Credit Risk
Credit Risk

Introducing Enigma Risk and Underwriting

According to the Small Business Administration, two out of three business owners who seek credit do not receive what they need. One of the biggest barriers is surprising: not bad applicants with high risk, but instead sparse, thin, and inaccurate data on applicants.

What is Enigma Risk and Underwriting?

Enigma Risk and Underwriting offers accurate intelligence about the identity and financial health of 16 million + businesses to help you dynamically manage risk. Enigma Risk and Underwriting is a pre-permissioned data set. Enigma requires zero opt-in and can be used across any portfolio of business applicants. 

Why ask a small business for their bank account information - and lose 70% of your applicants – upfront? We help you facilitate an offer to your small business prospects, before asking for additional information. We help you see that prospect’s card transactions from a third party, versus what the merchant itself chooses to provide to you.

The results with new customers and evaluations are clear: Enigma can help fuel profitable growth by approving 20-30% more applicants and granting higher credit lines to those approved. Our data is also used to cut out pockets of riskier populations that our clients previously had no insight into. The coverage of our data is high with the ability to match to 70-80% of our client's portfolio.

We support the efforts of business lenders with: 

  • Prequalification: 
  • Business credit card & loan approval decisions: By using Enigma’s firmographic and merchant transactions attributes, credit card and line issuers are able to predict delinquency prediction before signing on customers.
  • Credit line issuance: For less risky customers, Enigma helps you offer larger credit lines with more confidence.

For payment processors we help with:

  • Merchant cash advance underwriting: By using Enigma’s card revenue data, MCA underwriters are able to give larger advances upfront without the need for a long history of processing data.
  • Chargeback risk and friendly fraud: By using attributes like the presence of an Enigma match, customers can better understand chargeback risk and friendly fraud.

How Might Enigma Risk and Underwriting Help You?

We’ve worked alongside our customers over the last year to understand how a general data asset like Enigma’s can aid with a variety of use cases. We understand that different features of our data asset have different importances across different products and businesses, and that some feature transformation is necessary. We’ve done that exploration ourselves and have it in our evaluation guide, so you can accelerate your evaluation. We explore the features that seem to have the most signal across a variety of use cases below.

Use Case 1: Business card and line issuers seek to decrease delinquency and offer higher credit lines

Business card issuers within large financial institutions have a difficult time underwriting business cards for applicants - usually, they must rely on personal guarantees from the business owner and information from the credit bureaus and existing accounts.

We met with some of our customers in this space – three of top ten business card issuers issuing lines between $5,000 and $100,000 – in order to see if we could help them decrease chargeoff rates of applicants with thin files and offer higher credit lines to lower risk customers.

For this use case, these features were found to be the most powerful: 

  • Average monthly revenue
  • Average transaction size
  • Months of merchant transactions history
  • Months since last calendar year’s lowest revenue month

By using Enigma’s data, our customers were able to approve ~20% more small businesses for which there were thin files, without asking new applicants to connect bank accounts. Within all approved accounts, Enigma features were used to segment 15% of the population with 57% lower chargeoff rates - a population ripe for greater line sizes.

Use Case 2: Payment facilitators and processors extend merchant cash advances with Enigma

Processors that offer MCAs generally need at least 6 months to a year of history prior to underwriting a loan - even then, they may only see a portion of the card revenues of a business (e.g. a business might use different processors/payfacs for online vs. offline transactions).

Multiple large payment facilitators and processors that offer merchant cash advances came to Enigma with the goal of offering larger loan size for brand new customers and determining the probability of default for brand new customers. 

Combining a variety of product features, we landed on average transaction size and months of merchant transactions history as two key signals for underwriters to write larger loans sooner for business clients, as well as use this knowledge to turbocharge pre-qualification and prospecting efforts. All the major processors use us as a core data set for revenue-based underwriting or MCAs, with one saying “We built our core MCA model with your data.”

Use Case 3: Payment facilitators and processors seek to decrease chargeback and friendly fraud risk

Non-lending credit risk incidents, including chargeback risk and friendly fraud, put wholesale ISOs, payment facilitators, and major card processors at risk with card networks. Moreover, if the business shuts down, the merchant processor may be on the hook for chargebacks instead. This risk is especially high during initial onboarding, when the processor has very little information about a particular merchant, especially as onboarding processes become increasingly automated.

Payment facilitators and processors of different sizes need Enigma data to help reduce non-lending credit risk for new merchants (e.g. chargeback risk or friendly fraud). For this use case, these features were found to be the most powerful: 

  • Average monthly revenue
  • Revenue and transaction growth trends
  • Refund amount
  • Name of previous processor 
  • Performance against benchmarks by industry and geography

These features were effective at splitting risk and the results were intuitive. At a high level, we found that a subpopulation comprising of half of all onboarded merchants within the last two years was found to have 40-50% higher chargeback incidence rate if they had no match on any business OR no credit card transaction presence in the last 12 months. More granularly, a subpopulation comprising ~1% of the total was found to have 5x the credit risk incidence rate if they were found in certain Enigma-provided industries and an abnormal amount of card refunds.

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