Case Study

Top Credit Provider Prioritizes Marketing Leads to Save $5M

How a lender safely increased credit lines with Enigma’s data

Better coverage of the client’s SMB portfolio

Richer data about business growth and revenues

Leading indicators of business decline and distress

Early results


Increase in risk model accuracy


New high-value businesses identified


Expected incremental revenue

The Challenge

Safely increase credit lines

A top 10 SMB lender wanted to grow revenue without increasing loss ratios. Safely increasing credit lines was a core part of this strategy. Throughout 2020, the risk team saw increased volatility in their small business portfolio. Without clear visibility into their portfolio’s health, they were missing opportunities and taking on unnecessary risk.

The risk team assigns a risk score to every business in their portfolio and uses it to segment customers into three buckets: decrease, maintain, or increase credit lines. Accuracy is vital with the risk score, which gets refreshed each month. The team’s models previously relied upon internal data, as well as data from bureaus and the Small Business Financial Exchange. The team sought more timely data with signals about a business’s financial health to improve their models’ accuracy and overall customer management.

The Solution

Add timely data to improve customer segmentation

The team introduced Enigma’s Merchant Transaction data into their models and backtested against historical data. They found Enigma’s data increased their models’ accuracy by around 25% for about one third of their portfolio.

In particular, Enigma’s data about presence of transactions, transaction stability, and three-month growth rates led to the strongest increase in predictive power of their model. More accurate models meant the team could improve customer segmentation, both reducing risk and uncovering new opportunities.

Aligned to strategy, credit line increases made up a large share of the team’s new revenue generation. With these risk model data improvements, the team identified around 5% of their portfolio that was eligible for incremental credit line increases. During the first year of implementation, the team expects these adjustments will generate $30,000,000 in incremental revenue.

Early Results

  • 25% increase in model improvements
  • 70,000+ new high-value businesses identified
  • $30M in expected incremental revenue

Ready to explore what more robust small business data can mean for you?