Using Unique Data in Portfolio Management Provides Improvements, Returns

To stay competitive and within budget, many auto financing companies are evaluating and determining which emerging technologies and other resources to tap into and which to pass on or postpone.

Fortunately, not all innovations include costly modern technology systems or trends. And not all business challenges require big overhauls in spending and processes.

Sometimes the most tried-and-true options—the obvious fixes—yield the most beneficial outcomes.

These can include staffing or policy changes, new equipment and new software, to name a few. In addition, adding unique data to traditional sources can offer big returns in the areas of underwriting, servicing and collections. By leveraging proprietary, alternative credit data—in real-time—financing companies better know how to interact with the consumers they serve, thereby growing and protecting their company’s portfolio.

Such unique data sheds light on underbanked consumers, a real bonus for auto financing companies who are dissatisfied with a lack of visibility into the rapidly changing alternative credit market—an estimated 20 percent of all households, or 51 million adults, according to the FDIC. This proactive tactic provides insights into the underbanked market that the Big 3 bureaus don’t have. By pairing unique alternative credit data with traditional data, auto financing companies begin to get the complete picture of their customers and prospects. And consumers begin to receive the credit they deserve.

Case study: Uncovering life events increases net returns

A case in point: Let’s look at a real-world example from an active non-prime automotive finance company. The company leveraged alternative credit data to efficiently monitor all its consumer accounts that were 30 days past due. By using this data, the company was able to identify when customers had varying life events—a change in address, mobile phone number, employment or salary, new loans or inquires, for instance—that might affect their ability to pay their auto loan. For this scenario, the file being monitored included 74,000 records.

Within the first 30 days, there was about a five percent level of activity, meaning that 3,870 of the company’s customers from this file were found to have had some type of life event that might impact their ability to pay on their existing loan. This access to timely, relevant information helps auto financing companies determine when and how to proactively start a conversation with customers and respond with the appropriate servicing or collections activity.

By identifying these customers with life-event issues, the auto financing company was able to help their customers manage their payments, provide a new credit product or even refinance, in an effort to avoid repossession of the vehicle. It also enabled the company to give reliable and current right-party contact (RPC) information, when needed, to its servicers, skip-tracers and collectors, eliminating otherwise time-consuming searches for each past-due consumer.

Within 60 days, 11,380 customers, or 15 percent of the total file on the accounts that were 30 days past due, showed some sort of level of activity or change. However, when that timeframe moved out to 180 days, that figure jumped to 29,660 customers.

Therefore, in a six-month timeframe, by monitoring this one batch of records of 30-day past due customers, the company was able to identify 40 percent of its customer base as having had some sort of life event significant enough to impact their original loan terms.

With these results, the company was able to build a use case about alternative credit data based on its monthly net savings return alone. By identifying 40 percent of its customers in this file as having a life event, the company was able to speak to these consumers on their level and help them by adjusting the terms of their loans.

After implementing alternative credit data and incorporating this approach into its portfolio management, the company conservatively estimated that it kept at least five customers’ motor vehicles from being repossessed each month. Considering that the average net charge-off rate per unit per repossession totals about $5,000 in costs and fees, it is estimated that the total monthly loss avoidance totaled about $25,000. Even with nominal monthly fees in monitoring the data at about $3,500, the company had a net monthly return of $21,500, or an annual savings of about $258,000.

Auto financing companies can continue business as usual and see the same results. Or they can take a look at available, cost-effective, easy-to-implement resources like alternative credit data, and see immediate improvements to their bottom line.

Dallas Munkus is director, customer analytics, with FactorTrust, The Alternative Credit Bureau, where he helps customers leverage the power of FactorTrust data to positively impact their business. He has more than 20 years in consumer finance, leading analysis and strategy on diverse teams, including organizations, operations, servicing, collections and more. Connect with Dallas at dmunkus@factortrust.com. Learn more at www.FactorTrust.com.