Strategy and Solutions

Zymr partnered with the client to implement a robust AI-based fraud detection engine. Our data science team developed real-time machine learning models using Python and AWS SageMaker, capable of scoring transactions with high precision. We introduced anomaly detection techniques through unsupervised learning models to flag irregular behavior across payment streams. To ensure transparency, we integrated a SHAP-based explainable AI (XAI) layer that provided clear justifications for each flagged transaction, satisfying both internal analysts and external auditors. All transaction data was centralized into a Snowflake data lake, with Kafka pipelines enabling real-time ingestion and scoring. We also enhanced the risk analysis process with a custom React dashboard, offering visualizations of fraud patterns and risk profiles. APIs were integrated directly into the client’s transaction processing system to ensure minimal disruption to live operations.

About the Client

A leading FinTech company delivering digital payment and real-time money transfer solutions across North America. With millions of transactions processed daily, the client aimed to enhance its fraud detection capabilities using AI while maintaining full PCI-DSS compliance.

Key Outcomes

Real-time risk scoring was enabled across 1M+ daily transactions

The client achieved PCI-DSS Level 1 compliance with comprehensive logging and encryption.

Business Challenges

The client’s existing rule-based fraud detection system was limited in scope, generating a high volume of false positives and failing to adapt to new fraud patterns. Critical transaction data was siloed across disparate systems, making real-time analytics and unified oversight nearly impossible. Additionally, the client faced growing pressure from regulators and internal teams to explain fraud decisions with greater clarity. The latency in alert generation further delayed incident response and investigation, putting customer trust and compliance at risk.

Business Impacts / Key Results Achieved

The client saw a 48% reduction in fraud-related financial losses within the first quarter of deployment. Fraud detection accuracy rose to 92%, while false positives dropped by 60%. The intuitive AI explanations reduced analyst workload and shortened investigation cycles significantly.