A Belgian bank cut risk assessment time from 14h to 3h

The context

Belgian banks operate under persistent pressure from financial volatility and cyber threats. Traditional risk management approaches often fail to keep pace with the complexity and speed of emerging risks, resulting in operational inefficiencies and exposure to fraud.

The logic

Standard manual processes for risk modelling and fraud detection are limited by scale and latency. Our internal benchmarks show that human teams alone cannot screen the volume or diversity of threats present in modern banking environments. Across multiple engagements, our practice has observed that integrating AI-driven models with big data analytics enables deterministic, scalable risk assessment and real-time anomaly detection.

The solution

We deployed composite AI models to optimize asset liability management and automate fraud detection for our client.

• Asset liability optimization was achieved by integrating market data (interest rate trends, asset price fluctuations), customer behavior analytics, and environmental risk factors into constraint-based risk models.
• The models enabled precise balance sheet structuring, supporting profitability and capital stability.
• Product and service recommendations were tailored to customer segments, allowing targeted investment and improved customer experience.
• Manual tracking interventions were reduced by automating client identification and authentication, cross-referencing historical and real-time transaction data.
• Fraud detection algorithms were engineered to identify transaction anomalies before completion, leveraging quantitative metrics for early intervention.

Next steps in applied excellence

Model efficiency is directly correlated with data fidelity. Adding external data sources will further increase predictive accuracy and operational robustness.