Business Dashboards & Apps

MII Credit Scoring & Fault Prediction

An interactive credit-risk dashboard with in-browser ML scoring.

At a glance

300–850Credit score range
ONNXIn-browser inference
ExplainablePer-factor score breakdown

01 / 05

Overview

A decision-support dashboard that brings credit risk models directly into the lending workflow.

Loan officers enter an applicant profile and get an instant creditworthiness score and default-probability estimate, each broken down into the factors that drove it. Gradient-boosted models are exported to ONNX and run in the browser, so scoring is immediate and no applicant data leaves the session. A model-performance view exposes accuracy, the confusion matrix, and feature importance so risk teams can see how the model behaves before trusting it.

Case study

01
The challenge
Credit assessment in smaller lending institutions leans on manual review and static scorecards. They are slow to update, hard to explain to an applicant or a regulator, and give risk teams little visibility into why a decision was made.
02
Our approach
Gradient-boosted models were trained on an applicant-profile feature set, then exported to ONNX so they run inside the browser alongside a transparent scoring fallback. The interface pairs every score with the factors that moved it, and a separate view reports model accuracy and feature importance.
03
The outcome
A working dashboard covering the lending decision path end to end: portfolio overview, applicant scoring, default prediction, and model performance, with Microsoft Entra sign-in wired for enterprise rollout.
04
Impact
Shows how credit risk models can sit inside the tools lending teams already use, with every score traceable to the factors behind it, giving risk and compliance a basis to review the model rather than take it on trust.