Challenge
- A leading bank in Saudi Arabia struggled with inefficient risk management.
- Delays in detecting fraudulent transactions.
- Traditional rule-based fraud detection methods generated false positives.
- Compliance teams overwhelmed and impacting customer experience.
Our Approach
- Deploying machine learning algorithms for anomaly detection in real-time transactions.
- Integrating predictive analytics to assess customer behavior patterns and flag high-risk activities.
- Automating compliance reporting, reducing manual interventions and false alerts.
- Optimizing credit risk assessment models, improving loan and investment decision-making.
Outcome
- 30% reduction in fraud cases, enhancing financial security.
- 60% faster risk detection, minimizing potential damages.
- 20% improvement in compliance efficiency, streamlining regulatory processes.
