our model. Using a proprietary dataset from a major commercial bank...from January 2005 to April 2009, we show that conditioning on certain changes in a consumer’s bank-account activity can lead to considerably more accurate forecasts of credit-card delinquencies in the future. For example, in our sample, the unconditional probability of customers falling 90-days-or-more delinquent on their payments over any given 6-month period is 5.3%, but customers experiencing a recent decline in income—as measured by sharp drops in direct deposits—
have a 10.8% probability of 90-days-or-more delinquency over the
subsequent 6 months."

Reported Results

"Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses."


"...techniques include radial basis functions, tree-based classifiers, and support-vector machines, and are ideally suited for consumer credit-risk analytics because of the large sample sizes and the complexity of the possible relationships among consumer transactions and characteristics."





"Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer."



"...we use a unique dataset consisting of transaction level, credit bureau, and account-balance data for individual consumers. This data is obtained for a subset of the Bank’s customer base for the period from January 2005 to April 2009. Integrating transaction, credit bureau, and account-balance data allows us to compute and update measures of consumer credit risk much more frequently than the slower-moving credit-scoring models currently being employed in the industry and by regulators."

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