Modernization of a Legacy Fraud Detection Tool with AI
Challenge
Our client, a digital bank, wanted to replace its outdated fraud detection tools with a unified system that could analyze transactions in real-time, assign dynamic risk scores, and issue immediate alerts. Regulatory compliance was critical, so seamless support for AML and KYC checks was a must. Just as importantly, the platform had to scale without friction and integrate cleanly with their existing banking infrastructure.
Our client, a digital bank, wanted to replace its outdated fraud detection tools with a unified system that could analyze transactions in real-time, assign dynamic risk scores, and issue immediate alerts. Regulatory compliance was critical, so seamless support for AML and KYC checks was a must. Just as importantly, the platform had to scale without friction and integrate cleanly with their existing banking infrastructure.
Solution
The engagement began with an in-depth technical assessment of the client's infrastructure and threat landscape. A dedicated team of nine specialists developed a high-speed fraud detection platform with a distributed architecture capable of handling millions of transactions hourly with sub-second latency.
Key components of the solution included:
Behavior-based risk scoring with contextual transaction analytics
Machine learning models (both supervised and unsupervised) to detect unusual activity patterns
Real-time data ingestion and processing pipelines using Apache Kafka and Spark Streaming
Seamless integration of compliance checks via automated AML/KYC workflows
A RESTful API suite for direct integration with core banking and third-party systems
A fully customizable dashboard for alert management, case investigation, and escalation
Containerized deployment on Azure Kubernetes Service with autoscaling and failover support
Continuous retraining workflows to keep fraud detection models accurate and adaptive
The engagement began with an in-depth technical assessment of the client's infrastructure and threat landscape. A dedicated team of nine specialists developed a high-speed fraud detection platform with a distributed architecture capable of handling millions of transactions hourly with sub-second latency.
Key components of the solution included:
Behavior-based risk scoring with contextual transaction analytics
Machine learning models (both supervised and unsupervised) to detect unusual activity patterns
Real-time data ingestion and processing pipelines using Apache Kafka and Spark Streaming
Seamless integration of compliance checks via automated AML/KYC workflows
A RESTful API suite for direct integration with core banking and third-party systems
A fully customizable dashboard for alert management, case investigation, and escalation
Containerized deployment on Azure Kubernetes Service with autoscaling and failover support
Continuous retraining workflows to keep fraud detection models accurate and adaptive
Results
Fraudulent transactions were cut by 40% with predictive anomaly detection.
Operational speed improved by 30% through automation and smarter alert handling.
Compliance costs dropped 25% thanks to built-in AML/KYC verification.
Fraudulent transactions were cut by 40% with predictive anomaly detection.
Operational speed improved by 30% through automation and smarter alert handling.
Compliance costs dropped 25% thanks to built-in AML/KYC verification.