AI-Powered Fraud Detection: ₹1.2 Cr Saved
How do you catch a fraudster who forged the "perfect" bank statement? Our AI caught it in 2.7 seconds.
The scam: A loan applicant submitted pristine-looking salary statements showing ₹3.2L monthly income. Clean CIBIL. Regular deposits. Everything checked out.
What our system detected:
❌ PDF created April 2024, claiming transactions from 2023
❌ Font inconsistencies in amount fields
❌ Weekend salary credits (employer closed)
❌ Metadata showed 4 Photoshop edits
Result: ₹45L fraud attempt blocked.
Across 1,000+ NBFCs and banks using Precisa:
→ 150M+ transactions analyzed
→ ₹1.2 Cr in prevented fraud
→ 97% detection accuracy
→ 2.7 seconds average detection time
The difference? AI doesn't get tired. It checks 47 fraud parameters on every statement. Every. Single. Time.
Are you still relying on manual review to catch financial fraud?
This is a strong fintech trust story because the real product is not just statement analysis. It is risk infrastructure sitting between lenders and bad financial decisions.
The fraud examples make the positioning much sharper: forged PDFs, metadata edits, salary pattern mismatch, hidden debt, and account behavior all point to something bigger than manual review replacement. This feels closer to a verification layer for financial truth.
One thing I would pressure-test is the product brand around Precisa. It is clean, but if the product becomes the fraud/risk layer behind banks, NBFCs, and lending workflows, the name has to carry serious security and infrastructure weight.
Vroth .com would fit that direction well because it feels harder-edged and more risk-focused, while leaving room for fraud detection, transaction intelligence, borrower verification, statement analysis, and financial risk infrastructure under one stronger brand.