Data Can Calm Your Election Day Jitters

The year 2020 has been filled with words like uncertainty, instability, anxiety and unprecedented. Tuesday night’s election is no different. The outcome of the presidential and congressional elections will determine the future of banking policies and impact the economy, not just on a national level, but in ways that will affect some markets more than others.

From CRA reform to CFPB leadership, there are many issues at stake for financial institutions. The theme of the Trump presidency was deregulation. We can expect status quo if he is reelected and the Republicans remain in control of the Senate. Meanwhile, a change of hands over to Biden would lead us down a path of reforms and policy adjustments, especially if the Senate goes to the Democrats. But remember, no matter who wins, change takes time. The checks and balances built into our government – not to mention the political dysfunction – will temper lofty campaign trail promises.

Contributing to the uncertainty is the pandemic as it continues to rage across the United States with tens of thousands of new cases reported each day, forcing the economic crisis to remain front and center.

As we approach an era of uncertainty and tune in to Tuesday evening’s elections, I have one point of advice for credit unions: Don’t hold back, and work with the facts.

Advanced, predictive insights happen when science and technology intersect. With data science you have useful methods to leverage otherwise hidden facts in a way that will strengthen your business strategies. Over the past few months, credit unions have shown a growing interest in leveraging data science in two key areas: Delinquency and portfolio management.

Employing data science and advanced analytics down to the member level provides not only statistical analysis, but gives you insights into unknown unknowns – risks you didn’t know you didn’t know. Gaining access to insights on a higher level will change the mindset and strategy of your organization, putting you on the path to a higher level of performance.

This shift to an enterprise-wide advanced analytics strategy creates opportunities to make smarter decisions beyond risk management and recovery. From gaining insight into exactly how some economic factors would affect your market to engagement and growth, the use cases are endless.

Reimagine Delinquency Management

Yes, data science can be applied to many areas of your credit union, but today you’re probably most concerned about how today’s election results may affect tomorrow’s delinquent recoveries. Collections often falls to the bottom of the list at budget time, but with hopes of a V-shaped recovery fading, many credit unions are refocusing their strategic energy on this area of the business.

Everything we know about collections is being challenged and reinvented. Credit unions are bracing for a drastic rise in delinquencies and are searching for ways to minimize losses. Meanwhile, advances in technology like AI, machine learning, natural language processing and all applications of data science afford financial institutions the opportunity to harness their power in meaningful ways that will help heal post-pandemic balance sheets. Applying the technology budget to data-driven collections business strategies also allows credit unions to stick to their digital transformation plans while also continuing to provide personalized, person-to-person collections service.

What does a data-driven collections strategy look like? Consider the traditional approach to allocating delinquencies within queues. The attributes may include days past due, product type, balance and FICO score. In this traditional approach, delinquent members with vastly different risk profiles, employment status and propensity to pay are being allocated and treated the same. This one-dimensional method will not promote efficiency gains or dramatically increase recoveries.

Now imagine an allocation of delinquencies that is driven by data science and includes the following: Propensity to Pay score, member risk score, sentiment, etc. While traditional attributes cannot be ignored, they can be enhanced. These additional insights derived from data science, like sentiment, keywords and other alternative data, allow you to prioritize riskier members based on more insightful and robust profiles you are creating for them.

With data science you now have a layer of predictability that will allow you to make wiser decisions in resource allocation, account placement and treatment strategies.

Pinpoint Portfolio Risk and Grow Income Opportunities

There are many use cases where predictability and data science can be applied, not just within collections.

For example, account management strategies can greatly benefit from insightful data that will promote relationship building and help offset potential risk to the credit union. Think about a member who holds no delinquency with the credit union, but whose credit score has migrated slightly down and has additional variable risk attributes that tag them as potentially troublesome. Incorporating predictability into your technology approach allows you to become adaptive and proactively target the right members in a pre-delinquency manner.

On the other hand, you may have a member who is doing quite well with a growing credit score, other positive credit attributes and a positive sentiment. This member, and others like them, may be a great resource for income growth and would benefit from a personal touch and promotional nudge. Incorporating predictable, advanced data science techniques will not only support portfolio loss prevention, it will help you retain your profitable members.

Thanks to the pandemic, we’ve all been forced to make technology leaps several years into the future in mere months. Regardless of how digitized your credit union was before 2020, chances are you now have an expanded mindset when it comes to the use of advanced analytics and data science.

So, as you await the election results and anticipate making decisions that will affect your credit union’s future, remember that data has a way of cutting through the chatter and bringing clarity to the unknown.

When the results of today’s elections have been determined, which data points should credit union leaders use to make future decisions? We’ll provide post-election advice in the Nov. 25 print issue of CU Times that financial institutions should study and include in their decision making process as they look to 2021 and beyond.

Abby Progin is vice president – product management at AKUVO, a technology CUSO based in Malvern, Penn.