Real-time banking risk & retention intelligence powered by advanced ML
of at-risk customers identified for proactive retention campaigns
overall fraud detection accuracy with minimal false positives
precision reduces unnecessary retention marketing spend
of fraudulent transactions caught in real-time
Comprehensive analysis of banking customer behavior and risk patterns
100% churn rate for customers with 4 products. Immediate portfolio optimization needed.
Female customers 50-59 in Germany represent the highest combined risk profile.
2-product customers show 7.6% churn - optimal cross-sell target for single-product users.
Active members have 47% lower churn rates than inactive customers.
Advanced XGBoost models with proven industry benchmarks
Algorithm: XGBoost with SMOTE
Features: 64 engineered features
Training: 600 estimators, early stopping
Threshold: 0.56 (optimized for recall)
Algorithm: XGBoost Ensemble
Features: 31 PCA features + Amount_log
Training: 1200 estimators, AUCPR optimization
Threshold: 0.88 (optimized for precision)
Test our production-ready models with custom inputs
Assess customer retention risk using advanced ML models
Real-time transaction fraud risk assessment
Fraud detection uses 28 anonymized PCA features (V1-V28) from the transaction. For demo purposes, we'll simulate typical transaction patterns based on amount and timing.