Fraud Detection

Use AI to detect and prevent fraud in real-time, reducing losses and improving efficiency.

Business impact

• Fraud detection rate — Improves by identifying more fraudulent transactions accurately and timely
• False positive rate — Reduces by distinguishing legitimate transactions from fraud, lowering unnecessary reviews
• Operational efficiency — Increases by automating detection and reducing manual investigation workload
• Customer satisfaction — Enhances by minimizing fraud impact and reducing false declines on legitimate users
• Fraud loss reduction — Decreases financial losses by preventing fraudulent transactions before completion

Data requirements

• Transaction records (Structured) — Used to analyze patterns and detect anomalies in payment activities
• User behavior logs (Text) — Capture behavioral biometrics and activity patterns to identify suspicious actions
• Device and network data (Numeric) — Provide device fingerprinting and IP analysis for fraud risk scoring
• Historical fraud cases (Structured) — Train models on known fraud patterns to improve detection accuracy
• External data sources (Text) — Incorporate open data and social media for enhanced identity verification

AI methods and techniques

• Predictive AI — Forecasts likelihood of fraud by analyzing transaction and behavioral data patterns
• Generative AI — Simulates fraudulent scenarios to improve detection models and reduce false positives
• Agentic AI — Orchestrates multi-step fraud investigation workflows with automation and human oversight

AI models and model families

GPT-4o, Claude, XGBoost, Graph Neural Networks (GNNs), Transformer models

Industries

Real-world evidence

20 documented case studies on record.

Companies using this: American Express, BNP Paribas, Binance, Bunq, Caixabank Sa, Calltic, Deloitte, Fraugster, Gumtree, HCLSoftware, Mastercard, Nat West, Nethone, OKX, Revolut and 3 more.