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🎉 Thanks for the PR, @Ayaanshaikh12243!We really appreciate you taking the time to contribute to ExpenseFlow! 💙 ⭐ Love this project?Please give us a star! It helps the project grow and reach more developers! 🌟 🔗 https://github.com/Renu-code123/ExpenseFlow ✅ PR ChecklistBefore we review, please ensure:
🙌 Thank You for Contributing!We truly appreciate your interest in contributing to this project.
We'll review your PR as soon as possible. Keep up the great work! ✨ |
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@Renu-code123 conflicts resolved please merge |
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@Renu-code123 resolved again i was expecting |
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@Renu-code123 i am watiing for you mam aap merge karke pls pls mujhe issus assing krna |
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close #847
Implemented Adaptive Risk Engine v2 to replace static login-risk thresholds with a reproducible, policy-driven scoring pipeline. It now combines weighted rule signals with a calibrated ML ensemble, supports per-tenant policy versions, and logs fully explainable decisions (top factors, policy/model/checksum, reproducibility key). Added drift monitoring to detect feature distribution shifts and classify model health as stable/watch/alert. Introduced rollback-safe policy lifecycle management (publish, history, rollback) and integrated all risk decisions into existing suspicious login flow and security event telemetry. This delivers deterministic, versioned, explainable risk outcomes with safer operations and tenant-specific control.