AI-Driven Fraud Detection (Anomalous Pattern Analysis)

[Cedarich]

Complexity Score: 200

Description

Analyze batches of claims to identify suspicious patterns (e.g., multiple claims from the same IP or identical evidence).

Requirements

• Implement a service to compare new claim metadata against historical data.
• Use scikit-learn to cluster claims by similarity.
• Flag "outliers" or clusters that exceed a risk threshold.
• Return a fraud_risk_score for each claim in a batch.

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[drips-wave]

This issue has been added to the Stellar Wave Program and is worth 200 Points.

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[Amas-01]

@Amas-01 has applied to work on this issue as part of the Stellar Wave Program's 3rd wave.

Hello Maintainer, I'll love to work on this Issue

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[Robinsonchiziterem]

@Robinsonchiziterem has applied to work on this issue as part of the Stellar Wave Program's 3rd wave.

I'm a backend developer with experience building AI-driven data processing services and implementing anomaly detection. I've worked with Python and libraries like scikit-learn to cluster data, identify outliers, and compute risk scores, while integrating such services into backend workflows for real-time or batch processing. I would love to work on this issue, my ETA is 1 hour.

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Congratulations, @Robinsonchiziterem! 🎉 Your application was accepted by the repo's maintainers, and the issue is due on March 30, 2026.

🧑‍💻 @Robinsonchiziterem: Please resolve the issue such that the repo's maintainers have enough time to review your contribution before the due date. You'll earn Points for completing the issue on-time, which will make you eligible for a share of the Stellar Wave Program's reward pool.

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