Gig Economy
31% reduction in income-uncertainty defaults for gig workers
An online lender serving independent contractors replaced static income underwriting with a cash-flow volatility model — cutting defaults without shrinking the book.
−31%default rate reduction
Context
A fintech lender discovered that gig-economy borrowers had 2.4× the default rate of traditional W-2 borrowers at equivalent stated DTI ratios. Volatility in monthly income — not income level — was the root cause.
What we built
AgentHive's feature engineering pipeline constructed a 12-month rolling income-volatility index from bank-feed transactions. The rules engine added an income-stability gate on top of the ML score, with automatic SHAP reason codes for every decline.
Results
| Metric | Before | After |
|---|---|---|
| 12-month default (gig cohort) | 8.9% | 6.1% |
| Approval rate (gig cohort) | 54% | 57% |
| Manual-review queue depth | 38% of pipeline | 7% of pipeline |
| Regulatory exam findings | 2 | 0 |
"We didn't shrink our book — we just stopped lending to volatility risk we couldn't see. AgentHive made that volatility visible." — Head of Risk
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