AI-Driven Lending Strategy
AI
Developed an AI strategy to reduce portfolio at risk, improve cost per acquisition, and optimize debt-to-income ratios — cutting the at-risk portfolio by 50.6%, from $8.5M to $4.2M, with conservative projections validated in a 5-week analysis.
Portfolio at Risk
in CPA
Ratio Reduction
Challenge
The institution's lending operation was bleeding money across three critical fronts. Collections teams only contacted borrowers once they were already 10 days past due — by which point many accounts had already deteriorated beyond recovery. There was no early-warning system, no predictive capability, and no proactive outreach strategy.
On the acquisition side, high drop-off rates plagued the application funnel. Manual review of IDs and bank statements took up to 48 hours before applicants received a simple yes or no, driving qualified borrowers to competitors with faster turnarounds.
Underwriting relied on stated income and rough estimates rather than verified data. This led to approvals for over-leveraged borrowers who appeared creditworthy on paper but carried hidden debts — inflating portfolio risk and eroding margins.
Solution
Predictive Outreach. AI agents now identify borrowers likely to miss a payment based on behavioral data and transaction patterns, triggering automated personalized reminders via SMS and WhatsApp — days before a payment is due, not after it's missed.
Instant Pre-Approvals. Optical Character Recognition (OCR) and automated credit logic agents replaced the 48-hour manual review cycle. IDs and bank statements are now processed in seconds, delivering instant pre-approval decisions and dramatically reducing funnel drop-off.
Real-Time Debt-to-Income Verification. Agents leverage Open Banking APIs to pull real-time cash flow data, calculating exact debt-to-income ratios rather than relying on stated income estimates. Over-leveraged applicants are flagged before approval, not after default.
By shifting from reactive collections to predictive intelligence, and from estimated underwriting to verified data, we turned a $8.5M at-risk portfolio into a $4.2M one — with conservative numbers from a 5-week blueprint.