A Hummer or a Hybrid? Identity Verification in Lead Generation and Origination in Online Lending and FinTech

How are you growing your online lending business? Is your lead gen program a Hummer getting 6 miles to the gallon? Or is it a hybrid Lexus SUV with equal clearance, greater comfort, and 26 mpg?  If yours is like dozens of our online lending customers’ businesses, you are spending out your ears on lead generation ‘gas’ to fill the top of your Hummer’s funnel, using basic lead-purchase-decision logic and free filters to whittle down to a daily set of serviceable leads. Unfortunately, alternative credit bureau filters and de-duping don’t dig down into the real quality of the lead: the veracity of the metadata and the likelihood of a real, contactable customer existing behind the lead’s non PII identity elements.
For example: say you look at 50,000 leads a day in a ping-tree system to decide which 10,000 you want to vet at cost with your alternative credit bureau checks. Those checks may ping for known fraud, bank account data, and scoring, but what they will not answer for you is the question, “Is this person likely to be a real identity, or could this be affiliate fraud, identity theft, or some other risky identity?”
The hardest thing for a fraudster or identity thief to accomplish is to tie together each piece of the non-PII identity into a cohesive, matching set.  Does the name match the phone? Does the name match the address? Maybe – and these are easier problems to solve, but does that phone match that address? Has that email address been seen in positive transactions across the web for more than a year? What is the line-type and is that Non-Fixed VOIP risky?  These questions are harder for fraudsters to satisfy, especially in the case of synthetic identity theft.
So here’s the status quo: online lenders buy leads without knowing the answers to the above questions, accepting existing FPD rates (first payment default) and conversion rates as they are, relying downstream on alternative credit pulls in underwriting to barely scratch the surface of the face value identity profile.
The status quo is insufficient in terms of vetting identities during the lead-purchase decision, allowing costly bad actors to slip through the cracks. Running an Identity Check query with Ekata for pennies per search before deciding to buy a lead can ensure you are spending your gas money as wisely as possible, avoiding affiliate fraud and other bad actors.
Identity Check is a 5-in-1 query of name, phone, email, address, and IP address against a 5 billion record Identity Graph Database.  Included in the response is a broad payload of data per queried element, indicating the veracity of each element and – and this is where bad actors get nailed – the matches between those elements.  Does the name match the phone? Does the phone match the address given? Is the line-type a risky non-fixed VOIP or a Big 4 trusted mobile carrier? Does the name match the email and have we seen this email for years in trusted online transactions? Is the IP address a proxy? And on and on with some 70+ response elements across the identity profile. With that rich data, lenders can easily build strong purchase decision logic in their LMS or decision engine.
Curbing bad actors with a sub-second query that answers these questions before purchasing the lead avoids incurring spend on leads with risk signals that correlate to poor conversion and FPD rates.  Using the saved budget, buy leads that return positive signals in the Identity Check response and realize the benefit three-fold downstream:

  1. FPD rates and Conversion rates tend to adjust positively as the gas you put in the tank has been ‘filtered,’ now excluding obvious erroneous phone numbers, malformed email addresses, and other risk signals that indicate that a lead is less likely to be contactable. (This can be tested in a retro study) .
  2. Besides saving $1.00+ in alternative credit pulls on junk leads that you’ve now avoided, the matching elements in the response and other data points can help you develop smart prioritization in your agents’ call-downs or automated underwriting processes. You can now prioritize the leads that show the greatest promise.
  3. With 70+ additional fields available on the customer record – metadata on phone, email, addresses, etc. – you now have a much stronger pre-collections profile with which to begin your collections efforts downstream.

Whether applicants come through organic channels or via a purchasing decision on leads from a ping-tree system, there is basic non PII information that inherently carries with it a rich set of valuable hidden metadata. This data surfaces when algorithmically processed and corroborated against billions of records in the Ekata Identity Graph Database, providing much-needed verification of the lead’s identity and contactabliity during the purchasing decision, with downstream effect in your underwriting process.  Putting better gas in the tank ensures the engine stays cleaner and propels us further on the same dime. I’ll take the Lexus Hybrid SUV for this commute.
Want to learn more? Let us know or come meet us at LendIt.

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