Today’s Credit Decisioning: Navigating the Current Complexities

The science of consumer credit decisioning is complex. First, there is the process of access and analyzing the data, and second, there is the applying of strategies and, ultimately, decisions, using that information. With the right tools in place to make sense of data and analytics, credit unions and building societies can test strategies quickly, deploy decisions confidently and deliver on members’ expectations.

Yet the COVID-19 pandemic has changed consumers’ needs and behaviors while upending how lenders assess credit risk and build their decision strategies. Income fluctuations and job losses have impacted how people spend and save, while payment assistance programs, a necessary bridge for many in a challenging year, have distorted the traditional metrics lenders use for credit decisioning.

Today, after more than a year of irregularity, financial transactions and therefore credit risk decisioning are beginning to return to a recognizable pattern. However, it is increasingly clear to me that the world of credit is recalibrating and that a new path has been forged because of changes in data brought on by the effects of the COVID-19 pandemic.

How can we navigate this new road? By learning more about our clients, being more elastic about our expectations of them in the short term and embracing data and the analytics it produces. We know far more about how consumers behave during a global crisis than we did before the pandemic. With that information in hand, the unprecedented shifts of the past year can become a long-term opportunity rather than a setback for lenders.

Below, I’d like to share three strategies that I believe will be helpful to navigate this new road.

Engage With Your Members

The COVID-19 pandemic has impacted everyone differently and we, as lenders, must react accordingly. One-size-fits-all lending approaches no longer apply (and maybe they never truly did). Spending patterns changed overnight and are still evolving. Now, as we begin to put the crisis behind us and make sense of the road before us, Experian’s research indicates two emergent paths.

In the first, the financial fallout of 2020 is still surging through many consumers’ lives. The pandemic’s negative economic effects are either still occurring or have very recently ended. Salaries have been trimmed and jobs have been lost. For businesses, the Paycheck Protection Program just closed in May 2021. For many, payment holidays are coming to an end. Experian’s recent Global Decisioning eBook, “Navigating a new era of credit risk decisioning,” showed that one in three consumers remain concerned about their finances and worried about their employment worldwide.

For others, such as those with countercyclical or essential businesses, they may have more cash than they did prior to the pandemic. Some households are no longer reducing their discretionary spending. Only 11% are cutting their spending compared to 19% a year ago. This second path clearly has very different lending needs than the first.

Consumers now range across a broad spectrum of credit needs. In fact, lending patterns are in flux. Lenders must prepare for delinquencies, and proactively meet members where they are by offering new, flexible credit products tailored to their needs. Meanwhile, they also need to support those that have been fortunate to thrive and are ready to spend.

Leverage Data and Analytics

To anticipate members’ needs and clearly assess your portfolio’s risk, invest the time to learn about and implement more advanced analytics. It’s important to note that predictive analytics in credit risk decisioning take past behavior and outcomes to build analytics that predict future behavior and outcomes. But the behavior and outcomes of the past year are an anomaly and not predictive, so the data generated during the pandemic has impacted credit risk models in unexpected ways. Machine-learning credit algorithms have evolved in line with the data they have received. Greater government assistance and longer payment holidays mean that the structure of information has been altered from the norm.

In businesses of all sizes, business confidence in current credit risk models declined between June 2020 and January 2021. For small businesses, those with $10-49 million in revenue, confidence in their analytics declined as much as 15%. For large businesses with revenue greater than $1 billion, the percentage decrease during the same period was 9%.

To counteract this decline, forward-thinking companies will need to rethink credit-decisioning models.  Now may be the time to use synthetic and alternative data to simulate behavior and outcomes, build models, test and refine them, and then deploy them into redesigned customer decisioning strategies.  Opt-in consumer data – such as rental payments, telephone bills, utility bills, etc. – is an example of an alternative data set. Synthetic data can and should be utilized to train machine-learning models on hypothetical scenarios, increasing the number and breadth of potential credit decisions it can make, in turn aligning to macroeconomic shifts in consumer behavior and needs that continue to evolve.

This approach, combined with meeting consumers where they are, can create fairer and more efficient credit experiences.

The Future of Credit Decisioning

Credit decisioning remains a complex mix science today – lenders simply need to recalibrate the mix to meet new sector and member expectations. The digital first-approach will accelerate, which will make the community-minded history of credit unions even more valuable. What’s technology without the heart?

Despite challenges, lenders right now have the perfect opportunity to rethink existing processes and rebuild even better. Credit decisioning now requires more member engagement, and improved data and risk models. For those who recognize these shifts, it’s a golden opportunity to shape the future of our industry.

Harry Singh, Experian’s SVP for Decisioning Products & Solutions.