RISK-SCORING-FOR-LOAN-APPROVAL.MD
FINANCE + EXPLAINABLE ML
Risk Scoring for Loan Approval
An explainable ML workflow for loan approval that combines structured preprocessing, risk scoring, and business-facing decision support.
Overview
This project focuses on building an explainable risk scoring workflow for loan approval. The goal was not only to classify outcomes accurately, but also to provide a process that is understandable enough for financial decision support.
Problem
Loan decisions require more than raw prediction accuracy. In practical settings, decision-makers need transparent reasoning, interpretable features, and a workflow that supports business confidence rather than opaque model output.
Role
I built the workflow around preprocessing, feature handling, model evaluation, and explainability. I focused on both technical performance and decision usefulness.
Implementation
- Built SQL-backed preprocessing and structured feature pipelines for model-ready data preparation.
- Used SMOTE to address class imbalance and support more stable learning behavior.
- Trained and evaluated a logistic regression-based scoring workflow for loan approval prediction.
- Used SHAP and ROC-based evaluation to understand feature influence and model behavior.
Outcomes
- Improved model performance by 13% over baseline.
- Reached 86% accuracy in the final workflow.
- Delivered a more explainable and business-ready decision-support system.