NEW REPORT: The Banks’ How-To Guide To Using AI To Manage Credit Risk

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Managing credit risk used to be a reactive process.

Bank customers would fall behind on their payments, and their banks might react by imposing fees or having a case manager work with them to bring their accounts back up to speed. In severe cases, banks might have to take the drastic measure of closing accounts altogether.

This was not only costly for customers, but also financially dubious for their banks. Waiting until account holders fall behind to take action not only meant that customers’ credit scores would take a hit before their banks were alerted to a problem, but also that banks would lose the revenue from the scheduled payment.

Taking this retroactive approach to credit risk management was never efficient, but it has become even less feasible amid the pandemic. Consumers are more susceptible than ever to falling short on their monthly bills, leaving banks searching for more proactive ways to mitigate the risk of defaults.

There are countless technologies banks are employing to help them in this task, but none are as effective as artificial intelligence (AI). Banks that use AI-driven predictive models are able to detect the risk of delinquency as many as 12 months before a customer ever misses a payment, providing banks and their customers the breathing room they need to take action.

The practical applications for AI extend far beyond credit risk assessment and detection, however. The predictive modeling techniques that make AI an invaluable asset to any consumer credit strategy can also be applied to nearly every aspect of the consumer banking experience. The only question is how banks can ensure that they are employing the best strategies to apply them.

In How To Put AI In Your 2021 Business Plan Playbook, PYMNTS and Brighterion collaborate to provide a blueprint of how financial institutions (FIs) can incorporate AI into their consumer credit strategies.

The Playbook outlines six key operations that can be enhanced by applying AI systems, ranging from fraud detection to marketing optimization and even personalized chatbots. PYMNTS research shows that 55 percent of FIs that have implemented AI as a tool to fight fraud have seen a reduction in the number of false positives, for example, while others that have adopted AI say their customers are now more broadly satisfied with their services.

The trouble is that the skill set needed to be a bona fide AI expert is notoriously rare, with many banks lacking the resources and expertise needed to make informed decisions about the AI systems they hope to adopt. Many are therefore turning to third-party providers that specialize in AI technology and expertise to benefit from a wealth of industry knowledge, without the costs associated with building their own AI teams from scratch.

Reaching out to AI firms for assistance is only the beginning of a complex set of procedures that banks must follow to ensure they are getting the most out of their AI strategies, however. How To Put AI In Your 2021 Business Plan Playbook details all the ways that banks can ensure they are getting the maximum return on investment from their AI innovations and more.

To learn more about how banks can build an effective AI adoption strategy, download the playbook.

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