Risk Management

Use AI to proactively manage and mitigate organizational risks with real-time analytics and automation

Business impact

• Fraud rate — AI reduces fraudulent transactions by detecting suspicious patterns in real time
• Operational efficiency — Automation decreases manual workload and accelerates risk-related processes
• Compliance rate — Continuous monitoring ensures adherence to evolving regulatory requirements
• Risk mitigation effectiveness — Improved risk identification leads to timely and effective mitigation actions
• Customer satisfaction — Reduced false positives and faster issue resolution enhance user experience

Data requirements

• Transaction records (Structured) — Used to detect anomalies and fraudulent activities in payments
• Customer interaction logs (Text) — Analyze communication patterns for risk signals and service issues
• Vendor and supplier data (Structured) — Monitor third-party risks and compliance status continuously
• Regulatory documents (Text) — Automate compliance checks by mapping policies to regulations
• Security event logs (Numeric) — Identify cybersecurity threats and vulnerabilities in real time

AI methods and techniques

• Predictive AI — Forecast potential risks and fraudulent activities before they occur
• Generative AI — Automate compliance documentation and scenario simulations for risk planning
• Agentic AI — Autonomously monitor and respond to emerging risks and compliance gaps

AI models and model families

GPT-4o, Claude, Llama, Custom ML models, MLP neural networks

Industries

Real-world evidence

11 documented case studies on record.

Companies using this: 4most, Binance, Capital One, City Ottawa, Jordi Labs, Klarna, Stripe, Sumitomo Corp, Supply Shift, Up Guard, Waymo.