How Credit Unions Can Reinvent Risk Management Through AI

Credit unions helped Americans through multiple crises over the last several years. From processing PPP loans for small businesses to facilitating loan forgiveness, credit unions took steps to create deeper connections with consumers during the pandemic.

Credit unions should build on this momentum by accelerating their digital transformations toward improving the member experience. Right now, consumers are at a crossroads. Many credit union members’ lives have been upended – their finances have changed, they are shopping differently and they have new and sometimes more irregular incomes. At the same time, they’ve become more wary of fraud and scams that were rife during the pandemic. Consumers need and expect more from the institutions they bank with, and it’s time for credit unions to step up – both by improving the member experience and protecting them against fraud risk.

In recent years, many credit union leaders have balked at artificial intelligence as a possibility to improve business – it’s historically been seen as too complex and pricy to adopt and maintain. But AI applications designed specifically to work within the credit lifecycle have made strides within the past five years and deserve a second look.

How AI Benefits Everyone in the Credit Lifecycle

Implementing AI in credit decisioning, portfolio maintenance and collections management sounds like an exercise that only benefits financial institutions. But because of credit unions’ non-profit status and important place in the community, using AI to better assess borrower risks and predict member financial health can end up benefiting members as individuals and as a group. When implemented properly into the following areas, modern AI applications bring benefits to each part of the consumer credit lifecycle.

• Credit applications and approvals: Credit unions can get a much sharper upfront picture of new members through AI. Tools that go beyond credit scores and other traditional metrics allow credit unions to estimate specific risks at the time of application. These tools include insights into real-time member transactions, active accounts and payment histories beyond what was typically available. This not only helps reduce the chances of defaults and other negative outcomes for credit unions, but benefits members as well. With additional data and context about their members, credit unions can offer tailored credit offers that result in more personalized experiences.
• Portfolio management and credit delinquency: Financial institutions and their customers/members have something big in common – neither likes a financial surprise. AI can act as a forecasting tool for consumer delinquencies, using transaction and other data to provide early warning signals that a payment may be missed or deficient. This can help credit unions reach out sooner to offer assistance and options for the member to avoid collections.
• Collections optimization: Despite smarter credit decisions and better portfolio management, credit unions inevitably deal with member collections. AI can help make the process less overwhelming. Because of its ability to work well with real-time data, AI can help guide collections strategies — confirming member addresses and contact information, and identifying the best ways to reach out. For credit union members, better collections mean a more solvent union overall.

Why CUs Are Well-Positioned to Harness AI in the Credit Risk Lifecycle

While some credit unions are wary of AI, a look at the competitive landscape should give them a greater sense of urgency. Most financial institutions will be considering implementing some form of AI in the next few years and about a third of them will use it for credit risk monitoring, according to a LendIt and Brighterion study. Credit union leaders shouldn’t overestimate the resources or effort needed to implement AI. There are several reasons why credit unions are well-positioned to begin adopting AI sooner than they may think:

• Barriers to adoption have dropped. Taking the first step into AI isn’t nearly as difficult as it once was. For credit union professionals who have heard AI batted around as a buzzword for nearly a decade, the concept may have lost some meaning. But several important things have changed. First, companies offering AI solutions are now working with an unprecedented range of data and intelligence assets – all of which are at the disposal of their partners, allowing them a far deeper financial picture of consumers than they once had. And thanks to the cloud and easier ways of securely connecting to consumer data, the cost of deploying an AI-driven application has dropped significantly. With 80% of companies using AI already seeing value from it, according to McKinsey & Company research, the investment has never made more financial sense.
• Credit unions are more agile than competitors. Credit unions often underestimate their ability to compete on a technological level with big banks. With the right partners, credit unions can create powerful engines of research and development that can rival the biggest financial institutions’ efforts. As not-for-profit institutions under ownership by their members, credit unions have the agility to move fast and seek fewer approvals to gather data. So when an experiment requires data from disparate parts of the institution, it’s much easier to wrangle.

Credit unions have an unprecedented opportunity to better serve individual members and their communities by using AI to monitor credit risk. With strong leadership, credit unions can set the standard for what more personalized service and better protected consumers look like.

Amyn Dhala is Chief Product Officer at Brighterion, a San Francisco-based AI company owned by Mastercard, and Vice President at Mastercard.