AnandRajendran87/account-takeover-device-behavior-package

# Detecting Account Takeover Fraud Through Device Intelligence and Behavioral Biometrics ## Overview This repository contains a reusable AI-driven Account Takeover (ATO) fraud detection framework using device intelligence, behavioral biometrics, machine learning, streaming risk scoring, and adaptive fraud orchestration. The framework is designed for banks, fintech platforms, insurance providers, healthcare portals, digital commerce platforms, and enterprise identity security teams. ## Business Problem Account takeover fraud occurs when attackers gain unauthorized access to customer accounts using stolen credentials, phishing, malware, credential stuffing, SIM swap attacks, session hijacking, or social engineering. Traditional authentication systems that rely only on usernames, passwords, and one-time passcodes are not sufficient because attackers may appear legitimate after successful login. ## Solution Objectives - Detect suspicious login and session activity in real time. - Identify unfamiliar, spoofed, or high-risk devices. - Use behavioral biometrics for continuous authentication. - Score account takeover risk using device, network, behavioral, and transaction signals. - Trigger adaptive authentication based on risk level. - Support fraud analyst review with explainable reason codes. ## Architecture Layers 1. Digital access channels 2. Authentication and session telemetry 3. Device intelligence 4. Behavioral biometrics 5. Streaming event processing 6. AI-driven risk scoring 7. Adaptive fraud orchestration 8. Analyst investigation workflow 9. Explainability and governance 10. Monitoring and drift detection ## Key Capabilities - Device fingerprint risk scoring - Proxy, emulator, and spoofing indicators - Login velocity monitoring - Impossible travel detection - Typing cadence analysis - Mouse movement and scroll behavior analysis - Mobile touch gesture analysis - Session behavior anomaly detection - Real-time ATO risk scoring API - Adaptive authentication decisioning ## Expected Business Impact - Reduced unauthorized account access - Lower fraud losses from compromised accounts - Improved continuous authentication - Lower false positives through contextual risk scoring - Better customer experience through adaptive friction - Stronger auditability using explainable risk indicators ## Disclaimer This repository is for educational, research, and enterprise architecture demonstration purposes. It does not include confidential customer data, production security rules, or proprietary fraud models.