Fraud Prevention

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

Business impact

• Fraud detection rate — Higher detection rates reduce undetected fraudulent transactions and financial losses
• Fraud loss reduction — Lower fraud losses improve profitability and reduce financial risk exposure
• Customer trust — Enhanced security increases customer confidence and loyalty
• Operational efficiency — Automated fraud detection reduces manual review workload and speeds response
• False positive rate — Reducing false positives improves customer experience and lowers investigation costs

Data requirements

• Transaction records (Structured) — Analyze patterns and anomalies in payment and account activity
• User behavior logs (Numeric) — Monitor behavioral biometrics and interaction patterns for fraud signals
• Device and IP data (Text) — Identify suspicious device usage and geolocation inconsistencies
• Historical fraud cases (Structured) — Train AI models on known fraud patterns and outcomes
• External threat intelligence (Text) — Incorporate data on emerging fraud tactics and blacklisted entities

AI methods and techniques

• Predictive AI — Forecast potential fraud risks by analyzing transaction and behavior patterns
• Generative AI — Simulate fraud scenarios to improve detection models and reduce false positives
• Agentic AI — Automate real-time decision-making and response to suspected fraud events

AI models and model families

GPT-4o, Claude, Llama, Custom ML fraud detection models

Industries

Real-world evidence

7 documented case studies on record.

Companies using this: Binance, DAT Freight & Analytics, Food, Grasshopper Bank, Mastercard, Onda Pi, Orca.