AmirhosseinHonardoust

AmirhosseinHonardoust / Underwriting-Decision-Safety-Lab

Public

A decision-safety lab for loan approval: trains a baseline classifier, calibrates probabilities (ECE/Brier), sweeps confidence thresholds to build a coverage, quality frontier and outputs a defensible abstention policy (auto-decide vs review). Includes a Streamlit dashboard for report cards, triage UI, and data quality checks.

abstention calibration classification credit-risk data-quality
11
0
100% credibility
Found Feb 23, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A prototype dashboard and analysis tool for creating defensible loan approval policies using calibrated predictions, abstention for uncertain cases, and interactive triage.

How It Works

1
🔍 Discover the lab

You find this helpful tool on GitHub that helps make safer decisions for loan approvals by sorting cases into auto-approve, auto-reject, or review.

2
📥 Gather your data

Get a simple spreadsheet of past loan applicants, like their age, income, credit score, and whether loans were approved.

3
🚀 Run the analysis

Click a button to let the tool study your data, learn patterns, and create trustworthy confidence scores for decisions.

4
📱 Open the dashboard

Launch the easy-to-use screens that show charts, scores, and insights in a friendly web view.

5
📊 Review the report card

Check simple metrics like how often it's right, how confident it is, and the best balance of speed versus safety.

6
🧑‍💼 Test new applicants

Enter details for a new loan seeker and instantly see the predicted chance of approval plus the safe action: auto-decide or send for review.

🎉 Safer decisions unlocked

You now have clear rules, charts, and a tester to defend quick approvals, reduce reviews, and avoid risky loans confidently.

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AI-Generated Review

What is Underwriting-Decision-Safety-Lab?

This Python project builds a decision-safety lab for loan approval systems: it trains a baseline classifier, calibrates probabilities using ECE and Brier scores, and sweeps confidence thresholds to build a coverage-quality frontier for abstention policies like auto-decide or review. You get JSON outputs for metrics, policies, and predictions, plus PNG figures for confusion matrices and reliability diagrams. A Streamlit dashboard delivers report cards, coverage curves, a triage UI for applicant inputs, data quality checks, and notes—all from one CSV input via a simple pipeline command.

Why is it gaining traction?

It stands out by turning raw model scores into defensible actions for high-stakes approval workflows, with interactive tools to explore confidence tradeoffs and expected coverage. Developers love the end-to-end flow: run the pipeline, tweak calibration or target coverage in the sidebar, and instantly see policy recommendations plus a live triage demo. No more stopping at AUC—here you get a full decision frontier and UI ready for stakeholder demos.

Who should use this?

Fintech ML engineers prototyping loan underwriting models, data scientists at banks tuning abstention for risk-averse auto-decide rules, or compliance teams validating calibrated classifiers against ECE/Brier. Ideal for teams handling imbalanced approval data who need quick data checks and coverage dashboards before production.

Verdict

Grab it if you're exploring decision-safety in lending—solid docs, Streamlit polish, and practical outputs make it a strong prototype despite 10 stars and 1.0% credibility score. Not production-ready yet (research status), but extend it for two-sided policies or fairness audits to ship faster.

(198 words)

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