Fraud Risk Score API

Fraud detection in India's digital economy requires a multi-dimensional approach. Individual verification checks — identity, financial, or device — can be bypassed by sophisticated fraudsters. True fraud prevention requires combining multiple signals into a unified risk assessment that catches fraudulent patterns invisible to single-check systems. digiverification 's Fraud Risk Score API does exactly this.

Our Fraud Risk Score combines data from multiple verification dimensions: identity verification results (Aadhaar, PAN, face match), financial intelligence (bank account, credit bureau, income), device fingerprinting (browser, OS, device ID, emulator detection), behavioral analysis (typing patterns, navigation speed, session behavior), and digital footprint (email age, phone number history, social presence).

The API generates a comprehensive risk score for each user or transaction, ranging from 0 (highest risk) to 1000 (lowest risk). This score is accompanied by risk factors — specific signals that contributed to the score — enabling your compliance and risk teams to understand why a particular user or transaction was flagged. Common risk factors include mismatched identity data, new/disposable phone numbers, VPN usage, emulator detection, and suspicious behavioral patterns.

For real-time transaction fraud detection, our API delivers scores in under 500 milliseconds, making it suitable for payment authorization, loan approval, and account creation flows where speed is critical. The API can be integrated as a single-call risk assessment or as a continuous monitoring system that updates scores as new signals are collected.

Machine learning models power the scoring engine, trained on millions of Indian verification records and fraud patterns. The models continuously adapt to new fraud techniques, reducing false positives over time while maintaining high fraud detection rates. Unlike static rule-based systems, our ML models detect emerging fraud patterns that no predefined rule would catch.

For platform businesses managing high-volume user onboarding — fintech apps, e-commerce marketplaces, gaming platforms, dating apps — the Fraud Risk Score enables automated risk-based decisioning. Low-risk users get instant approval, medium-risk users undergo additional verification, and high-risk users are blocked or flagged for manual review.

Enterprise customers can request custom model tuning to optimize fraud detection for their specific business context, user demographics, and risk tolerance.