Fintech Firm Cut Fraud Losses by 66% with Infoslab’s Real-Time Detection Team

Project Overview

A high-growth fintech operating across seven countries, this client delivers digital-first banking through peer-to-peer payments, virtual cards, and embedded credit lines. Their user base grew exponentially, but so did fraud vectors, particularly during high-volume transaction bursts.

Industry

Finance

Challenge

Their existing fraud system was:

• Rule-based and lag-prone: Detection came 2–3 minutes after the fraud occurred.

• Operationally expensive: 14%+ false positives drained analyst time.

• Lacked adaptability: No ML, no real-time drift detection.

• Hiring issues: Data scientists and ML engineers with fintech+fraud experience were scarce and costly.

Infoslab’s Talent-Led Solution

Infoslab onboarded a specialized AI pod within 8 days consisting of:

Fraud Data Scientists: Designed behavioral fingerprinting models combining anomaly detection with user profiling.

Real-time Streaming Engineers: Used Delta Live Tables + Apache Kafka to process events with sub-5-second latency.

ML Ops Experts: Integrated ML scoring APIs with the transaction system. Enabled real-time retraining and model monitoring.

Why Infoslab?

Latent Fintech Experience: Domain fluency led to faster iteration.

Regulatory Alignment: PCI DSS-compliant fraud models.

ML in Production: Fully owned MLOps layer ensured scalable and trustworthy deployment.

Get in Touch with Infoslab

Technical Highlights

• Built fraud feature store ingesting behavioral signals (IP, velocity, transaction size, geolocation drift).

• Scoring latency optimized to <5 seconds from 2+ minutes.

• Grafana dashboards for false positive rate, latency, and alert quality.

• A/B testing across models to continuously optimize fraud detection vs. user friction.

Delivery Highlights

• Reduced user dropouts caused by unnecessary transaction blocks.

• Decreased fraud analyst workload by 40%

• Enabled go-to-market for 2 new countries with risk coverage.