A Leading European Banking Group Reduced Transaction Decline Rate by 58% Using AI-Driven Fraud Detection Solution

Challenges

The bank’s legacy rule-based fraud detection systems struggled to detect sophisticated fraud patterns and adapt to evolving threats.
Key challenges included:

• Limited ability to process large, multi-source datasets in real-time.
• High false positives, leading to unnecessary transaction declines and poor customer experience.
• Lack of a scalable, automated fraud analytics platform to support proactive detection and prevention.

Solution

Coforge implemented a cutting-edge machine learning-driven fraud detection ecosystem that transformed the bank’s fraud prevention capabilities:

• Deployed an ML-based fraud detection framework using Hadoop and PySpark to analyse high-volume, multi-source data.
• Designed AI/ML models (XGBoost, Random Forest, Decision Tree, KNN) to detect hidden fraud patterns and improve accuracy.
• Automated data pipelines to process and integrate structured and unstructured data from multiple sources.
• Engineered artificial features using domain expertise to improve detection performance.
• Developed an interactive Bokeh dashboard to monitor rule performance, fraud trends, and key metrics in real-time.

The Impact

• 58% reduction in Transaction Decline Rate (TDR), improving customer experience.
• 18% increase in user approval rate, boosting transaction success.
• 20% reduction in False Positive Rate (FPR), reducing operational inefficiencies.
• 36% increase in Fraud Capture Rate, significantly improving detection capabilities.
• 5x increase in total captured fraud value, safeguarding financial assets.