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How Credit Unions Can Lower their Delinquency and Charge-Off Rates through Predictive Analytics

The U.S. economy may have started 2019 strong with a higher-than-expected GDP growth, but the nation’s mounting debt is causing more and more problems for people trying to make ends meet. According to the Federal Reserve Bank of New York’s recent quarterly report, the average American’s debt is on the rise, with the total household debt equating to $13.54 trillion – amassing 21% more debt than the national debt during the 2008 recession!

Along with an alarming increase in debt is a rise in delinquencies, especially within the auto loan segment, which comprises of a record 7 million Americans being 90 days or more behind on their payments.  This is, of course, a problem for Credit Unions and other financial institutions, but it is also a major problem for their members.

However, there is a solution that could help both sides avoid the grueling process of collections. Predictive analytics can identify members that are likely to default and informs Credit Unions about their members in need.  Credit Unions can then take action by reaching out to members and proactively offer individualized payment plans, while also providing education to their members on how to be financially responsible and successful.  By leveraging these analytics, Credit Unions will not only lower their delinquency and charge-off rates, but also identify and notify at-risk members to help protect their financial health.


Damage of Delinquency on Your Members’ Credit

Delinquency can have long-term consequences for your members.  Every late or missed payment causes their creditworthiness to deteriorate, which could impact their financial health for decades.  Members with lower credit scores face higher interest rates and risk losing out on opportunities to qualify for a reasonable home or auto loan.

This particularly impacts younger members, who often have lower credit scores than their older counterparts: A recent credit score study found that 41% of people between the ages of 30 and 39 have a credit score below 621, compared to 34% at the next age group (40 – 49).

Younger members are also at the highest risk for high credit card debt and are the most likely to be seeking loans for homes or cars within the next few years.  It’s not hard to see how quickly the situation can spiral for members unable to get their payments under control.


Strengthening Member Relationships and Financial Health with Predictive Analytics

Arvato’s Member Default Alerts utilize a predictive model that monitors members and detect which members are likely to default.  If detected, early warning alerts prompt Credit Unions to take preventative actions, like contacting their members about an upcoming payment.

Credit Union Member Lifecycle

These notifications better protect your at-risk members and their future.  With continuous monitoring through predictive analytics, Credit Unions can proactively work to avoid issues before they even occur. Younger members who turn to financial organizations offering digital services will appreciate the preemptive support, further strengthening their relationship with their Credit Union.


Double the Benefits: Protect Your Members, Lower Delinquencies with Predictive Analytics

Credit Union Delinquent MemberCredit Unions can drastically reduce their risk of charge-offs and lower member delinquencies with analytics.  Utilizing predictive models help identify at-risk members and provide the tools needed to help members avoid late payments.  For members who are already delinquent, leverage our Intelligent Collection Worklists to take the guesswork out of prioritizing who to contact and how.

With these tools, Credit Unions will strengthen member relationships and protect member financial health, while lowering delinquencies and charge-offs at the same time!  Bring a more personalized experience to your members by offering a benefit that identifies risk and helps mitigate it through early notification.

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