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Fraud Detection

Production Fraud Detection System

A real-time fraud detection framework in production — catching fraud at the point of transaction, with investigators working from one queue.

PythonAzure MLFabric RTIPower BI
Fraud Detection illustration

The challenge

Fraud was being caught after the fact by overnight batch jobs, and investigators were working from a mix of spreadsheets and emails instead of one defensible queue.

Our approach

How we structured the work, end to end.

01
Streamed transactions into Fabric Real-Time Intelligence so detection could happen at sub-second latency.
02
Built the detection engine in Python and Azure ML — pattern-based rules combined with ML scoring per transaction.
03
Surfaced every flagged transaction on a single investigator dashboard with full context and audit trail.
04
Closed the loop: investigator decisions became labelled training data feeding the next model retrain.

The architecture

From source to insight, in one governed flow.

01
Transaction Stream
02
Detection Engine
03
Real-Time Score
04
Risk Dashboard

Outcome

What changed
Fraud is now caught at the point of transaction. Investigators work from one trustworthy queue and the platform learns from every decision they make.

Related work

Other engagements with a similar shape.

Process × Intelligence

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