Case Study FinShield AI – Real-Time Fraud Detection & Risk Scoring Engine (UK)

Project Overview

FinShield AI was developed for a high-growth FinTech payment provider based in London, UK. The platform monitors high-volume card payments, open banking transfers, and digital wallet transactions in real time to prevent financial fraud while maintaining sub-second user experience.

Client & Location

FinShield Pay Ltd
Location: London, United Kingdom
Industry: FinTech / Payments

Challenges

• Processing over 250 payment transactions per second with strict requirement for sub-50ms latency.
• Elevated false-positive rates causing genuine customer card transactions to be declined.
• Compliance requirements under UK FCA and European PSD2 Strong Customer Authentication (SCA).

Solution

SM Softwares engineered an asynchronous streaming anomaly detection architecture using Apache Kafka and lightweight gradient-boosted decision trees deployed via AWS SageMaker with millisecond inference response times.

Technologies Used

• Python & TensorFlow
• Apache Kafka & Redis In-Memory Datastore
• AWS SageMaker & AWS Lambda
• RESTful Webhook Alerting Engine

Results & Business Impact

• Average fraud inference latency of 38 milliseconds per transaction.
• 34% reduction in false-positive transaction declines.
• Over £2.1M in fraudulent payment attempts intercepted in the first 6 months of deployment.