A Winning Strategy: Identity Data and the Path to Auto Fund

Online lending companies are in a bind. In order to stay ahead of the competition, they need to tackle two problems that—at first glance—look to be at odds:

  1. Provide loans quickly and at scale
  2. Reduce fraud and defaults

To help companies navigate this dilemma, Ekata’s Director of Online Lending, Kurt Weiss, joined Anthony Delmonico, Risk Manager at BorrowWorks (a company that helps lenders make data-driven credit decisions) to discuss the what, why, and how of auto-funding.

Identity data and auto-funding workflows can help lenders reach their goal of getting more loans funded while decreasing their risk of default, but it’s not without its challenges.

Read below for the highlights of Kurt and Anthony’s conversation, but be sure to watch the full webinar for an in-depth look at the philosophy and technology behind auto-funding, as well as a deep dive into the data aspects that allow you to confidently engage an auto-funding strategy.

Why auto-funding?

Auto-funding involves creating an underwriting workflow focused around identifying good customers to speed through a loan decision without unnecessary friction and manual review in origination process. One way to think of it is like TSA Pre✓, which allows pre-vetted customers through while applying a more thorough review process to customers that haven’t been identified as a sure bet.

Anthony notes that auto-funding’s major value proposition is to help online lending companies scale. It does this by bringing in the number of good applicants you need to meet your goals without losing too many people to friction in the workflow.

Along with achieving scale, auto-funding can:

  • Lower cost per fund
  • Increase conversion rates
  • Mitigate fraud-related defaults

Lead verification: Laying the groundwork for auto-funding

The initial challenge with most online lending companies attempting to scale is the quality of the leads coming in. That’s why lead verification needs to be the first step for companies wanting to streamline their lending process.

Kurt notes that lead scrubbing (or lead modeling) can give an immediate lift to the business. That’s because if you’re starting off with a cleaner slate, you’ll see two immediate benefits. First, you’ll reduce your number of fraud-related defaults. Second, you’ll reduce your cost per fund because you’re no longer paying for bad leads, and you’ll be spending less time verifying potential customers.

Anthony recommends companies scrub their leads by using identity data to sort leads into three “stoplight” categories: red (unverified; high risk), green (verified identities; low risk), and yellow (partial verification; needs more information).

Unverified leads can be culled immediately while verified leads can be moved into the auto fund workflow. The partially verified leads can be placed into a workflow that adds additional friction in order to verify them. For example, you can require these leads to provide more hard documentation while your best leads can be moved through without the extra steps.

Identity data: enabling lead verification

How do you quickly identify which leads fall into which categories? Through robust identity data, applied intelligently.

Kurt and Anthony discuss which identity factors—such as IP—are valuable but underutilized, and when you need to start using them in your auto fund process. They also delve into where to get this data, whether that’s using your company’s historical customer data and custom black lists, free data tools, or solutions like Ekata Identity Check that can help you confirm the identities of leads you don’t already have data on and can’t verify through free data.

Many companies amass data without having a good strategy on how to use the information, Anthony cautions. While you can collect all the data on leads you want, in order to get to auto-funding you need a strategy for using that information—such as creating custom scores, blacklists, and otherwise utilizing what you have to your competitive advantage.

Anthony suggests a three-phase process:

  1. Examine fraudulent behaviors to identify bad actors
  2. Look at your best customers to identify qualities that make auto-funding a possibility
  3. Apply additional friction to the unknowns in the middle

Identifying the top five to ten percent of applicants is a good place to start, he notes. Then, companies can begin to expand their auto fund pool as they get more comfortable with the process. In the webinar, Kurt and Anthony go more in-depth on strategies to get that percentage as high as you possibly can, intelligently.

Be sure to check out the whole webinar for more in-depth information on how to scale your online lending business through auto-funding.

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