Risk, Fraud & Trust Intelligence

Fraud is the single biggest threat to digital businesses in India. From synthetic identity fraud and account takeover to payment fraud and loan stacking, the sophistication of fraud attacks is increasing exponentially. Traditional rule-based fraud detection systems are no longer sufficient — they generate too many false positives, miss emerging fraud patterns, and cannot adapt to new attack vectors in real-time.

digiverification 's Risk, Fraud & Trust Intelligence APIs provide a next-generation approach to fraud detection. By combining multiple verification signals — identity data, financial intelligence, device fingerprints, behavioral patterns, and digital footprint analysis — into unified risk scores, our APIs enable businesses to detect and prevent fraud before financial loss occurs.

Fraud Risk Score is our flagship risk intelligence API. It analyzes multiple data points from across the digiverification verification ecosystem and external signals to generate a comprehensive fraud risk score for each user or transaction. The score considers identity verification results, financial behavior patterns, device intelligence, location signals, and historical fraud indicators. Businesses can use this score to automate approval, rejection, or manual review decisions based on their risk tolerance.

Identity Deduplication detects duplicate accounts and identity overlaps across your user base. Fraud rings often create multiple accounts using slight variations of the same identity — different phone numbers, slightly altered names, or shared device fingerprints. Our deduplication engine identifies these patterns, helping platforms prevent multi-accounting, referral abuse, and coordinated fraud attacks.

Device and Behavioral Signals provide a critical layer of fraud intelligence. Our APIs analyze device fingerprints (browser, OS, screen resolution, installed apps), behavioral patterns (typing speed, navigation patterns, session duration), and network signals (IP reputation, proxy/VPN detection, geolocation consistency) to identify suspicious activity. This is particularly effective against automated bot attacks, account takeover attempts, and social engineering fraud.

Trust Score API provides a unified 0–1000 confidence rating that combines all available verification signals into a single, easy-to-interpret score. A high trust score indicates a verified, low-risk user, while a low score flags potential fraud or incomplete verification. Businesses can integrate this score into their onboarding flows, transaction approval systems, and risk management dashboards for real-time decision-making.

digiverification 's fraud intelligence is built on machine learning models trained on millions of Indian verification records. The models continuously learn from new fraud patterns, improving detection accuracy over time while minimizing false positives that hurt genuine users.