Loan Application Fraud: Detecting Fake Documents Using Data Analytics

A public sector bank faces increasing loan application frauds with forged documents. Build an analytics solution to verify document authenticity and flag suspicious applications.

## Background

Indian banks lost ₹71,000 crore to loan frauds in FY 2023-24. The majority involved forged documents including:
- Fake salary slips and Form 16
- Manipulated bank statements
- Counterfeit property documents
- Fabricated business financials

## The Problem

A leading public sector bank has identified that approximately 8% of rejected loan applications showed signs of document manipulation. However, manual verification is slow and inconsistent.

## Your Task

Build an automated document verification and fraud scoring system that:
1. Detects anomalies in submitted documents
2. Cross-validates information across multiple sources
3. Flags high-risk applications for detailed investigation
4. Provides explainable reasons for each flag

## Data Provided

- 50,000 historical loan applications (approved + rejected)
- Document images (scanned salary slips, bank statements, IT returns)
- Applicant details (demographics, employment, loan purpose)
- Bureau data (CIBIL score, credit history)
- Verification outcomes (which applications were later found fraudulent)

## Success Criteria

- Detect at least 85% of fraudulent applications
- Keep false positive rate below 10%
- Provide interpretable risk scores
- Handle multiple document types and formats