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CASE STUDY

Tier-1 Bank: Predictive Analytics for Real-Time Fraud Detection

A tier-1 bank built real-time fraud-detection ML models scoring 4M transactions per hour. Fraud losses reduced 41%; false-positive rate dropped from 11:1 to 4:1.

May 9, 2026 · 2 min read · By admin
At a glance
41% CHANGE
Industry
BFSI
Topics
AI / ML · Cybersecurity · Predictive Analytics
Published
May 2026
Headline outcome
41% Fraud losses: down 41%

Measured outcomes

What this programme actually delivered.

41%

Fraud losses: down 41%

11

False-positive ratio: 11:1 → 4:1

67%

Customer experience: legitimate transactions unblocked 67% more often

-50

Sub-50ms scoring latency at peak

The challenge

A tier-1 bank’s fraud detection ran on a legacy rules engine with high false positives. Real fraud was missed; legitimate transactions were blocked.

What we built

Real-time ML scoring pipeline — gradient boosting models trained on 4 years of transaction history with feature engineering for behavioural patterns. Models score 4M transactions per hour with <50ms latency. Continuous retraining on outcomes.

Outcomes

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