I recently participated in a great panel discussion at Origence’s Lending Tech Live ’22 conference that focused on reimagining lending and credit in a digital-first world. The session highlighted the keys to credit unions effectively transforming the borrower experience by integrating credit bureau data with their digital origination platform.
The underlying question is how can analytics advance lenders’ ability to deliver a frictionless experience from the moment an application is started?
As economic uncertainty persists, pressure continues to mount on credit unions to adapt faster and decision smarter. A global financial institution likely has more than enough data to create accurate, powerful custom models. However, some regional and local credit unions may feel they simply don’t have enough customer data points to power a model. U.S. businesses ranked implementing new analytics methods and models as a top priority in 2022.
Many financial institutions understand this priority but still face the ongoing challenge of how to fully integrate analytical solutions into business decision-making and processes. Without strong analytics resources to deploy, monitor, and retrain models quickly and seamlessly — and to keep up with changing consumer behaviors — businesses are left directionless.
Now more than ever, it’s important to build models that accurately measure risk and additionally implement a strategy to make optimal decisions. That’s because models are only as effective as their historical data. If there isn’t a data sample over a long enough time frame, the risk of creating blind spots that can leave businesses on the hook for unexpected losses can be high. Not to mention that rapid changes in consumer needs and desires means there’s less confidence in consumer risk management analytics models that are based on yesterday’s customer understanding.
So how do we as analytical experts best position our institutions for success? We see successful credit unions developing a strategy, modernizing them with a compute-intensive technique called mathematical optimization, and deploying them at scale.
Strategies are adaptable, which helps lenders adjust to changes in goals, vision, or shifts in the marketplace in a bid to attract the ideal customer. In a world that changes by the day, the ability to adjust risk tolerance on the fly is crucial. Strategies also help you work toward defined portfolio-level goals by determining the best combination of decisions to maximize profit while managing risk.
Strategies can also be developed and deployed in a relatively rapid manner, and then adapted on an ongoing basis to reflect the realities on the ground. While a model can provide a score, it can’t tell you what to do with it. By focusing on a decision management strategy, you can leverage other information and attributes about different consumer segments to inform actions and decisions.
The total value of a decisioning system goes beyond what traditional credit attributes can measure. Leveraging expanded FCRA data enables financial institutions to measure the true credit worthiness of even those invisible to traditional credit data and scores. Strategies also uncover certain segments of the population where the returns from that segment more than offset the risks, so lenders can open themselves to new to credit customers while maximizing profit.
Models and strategies are both key players in the way we do business. By focusing on how they work together, lenders can be empowered to effectively leverage analytics today to act while creating a steppingstone for more sophisticated model-based analytics tomorrow.
