🏦 Credit Risk Intelligence Engine

An end-to-end credit default prediction system with explainable AI, demographic fairness auditing, and regulatory-compliant bias mitigation — built on 150,000+ real borrower profiles.

Overview • Architecture • Results • Fairness • Setup • Usage

📌 Overview

The Credit Risk Intelligence Engine is a production-grade machine learning pipeline that goes beyond standard classification — it is designed to answer the hard questions that financial institutions actually face:

Can we identify high-risk applicants reliably and ensure that our model does not systematically disadvantage borrowers based on demographic characteristics?

This project combines gradient boosting, statistical feature analysis, multi-layer explainability (SHAP + LIME), and IBM AIF360 fairness constraints into a single, audit-ready system.

Business Questions Addressed

#	Question
1	Which borrower characteristics are the strongest predictors of default?
2	How do we build a model that catches defaults while minimizing false rejections?
3	Can the model provide specific, auditable reasons for each credit decision?
4	Does the model treat all age demographics equitably under the EEOC 80% rule?
5	Can fairness gaps be closed without sacrificing predictive performance?

🧱 System Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                    CREDIT RISK INTELLIGENCE ENGINE                  │
├──────────────┬──────────────┬──────────────┬────────────────────────┤
│   DATA LAYER │   ML LAYER   │  EXPLAIN.    │    FAIRNESS LAYER      │
│              │              │  LAYER       │                        │
│  Raw CSV     │  Logistic    │  SHAP Tree   │  AIF360 Reweighing     │
│  EDA         │  Regression  │  Explainer   │  (Pre-processing)      │
│  Stat Tests  │  (Baseline)  │              │                        │
│  Feature     │              │  LIME        │  Disparate Impact      │
│  Engineering │  Random      │  Tabular     │  Analysis              │
│              │  Forest      │  Explainer   │                        │
│  Imputation  │              │              │  Equal Opportunity     │
│  Outlier     │  XGBoost     │  Global +    │  Diff (EOD)            │
│  Capping     │  (Champion)  │  Local Scope │                        │
│              │              │              │  Threshold             │
│  StandardSc. │  Early       │  Per-case    │  Optimization          │
│              │  Stopping    │  explanations│  (Post-processing)     │
└──────────────┴──────────────┴──────────────┴────────────────────────┘

📂 Dataset

Source: Give Me Some Credit — Kaggle

Attribute	Value
Total Records	~150,000 borrowers
Features	11 raw + 5 engineered
Target Variable	`SeriousDlqin2yrs` (90+ day delinquency)
Class Imbalance	~6.7% default rate (14:1 ratio)
Missing Data	`MonthlyIncome` (~19%), `NumberOfDependents` (~2.5%)

Feature Dictionary

Column	Engineered Name	Description
`SeriousDlqin2yrs`	Target	90+ day delinquency within 2 years
`RevolvingUtilizationOfUnsecuredLines`	Credit Usage %	Proportion of revolving credit in use
`age`	Age	Borrower age in years
`NumberOfTime30-59DaysPastDueNotWorse`	1-Month Lates	Count of 30–59 day delinquencies
`DebtRatio`	Debt vs Income	Monthly obligations / monthly income
`MonthlyIncome`	Monthly Income	Gross monthly income
`NumberOfOpenCreditLinesAndLoans`	Open Accounts	Active credit lines + loans
`NumberOfTimes90DaysLate`	3-Month Lates	Count of 90+ day delinquencies
`NumberRealEstateLoansOrLines`	Mortgages	Real estate credit lines
`NumberOfTime60-89DaysPastDueNotWorse`	2-Month Lates	Count of 60–89 day delinquencies
`NumberOfDependents`	Family Size	Number of dependents

Engineered Features

Feature	Logic	Rationale
`TotalPastDue`	Sum of all 30/60/90-day lates	Single delinquency severity signal
`CreditHistoryLength`	`(age - 18).clip(0)`	Proxy for years in credit system
`MonthlyPayment`	`DebtRatio × MonthlyIncome`	Actual cash-flow burden
`IncomePerPerson`	`MonthlyIncome / (Dependents + 1)`	Effective disposable income
`AgeGroup`	Binned: Young/MiddleAge/Senior/Elderly	Protected attribute for fairness audit

🔬 Statistical Feature Analysis

Before modeling, every feature was validated using the Mann-Whitney U Test + Cohen's d effect size across the default/non-default split:

Tier	Features	Cohen's d	Business Meaning
Power Trio	`TotalPastDue`, `NumberOfTimes90DaysLate`, `RevolvingUtilization`	> 1.0	Primary behavioral risk signals
Stability	`Age`, `CreditHistoryLength`	0.2–0.5	Protective maturity factors
Secondary	`MonthlyIncome`, `DebtRatio`, `NumberOfDependents`	< 0.2	Supporting context features

Verdict: The extreme Cohen's d of the Power Trio features confirmed that a tree-based, split-optimizing model (XGBoost) would be the ideal architecture.

🤖 Model Development

Three models were trained and compared in a rigorous pipeline:

1. Logistic Regression (Baseline)

class_weight='balanced' to address 14:1 imbalance
SAGA solver for large-scale convergence
Purpose: interpretable linear baseline + recall ceiling benchmark

2. Random Forest (Ensemble Benchmark)

200 estimators, max_depth=10
Non-linear interaction capture
Bridge between linear and boosting paradigms

3. XGBoost (Champion Model)

n_estimators=1000 with early_stopping_rounds=50
scale_pos_weight tuned to exact class ratio (~14.0)
learning_rate=0.05, subsample=0.8, colsample_bytree=0.8
Early stopping on AUC — training halts automatically at optimal generalization

xgb_model = XGBClassifier(
    n_estimators=1000,
    max_depth=6,
    learning_rate=0.05,
    subsample=0.8,
    colsample_bytree=0.8,
    scale_pos_weight=ratio,      # ~14:1 imbalance correction
    objective='binary:logistic',
    eval_metric='auc',
    early_stopping_rounds=50
)

📈 Results

Model Comparison

Metric	Logistic Regression	Random Forest	XGBoost
Accuracy	—	—	~86%
ROC-AUC	Lower	Moderate	0.8651
Precision	Low	Higher	Highest
Recall	Highest	Moderate	~79%
F1-Score	Lower	Moderate	Highest

XGBoost selected as production model: highest ROC-AUC, best F1-Score, and most robust handling of class imbalance.

Confusion Matrix Breakdown (XGBoost)

                Predicted: No Default    Predicted: Default
Actual: No Default     22,453               5,540
Actual: Default           430               1,575

Business Metric	Value
Default Catch Rate (Recall)	78.55%
Safe Customer Clearance Rate	80.21%
Missed Defaulters	~430 (~21% of actual defaults)
AUC — Discrimination Power	0.8651
Average Precision Score	0.3976 (~6× better than random)

The model is risk-averse by design: it errs toward flagging borderline cases, since the cost of a missed default far exceeds the cost of a rejected safe applicant.

🔍 Explainability

Global Explainability — SHAP (SHapley Additive exPlanations)

SHAP TreeExplainer was applied to a stratified 1,000-sample test subset, producing a ranked, directional view of global feature influence:

Rank	Feature	Direction	Interpretation
1	`TotalPastDue`	↑ with value	Strongest default signal — any delinquency history sharply raises risk
2	`RevolvingUtilizationOfUnsecuredLines`	↑ with value	Credit strain above ~70% is a heavy penalty
3	`Age`	↓ with age	Youth = higher risk; maturity acts as a protective factor
4	`MonthlyIncome`	↓ with income	Higher income modestly reduces risk
5	`DebtRatio`	Mixed	Meaningful only above extreme thresholds

Local Explainability — LIME (Individual Cases)

For the highest-risk case identified in the test set (predicted default probability: 97.2%), LIME decomposed the prediction:

Feature                         Contribution
─────────────────────────────────────────────
TotalPastDue        (7.84)    → +0.33 risk
RevolvingUtilization(2.04)    → +0.29 risk
Age                 (-1.31)   → +0.07 risk  (young borrower)
CreditHistoryLength (-1.31)   → +0.06 risk  (short history)

Regulatory Value: LIME explanations provide individualized, auditable reasons for each credit decision — a direct requirement under GDPR Article 22 and similar frameworks.

⚖️ Fairness & Bias Mitigation

This is the most technically sophisticated component of the project. The fairness pipeline uses Age Group as the protected attribute and evaluates compliance with the EEOC 80% (Four-Fifths) Rule.

Phase 1 — Baseline Fairness Audit

Metric	Value
Senior/Elderly Approval Rate	Higher
Young/Middle-Age Approval Rate	Lower
Disparate Impact Ratio (Baseline)	< 0.80 → ⚠️ BIAS DETECTED
Root Cause	Proxy discrimination via `TotalPastDue` + `RevolvingUtilization` (both correlated with age)

Phase 2 — AIF360 Reweighing (Pre-processing Mitigation)

RW = Reweighing(
    unprivileged_groups=[{'privileged': 0.0}],
    privileged_groups=[{'privileged': 1.0}]
)
dataset_transf = RW.fit_transform(dataset_train)
instance_weights = dataset_transf.instance_weights

xgb_fair.fit(X_train_scaled, y_train, sample_weight=instance_weights)

Reweighing assigns corrective importance weights to training samples — upweighting under-represented fair cases and downweighting over-represented ones — so the model learns a naturally equitable decision boundary without modifying features or labels.

Phase 3 — Optimal Threshold Search (Post-processing)

A high-to-low threshold scan (0.99 → 0.01, 500 steps) identified the tightest threshold that simultaneously:

Satisfies DI ≥ 0.80 (EEOC compliance), and
Maximizes F1-Score (operational utility)

for t in np.linspace(0.99, 0.01, 500):
    preds = (xgb_fair_proba >= t).astype(int)
    cur_di = sel_unprivileged / sel_privileged
    if 0.80 <= cur_di <= 1.25:
        if f1_score(y_test, preds) > best_f1:
            final_thresh = t

Fairness Results Summary

Metric	Baseline	After Mitigation	Change
Disparate Impact Ratio	~0.796	≥ 0.80	✅ Compliant
Equal Opportunity Diff	Higher	Lower	Improved
ROC-AUC	0.8651	~0.865	Preserved
Accuracy Impact	—	< 1%	Negligible

Key Finding: Fairness and predictive power are NOT mutually exclusive. The combined Reweighing + Best-F1 Threshold strategy achieves regulatory compliance while maintaining the full discriminative capacity of the original XGBoost model.

🛠️ Setup

Requirements

Python >= 3.9

Installation

git clone https://github.com/your-username/credit-risk-intelligence-engine.git
cd credit-risk-intelligence-engine
pip install -r requirements.txt

Core Dependencies

numpy>=1.23
pandas>=1.5
scikit-learn>=1.2
xgboost>=1.7
shap>=0.42
lime>=0.2
aif360>=0.5
matplotlib>=3.6
seaborn>=0.12
scipy>=1.10

Dataset

Download cs-training.csv from Kaggle — Give Me Some Credit and place it in the data/ directory.

🚀 Usage

Run the Full Pipeline

Open credit_risk_intelligence_engine_v2.ipynb in Jupyter or Google Colab and run all cells sequentially. The notebook is self-contained and will install missing dependencies automatically.

Load Saved Models for Inference

import pickle, json
import pandas as pd

# Load artifacts
with open('artifacts/xgboost_fair_model.pkl', 'rb') as f:
    model = pickle.load(f)

with open('artifacts/feature_scaler.pkl', 'rb') as f:
    scaler = pickle.load(f)

with open('artifacts/fairness_thresholds.json') as f:
    config = json.load(f)

with open('artifacts/feature_columns.json') as f:
    features = json.load(f)

# Predict on new applicant
applicant = pd.DataFrame([{
    'RevolvingUtilizationOfUnsecuredLines': 0.85,
    'age': 32,
    'DebtRatio': 0.45,
    'MonthlyIncome': 4500,
    'NumberOfOpenCreditLinesAndLoans': 7,
    'NumberRealEstateLoansOrLines': 1,
    'NumberOfDependents': 2,
    'CreditHistoryLength': 14,
    'TotalPastDue': 1
}])

applicant_scaled = scaler.transform(applicant[features])
risk_score = model.predict_proba(applicant_scaled)[0, 1]
threshold = config['global_fair_threshold']
decision = 'DEFAULT RISK' if risk_score >= threshold else 'LOW RISK'

print(f"Risk Score: {risk_score:.2%} → {decision}")

🏗️ Pipeline Walkthrough

1. DATA LOADING & EDA
   └─ Load cs-training.csv → shape inspection → missing value audit → class distribution

2. FEATURE ENGINEERING
   ├─ Error code correction (96/98 → 0 in delinquency columns)
   ├─ TotalPastDue aggregation
   ├─ MonthlyPayment = DebtRatio × MonthlyIncome
   ├─ IncomePerPerson = MonthlyIncome / (Dependents + 1)
   └─ AgeGroup binning (protected attribute)

3. STATISTICAL VALIDATION
   └─ Mann-Whitney U + Cohen's d → feature power ranking

4. MODEL TRAINING
   ├─ Logistic Regression (baseline)
   ├─ Random Forest (benchmark)
   └─ XGBoost (champion, early stopping + scale_pos_weight)

5. MODEL EVALUATION
   ├─ ROC-AUC, Precision, Recall, F1, Confusion Matrix
   ├─ ROC Curve + Precision-Recall Curve
   └─ Threshold analysis

6. EXPLAINABILITY
   ├─ SHAP TreeExplainer → global beeswarm plot
   └─ LIME → individual case breakdown

7. FAIRNESS AUDITING
   ├─ Baseline DI + EOD calculation (AIF360)
   ├─ AIF360 Reweighing (pre-processing)
   ├─ Re-training with instance weights
   ├─ Optimal threshold search (high→low scan)
   └─ Granular per-group audit table

8. ARTIFACT EXPORT
   └─ .pkl models + .json configs + .csv reports

📊 Key Technical Decisions

Decision	Approach	Why
Class imbalance	`scale_pos_weight` (XGBoost) + `class_weight='balanced'` (LR, RF)	Avoids majority-class collapse without SMOTE artifacts
Outlier handling	Clip RevolvingUtilization at 2.0, late-counts at 20	Preserves over-extension signal without extreme skew
Feature scaling	StandardScaler on XGBoost	Required for LIME and fair model convergence
Bias mitigation	Pre-processing (Reweighing) + Post-processing (threshold)	Two-layer defense; neither alone is sufficient
Threshold strategy	High-to-low scan for tightest DI-compliant F1	Avoids the degenerate "approve everyone" solution
Explainability	SHAP (global) + LIME (local)	Different stakeholders need different explanation granularity

🔮 Future Roadmap

Streamlit Dashboard — real-time loan officer interface with per-applicant SHAP waterfall charts
Model Drift Monitoring — PSI-based feature distribution tracking for production deployment
Calibration Layer — Platt scaling / isotonic regression for well-calibrated probability outputs
A/B Testing Framework — controlled threshold experimentation with statistical significance testing
Intersectional Fairness — multi-attribute analysis (age × income group)
API Deployment — FastAPI wrapper with model versioning and audit logging

📚 References & Methodology

Kaggle Competition: Give Me Some Credit
Explainability: SHAP — Lundberg & Lee, 2017
Local Explanations: LIME — Ribeiro et al., 2016
Fairness Toolkit: IBM AIF360
Fairness Criterion: EEOC Uniform Guidelines — 80% Rule
Gradient Boosting: XGBoost — Chen & Guestrin, 2016

📄 License

This project is licensed under the MIT License. See LICENSE for details.

Built with a commitment to both accuracy and equity in automated decision-making.

If this project helped you, consider starring the repo ⭐

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🏦 Credit Risk Intelligence Engine

📌 Overview

Business Questions Addressed

🧱 System Architecture

📂 Dataset

Feature Dictionary

Engineered Features

🔬 Statistical Feature Analysis

🤖 Model Development

1. Logistic Regression (Baseline)

2. Random Forest (Ensemble Benchmark)

3. XGBoost (Champion Model)

📈 Results

Model Comparison

Confusion Matrix Breakdown (XGBoost)

🔍 Explainability

Global Explainability — SHAP (SHapley Additive exPlanations)

Local Explainability — LIME (Individual Cases)

⚖️ Fairness & Bias Mitigation

Phase 1 — Baseline Fairness Audit

Phase 2 — AIF360 Reweighing (Pre-processing Mitigation)

Phase 3 — Optimal Threshold Search (Post-processing)

Fairness Results Summary

🛠️ Setup

Requirements

Installation

Core Dependencies

Dataset

🚀 Usage

Run the Full Pipeline

Load Saved Models for Inference

🏗️ Pipeline Walkthrough

📊 Key Technical Decisions

🔮 Future Roadmap

📚 References & Methodology

📄 License

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