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Financial Risk Predictor

A high-performance financial risk prediction system using ensemble methods (LightGBM/XGBoost) with SHAP-based interpretability, deployed as a scalable REST API via BentoML.

Halftone photograph: an open ledger with ruled columns, a pen resting on the page.
Financial Risk Predictor — the ledger, file photo.
122a2b34567Fig.1.Fig.2.section of the driving hub

Reference

  1. 1.HOPPER — Kafka — daily market signals
  2. 2.MILLWHEEL — LightGBM / XGBoost
  3. 3.STAMP — BentoML — the serving hatch
  4. 4.LOUPE — SHAP — not this belt
  5. 5.BELT — the daily retrain
  6. 6.GAUGE — 0.87 AUC-ROC
  7. 7.LEDGER — the risk score†

† composed from the archives

APPARATUS FOR AN UNDERWRITING ENGINE. Filed Jul. 2025.

122a2b34567Fig.1.Fig.2.section of the driving hub

Tech

  • TensorFlow
  • XGBoost
  • LightGBM
  • BentoML
  • SHAP

The line

  1. 01

    Engineered a high-performance financial risk prediction system using LightGBM/XGBoost, achieving 0.87 AUC-ROC (15% improvement over baseline).

  2. 02

    Utilized SHAP values to interpret model decisions and identify key risk drivers, enhancing stakeholder trust and model transparency.

  3. 03

    Packaged and deployed the model to BentoCloud using BentoML, creating a scalable REST API and optimizing inference latency by 30%.

  4. 04

    Orchestrated a continuous-learning data pipeline to retrain models daily on streaming market signals from KafKa, ensuring proactive risk adaptation.

Measurable impact

0.87 AUC-ROC score

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