1. Identifying and supporting at-risk members: One of the best ways credit unions can maintain growth is to mitigate losses. While advanced credit-risk modeling tools help lenders assess risk and make solid lending decisions for new members, the same tools can help identify vulnerabilities for existing members. Using the analytical tools later in the member lifecycle, credit unions can identify financially at-risk members and take appropriate steps to minimize the impact of a late or missed payment and/or intervene before it happens. For example, a credit union can use a series of alerts that flag certain behaviors. Then actions can be taken to help members understand their options, such as implementing a payment plan.

Advanced analytics not only help prevent losses but can leave members feeling supported and valued. This builds trust and fosters long-term relationships, thus increasing the chances members will share positive experiences with others. Additionally, advanced analytical credit risk-modeling tools can help credit unions identify up- or cross-selling opportunities.

2. Addressing fraud throughout the customer lifecycle: Amid the digital transformation that's taken place over the past couple years, fraud continues to rapidly evolve and be a challenge for credit unions and their members. With that in mind, credit unions should be proactive and implement a multi-layered approach that leverages data, technology and advanced analytics to help verify identities and detect fraud across the member lifecycle. By putting the right processes and controls in place, credit unions can protect their current portfolio and mitigate losses in the future.

3. Reaching underserved consumers through expanded data and advanced insights: Mainstream credit models and legacy credit data are commonly used today. However, there are many consumers – most likely from underserved communities – who may be at a disadvantage. Our research with Oliver Wyman showed only 81% of the population can be scored using mainstream credit models, and 28% of Black and 26% of Hispanic consumers are unscoreable or invisible when legacy models are used. Moreover, there is an estimated 28 million "credit invisible" and 21 million "unscoreable" individuals currently excluded from the credit ecosystem.

Credit unions can leverage advanced analytics and machine-learning score models to create more opportunities for these consumers while gaining important insights about a consumer's financial position.

Combining machine learning and expanded data sets delivers better insights on a prospective borrower and a more accurate risk assessment. This allows for an estimated 65% of the credit-invisible population to be scored in addition to the entire conventionally unscoreable population.

Deep understanding of a consumer's financial situation allows credit unions to confidently make lending decisions and take on appropriate risk while helping more consumers get access to fair and affordable credit.

As credit unions navigate economic uncertainty, advanced analytics can empower better, faster and more-informed decisions. Members and potential members can reap the benefits of greater opportunities. Regardless of market conditions, credit unions that leverage sophisticated data and analytic tools can expand their lending universe and build long-lasting member relationships.

Greg Wright

Greg Wright is Chief Product Officer and EVP for Experian Consumer Information Services.