Credit Scoring & Loan Automation: AI for Smarter Lending Decisions
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Credit Scoring and Lending Systems
Credit lifecycle: application, scoring, approval, repayment
Traditional vs. AI-powered scoring models
Use cases in banks, neobanks, and microlenders
Module 2: Financial Data for Credit Risk
Credit reports, income, spending, KYC/AML data
Behavioral and alternative data: mobile payments, utility bills, social signals
Dealing with missing, skewed, or biased datasets
Module 3: Machine Learning Models for Credit Scoring
Logistic regression, decision trees, ensemble models (XGBoost, LightGBM)
Probability calibration and scorecard generation
Reject inference and bias mitigation strategies
Module 4: Loan Approval Workflow Automation
Rule engines and AI-powered underwriting
Document classification and OCR for automation
Real-time decisioning APIs and smart dashboards
Module 5: Compliance, Fairness & Explainability
Ensuring fairness (equal opportunity, disparate impact analysis)
Legal frameworks: GDPR, ECOA, FCRA
Model explainability with SHAP, LIME, and scorecard visualization
Module 6: Capstone Project – Build a Credit Scoring Model
Use real or synthetic borrower data
Train a model, calibrate scores, and simulate an approval workflow
Submit full code, model dashboard, and compliance documentation
Tools & Technologies Used:
Python
Scikit-learn, XGBoost, LightGBM
SHAP, LIME (for interpretability)
Streamlit or Flask for workflow automation interface
Target Audience:
Fintech professionals and data scientists
Credit risk analysts and loan officers
Developers building lending platforms or scoring engines
Students entering financial machine learning roles
Global Learning Benefits:
Design fair, accurate, and automated lending systems
Improve access to credit with alternative data and AI
Comply with regulations while leveraging advanced analytics
Create end-to-end solutions for real-time credit decisioning
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