Key Result
60% reduction in fraud losses
A regional bank was losing over $2M per year to fraudulent transactions. Their existing rule-based detection system was outdated, generating an excessive false positive rate of 18% that was overwhelming the fraud investigation team.
The compliance team required full explainability for every flagged transaction, making black-box AI solutions unsuitable. The bank needed a system that could reduce fraud losses while providing clear, auditable reasoning for every decision.
With transaction volumes growing and fraud patterns becoming increasingly sophisticated, the bank needed a real-time solution that could process thousands of transactions per minute while maintaining regulatory compliance.
AINinza built a real-time fraud detection ML model capable of processing 50,000+ transactions daily. The system uses an XGBoost ensemble with a SHAP explainability layer, providing clear feature-level explanations for every flagged transaction.
The solution includes automated risk scoring with configurable thresholds, allowing the bank's compliance team to adjust sensitivity based on evolving fraud patterns and regulatory requirements. Every decision comes with a human-readable explanation that satisfies audit requirements.
60%
Reduction in Fraud Losses
45%
Fewer False Positives
<50ms
Inference Time
3 weeks
5 weeks
3 weeks
3 weeks