AI fraud detection software
An AI-powered fraud detection platform was built to give a digital bank real-time protection against financial threats. The system was designed to:
- Detect fraudulent activity using a hybrid rules and ML engine for both known and novel fraud patterns
- Monitor accounts, payments, lending, and crypto trading with sub-second scoring
- Automate AML, KYC, PSD2, and GDPR compliance checks within the transaction pipeline
- Equip analysts with a unified dashboard featuring alert workflows, escalation, and model explainability
The platform unifies fraud prevention, compliance, and operational efficiency in a single scalable ecosystem, delivering end-to-end visibility from transaction ingestion to case resolution.
Team structure
ML/AI Engineer, Backend Developer (.NET Core / C#), Data Engineer (Kafka / Spark), Cloud & DevOps Engineer (Azure / Kubernetes), Fraud & Risk Analyst, Compliance & Regulatory Specialist (AML/KYC, PSD2, GDPR), Frontend Developer (Case Management Dashboard), QA / Security Engineer, Solution Architect
Used technologies
.NET Core, C#, ASP.NET Web API, Entity Framework Core, TensorFlow.NET, ML.NET, Apache Kafka, Apache Spark, PostgreSQL, Redis, Azure Kubernetes Service, Azure Monitor, Docker, RESTful APIs, Prometheus, Grafana, Keycloak
Development approach
We built a real-time scoring engine on Apache Kafka and Spark Streaming, processing millions of transactions per hour with sub-second latency. A rules and ML hybrid architecture combines deterministic rules with continuously retrained models to detect both known and emerging fraud patterns.
A behavioural analytics engine feeds dynamic risk scoring and anomaly detection across all banking channels, with AML, KYC, PSD2, and GDPR compliance embedded directly into the pipeline for automatic screening and full auditability.
Fraud analysts work through a unified dashboard with real-time alert workflows, false positive filtering, automated escalation, and model explainability for every flagged transaction.
Result
A unified AI fraud detection platform replaced a fragmented, rule-only legacy system with a scalable, intelligent ecosystem for real-time threat response, compliance automation, and investigative efficiency. Proactive anomaly detection and advanced behavioural analytics delivered a 40% reduction in fraudulent transactions, directly protecting customer assets and strengthening trust in the digital-only banking model.
Operational efficiency increased by 30% through automated case management, smart alert prioritisation, and false positive reduction — freeing analysts to focus on genuine threats. Embedded compliance automation cut compliance costs by 25%, streamlining audit reporting and ensuring continuous adherence to AML, KYC, PSD2, and GDPR requirements across all European operations.