Fraud Detection

AI-powered real-time fraud detection to prevent financial losses and enhance transaction security

Business impact

• fraud detection rate — Higher detection rate reduces undetected fraudulent transactions and losses
• false positive rate — Lower false positives reduce unnecessary transaction declines and customer friction
• transaction approval rate — Improved approval rates increase legitimate transaction success and revenue
• customer trust — Enhanced security builds customer confidence and loyalty
• operational efficiency — Automated detection reduces manual review workload and speeds response
• fraud loss reduction — Effective detection lowers direct financial losses from fraud
• model training speed — Faster training enables quicker adaptation to emerging fraud patterns
• compliance adherence — Meeting regulations avoids penalties and supports legal operation

Data requirements

• Transaction records (Structured) — Analyze payment and transaction details to identify anomalies
• User behavior logs (Text) — Monitor user interactions and device usage patterns for fraud signals
• Device fingerprinting data (Structured) — Identify unique device characteristics to detect suspicious activity
• Historical fraud cases (Structured) — Train models on known fraud patterns to improve detection accuracy
• External data feeds (Structured) — Incorporate third-party risk and identity verification data
• Image and document scans (Image) — Use computer vision to verify identity documents and detect forgeries
• Communication logs (Text, Audio) — Analyze text and voice data for suspicious language or behavior

AI methods and techniques

• Predictive AI — Predict likelihood of fraud based on transaction and behavioral patterns
• Generative AI — Generate synthetic data and detect anomalies using advanced models
• Agentic AI — Automate decision-making and response actions in fraud prevention
• Symbolic AI — Incorporate rule-based logic for explainability and compliance

AI models and model families

XGBoost, Graph Neural Networks (GNNs), Transformer models, Ensemble models, Generative Adversarial Networks (GANs), Large Language Models (LLMs)

Industries

Real-world evidence

21 documented case studies on record.

Companies using this: American Express, BNP Paribas, Binance, Bunq, Caixabank Sa, Calltic, Deloitte, Fraugster, Gumtree, HCLSoftware, Mastercard, Nethone, OKX, Pay Pal China, Revolut and 4 more.