AI Fraud Detection & Risk Intelligence Platform

A rapidly growing digital payments company in India was processing more than 1.5 million transactions daily across UPI, wallets, prepaid cards, and merchant payments. As transaction volumes increased, fraudulent activities became more sophisticated, making traditional rule-based fraud detection ineffective.

• Existing fraud detection system relied on manually created rules generating thousands of false alerts every day.
• Fraud analysts spent most of their time investigating legitimate transactions while genuine fraud often remained undetected.
• Nearly 22% of legitimate transactions were flagged for manual review, causing payment failures and poor customer experience.
• Manual investigation of more than 18,000 alerts daily creating operational bottlenecks and increasing staffing costs.
• Lack of explainability in fraud decisions made compliance difficult.

DevSecCops designed an AI-powered contextual fraud intelligence platform capable of evaluating every transaction in real time using machine learning, behavioral analytics, and LLM-generated fraud explanations.

• Collection of data from payment gateway, mobile app, merchant systems, device fingerprinting, customer profiles, and historical fraud database.
• AI analysis of spending behavior, device trust scores, transaction frequency, merchant reputation, IP intelligence, velocity checks, and login anomalies.
• LLM-generated natural language explanations for every fraud decision: 'Transaction blocked because customer usually spends below ₹5,000. Current transaction of ₹95,000 originated from an unknown device.'
• Intelligent risk scoring between 0-100 with dynamic thresholds based on customer behavior, merchant category, and transaction value.

• Fraud investigators received complete transaction timelines, risk summaries, similar historical cases, and recommended actions with supporting evidence.
• Manual investigation time dramatically reduced through explainable AI recommendations.
• Confirmed fraud cases automatically updated future detection models for continuous improvement.
• Fraud pattern discovery identifying emerging fraud rings, coordinated merchant fraud, and money laundering behavior.

• 96% fraud detection accuracy.
• 42% reduction in false positives allowing legitimate customers to complete payments.
• 68% faster fraud investigations.
• 55% reduction in operational costs.
• 82% fewer manual reviews required.
• 99.95% platform availability.
• Real-time fraud decisions in under 250 milliseconds.
• Significant improvement in customer satisfaction due to fewer payment declines.