Kurniadi Ahmad Wijaya
Forward Deployed AI Engineer
Built production-grade AI and software for banking, fintech and enterprise.
Technical Consultant, Software & AI · Metrodata (PT Mitra Integrasi Informatika)
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Explainable Credit Scoring in the Browser

Browser-based gradient-boosted credit scoring that advises, while the credit officer decides.

Key results

  • In-browser

    Client-side scoring using ONNX Runtime Web

Role
Sole engineer
Period
May 2026 to Sep 2026
Client
Regional lender
Project type
Project
Room in the 3D HQ
L3:credit-scoring-poc

Results

  • In-browser

    Client-side scoring using ONNX Runtime Web

    PoC models were trained on synthetic data. No accuracy metric is claimed.

Problem

The credit team needed quick, explainable scoring advice while keeping applicant data out of server-side models.

Contribution

  1. I trained an XGBoost model, compared it with LightGBM and random forest, and exported it for ONNX Runtime Web.
  2. I combined model output with deterministic policy rules and manual-review flags. Scoring remains advisory; the credit officer decides.

Tech stack

  • LightGBM
  • ONNX Runtime Web
  • Next.js
  • XGBoost

Related career entryTechnical Consultant, Software & AI, Metrodata (PT Mitra Integrasi Informatika)

I am open to freelance or part-time work in software and AI engineering.

kurniadiahmadwijaya@gmail.com
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