Lending Use Case: Stopping Fraudulent Loan Applications

A mid-sized lender was experiencing growing losses from fraudulent applications. Applicants were submitting tampered paystubs, fake tax documents, and forged IDs to qualify for loans. Manual reviews were slow and inconsistent, leaving the business vulnerable to fraudsters while delaying approvals for genuine borrowers.

The Challenge

• Increasing fraud attempts using edited paystubs and bank statements
• Synthetic identities created with forged IDs slipping past basic checks
• High manual review costs and longer onboarding times
• Difficulty proving fraud cases due to lack of clear evidence

The Solution

The lender integrated DeepXL's API into their loan origination system. Every ID and document was automatically scanned with forensic AI:

• Document forensics to detect manipulated paystubs, invoices, and tax forms
• ID verification to spot altered photos, fonts, and security features
• Confidence scoring (Trusted, Warning, High Risk) to route applications intelligently — safe fast-tracked, risky ones flagged
• Explainable AI providing heatmaps and anomaly details, giving staff evidence they could act on

The Results

• 6% fraudulent applications flagged
• Significant reduction in manual reviews, cutting onboarding time by 40%
• Staff productivity improved as resources focused only on flagged cases
• Clear, auditable evidence made compliance and reporting easier

Impact

With DeepXL, the lender gained both speed and security. Fraudulent applications were blocked early, reducing losses and protecting the loan book, while real customers enjoyed a faster, smoother onboarding experience. The use case shows a clear path to long-term ROI, with potential savings of millions in prevented fraud losses.