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CASE STUDY

Fintech: MLOps Platform for Real-Time Fraud Model Lifecycle

A fintech built an MLOps platform managing 14 fraud-detection models in production. Model deployment time: 6 weeks → 3 days; drift detection automated.

May 9, 2026 · 2 min read · By admin
At a glance
6 weeks BEFORE 3 days AFTER
Industry
BFSI
Topics
AI / ML · MLOps
Published
May 2026
Headline outcome
6 weeks Model deployment: 6 weeks →…

Measured outcomes

What this programme actually delivered.

6 weeks

Model deployment: 6 weeks → 3 days

24

Drift detection: automated; alerts within 24h of significant change

14

14 production models managed continuously

The challenge

A fintech’s fraud models drifted as fraud patterns evolved. Deployment of updated models took 6 weeks of manual coordination.

What we built

MLOps platform with model registry, automated deployment pipelines, real-time monitoring, drift detection, and explainability. Compliance-ready audit trail per model decision.

Outcomes

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