All posts by Natalie Daukas
Sometimes life throws you a curve ball. The unexpected medical bill. The catastrophic car repair. The busted home appliance. It happens, and the killer is that consumers don’t always have the savings or resources to cover an additional cost. They must make a choice. Which bills do they pay? Which bills go to the pile? Suddenly, a consumer’s steady payment behavior changes, and in some cases they lose control of their ability to fulfill their obligations altogether. These shifts in payment patterns aren’t always reflected in consumer credit scores. At a single point in time, consumers may look identical. However, when analyzing their past payment behaviors, differences emerge. With these insights, lenders can now determine the appropriate risk or marketing decisions. In the example below, we see that based on the trade-level data, Consumer A and Consumer B have the same credit score and balance. But once we see their payment pattern within their trended data, we can clearly see Consumer A is paying well over the minimum payments due and has a demonstrated ability to pay. A closer look at Consumer B, on the other hand, reveals that the payment amount as compared to the minimum payment amount is decreasing over time. In fact, over the last three months only the minimum payment has been made. So while Consumer B may be well within the portfolio risk tolerance, they are trending down. This could indicate payment stress. With this knowledge, the lender could decide to hold off on offering Consumer B any new products until an improvement is seen in their payment pattern. Alternatively, Consumer A may be ripe for a new product offering. In another example, three consumers may appear identical when looking at their credit score and average monthly balance. But when you look at the trend of their historical bankcard balances as compared to their payments, you start to see very different behaviors. Consumer A is carrying their balances and only making the minimum payments. Consumer B is a hybrid of revolving and transacting, and Consumer C is paying off their balances each month. When we look at the total annual payments and their average percent of balance paid, we can see the biggest differences emerge. Having this deeper level of insight can assist lenders with determining which consumer is the best prospect for particular offerings. Consumer A would likely be most interested in a low- interest rate card, whereas Consumer C may be more interested in a rewards card. The combination of the credit score and trended data provides significant insight into predicting consumer credit behavior, ultimately leading to more profitable lending decisions across the customer lifecycle: Response – match the right offer with the right prospect to maximize response rates and improve campaign performance Risk – understand direction and velocity of payment performance to adequately manage risk exposure Retention – anticipate consumer preferences to build long-term loyalty All financial institutions can benefit from the value of trended data, whether you are a financial institution with significant analytical capabilities looking to develop custom models from the trended data or looking for proven pre-built solutions for immediate implementation.
Trend: a general direction in which something is developing or changing. Last Update: January 2019 As a lender, it’s important to understand a consumer’s credit behavior and whether it is improving or deteriorating over time. Sure, you can pull a credit score at any moment, but it is merely a snapshot. Knowing a consumer’s credit information at a single point in time only tells part of the story. Two consumers can have the same credit score, but one consumer’s score could be moving up while another’s could be moving down. In order to understand the whole story, lenders need the ability to leverage trended data to assess a consumer’s credit behavior over time. Experian’s Trended Data is comprised of five fields of historical payment information over a 24-month period. It includes: Balance Amount Original Loan / Limit Amount Scheduled Payment Amount Actual Payment Amount Last Payment Date By analyzing historical payment information, lenders can determine if a consumer is consistently paying more than the minimum payment, has a demonstrated ability to pay and shows no signs of payment stress. It can conversely identify if a consumer is making only minimum payments and has increasing payment stress. Knowing how a consumer uses credit, or pays back debt over time, can help lenders offer the right products and terms to increase response rates, determine up-sell and cross-sell opportunities, prevent attrition, identify profitable customers, avoid consumers with payment stress and limit loss exposure. Using a consumer’s historical payment information provides a more accurate assessment of future behavior, which in turn helps effectively manage changes in risk, predicts in-the-market timing or balance transfer activity, and provides additional insight for other lending strategies. There is a catch though. In order for lenders to extract the benefits of trended data, they need to be able to analyze an enormous amount of data. Five fields of data across 24 months on every trade is huge and can be difficult for lenders with limited analytical resources to manage. For example, a single consumer with 10 trades on file would have upwards of 1,200 data points to analyze. Multiply that by a file of 100,000 consumers and you are now dealing with over 120,000,000 data points. Additionally, if lenders utilize the trended data in their underwriting processing and intend to use it to decline consumers, they will need to create their own adverse action reason codes to communicate to the consumer. Not all lenders are equipped to take on this level of effort. Still, there are solutions to assist lenders with managing and unlocking the power of trended data. Experian’s pre-calculated solutions utilizing trended data allow even the smallest lenders to leverage the most cutting-edge solutions in near plug-and-play environments to quickly and effectively action on the benefits of trended data, minus the hassles of analyzing it. Trended data, and the solutions built from it, allow lenders to effectively predict where a consumer is going based on where they’ve been. And really, that can make all the difference when it comes to smart lending decisions. Get Started Today
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