Chain Forward Risk Viability Assessment — V3.0 Strategic Cut-Off & Viability Path

Model Iterations & Validation: Documenting the Shift from Pricing to Hard Filtering

📌 Project Overview

This repository documents the full analytical journey—from the initial V1.0 feasibility study to the validated V3.0 model deployment strategy. The core objective remains achieving portfolio viability, but the strategic path has evolved from marginal pricing adjustments to implementing a definitive, high-confidence strategic cut-off.

It includes all core project artifacts, model code, and documentation across iterations.

Folder Structure

Chain_Forward_Risk_Assessment/
├── 1_Presentation/
│ ├── Achieving Profitability, Risk Model V3.0 Findings & Strategic Cut-Off Recommendation.pptx
│ ├── Chain Forward Profitability Analysis, Strategic Path to Viability.pbix
│ └── Chain Forward Risk Assessment & Profitability Study.pptx
│
├── 2_Code_and_Data/
│ ├── data/
│ │ ├── Loan_Snapshot_Dataset.xlsx
│ │ └── simulated_msme_data.csv
│ │
│ ├── outputs/
│ │ ├── chart_feature_importance.csv
│ │ ├── chart_feature_importance_v3.csv
│ │ ├── chart_segment_performance.csv
│ │ ├── chart_segment_performance_v3.csv
│ │ ├── combined_features_v3.csv
│ │ ├── combined_risk_dataset_final_v4.csv
│ │ ├── sample_combined_v4.csv
│ │ ├── scenario_analysis_results.csv
│ │ ├── scenario_analysis_results_v3.csv
│ │ ├── segmentation_summary.csv
│ │ ├── segmentation_summary_v3.csv
│ │ └── models/
│ │ ├── risk_model_v2.pkl
│ │ └── scaler_v2.pkl
│ │
│ └── src/
│ ├── chain_forward_risk_model.py
│ ├── data_combination_pipeline.py
│ ├── data_combination_pipeline_v2.py
│ └── risk_modeling_pipeline_v2.0.py
│
├── 3_Documentation/
│ ├── Chain Forward Profitability Analysis, Strategic Path to Viability.pdf
│ ├── Model Governance and Strategic Roadmap.pdf
│ └── Risk Management & Monitoring Framework (V3.0 - Post-Deployment).pdf
│
├── 4_Monitoring_Dashboard/
│ └── governance_dashboard.html
│
├── Readme.md
└── run_full_pipeline.ps1

🎯 Core Finding (V3.0 Final)

The V3.0 model provides a high-confidence solution:

A Hard Cut-Off (Filtering) of the most loss-concentrated segment (Segment 0: CRITICAL RISK) is required to reduce the portfolio-wide default rate below the 3.75% break-even point and achieve immediate positive NPV.

📊 Key Financial Insights (Baseline)

🔍 Analytical Findings: Evolution from V1.0 to V3.0

V1.0 Findings (Initial Hypothesis & Pricing Strategy)

The initial modeling focused heavily on non-traditional, behavioral variables, finding the model's rejection power was weak (AUC < 0.6). This led to a strategy centered on marginal pricing adjustments.

🧭 V1.0 Methodology

V1.0 followed a structured four-pillar approach:

• Portfolio Viability Modeling
• Behavioral Risk Analytics
• K-Means Segmentation
• Logistic Regression for Default Prediction

🔑 V1.0 Key Analytical Findings

Loss concentration: Nearly half the borrowers belonged to a high-risk segment above the break-even threshold.

V3.0 Findings (Validated Strategy & Hard Filtering)

Refined feature engineering and model tuning yielded a high-confidence predictive model (AUC 0.8820), validating traditional credit signals and enabling a decisive cut-off strategy.

V3.0 Segmentation Summary

🎯 Strategic Recommendations (V3.0 Path to Viability)

The V3.0 strategy is a 3-part plan to immediately reduce portfolio risk and then optimize pricing and retention.

📌 Phase 1 — Immediate Strategic Filtering (The Primary Fix)

Objective: Achieve positive NPV by forcing the portfolio DR below 3.75%.

Financial Impact Scenario

📌 Phase 2 & 3 — Deployment and Future Modeling

• Deploy the V3.0 model and Segment 0 rejection rule into the lending platform
• Setup Early Warning Indicators
• Future exploration: XGBoost / LightGBM

🔄 Automated Execution Pipeline

The repository includes a PowerShell script:

run_full_pipeline.ps1

Runs the entire workflow end-to-end.

▶️ How to Run

.\run_full_pipeline.ps1

📂 Pipeline Flow

run_full_pipeline.ps1
│
├── STEP 1 → data_combination_pipeline.py
│       • Load data
│       • Clean & engineer features
│       • Export initial combined dataset
│
├── STEP 2 → data_combination_pipeline_v2.0.py
│       • Refine feature engineering
│       • Apply V3.0 target definition
│       • Export final combined dataset for modeling
│
├── STEP 3 → chain_forward_risk_model.py
│       • K-means segmentation
│       • Logistic regression modeling
│       • Profitability & NPV analysis
│       • Scenario stress-testing
│       • Output generation
│
└── STEP 4 → risk_modeling_pipeline_v2.0.py
        • Model validation and fine-tuning
        • Scenario and stress-test replication
        • Final output generation for monitoring

📊 Monitoring Dashboard (Post-Deployment)

File: 4_Monitoring_Dashboard/governance_dashboard.html

To view in browser (Python HTTP server method):

1. Serve the folder from terminal:

cd 4_Monitoring_Dashboard
python -m http.server 8000

2. Open your browser and navigate to:

http://localhost:8000/governance_dashboard.html

⚠️ Using forward slashes / in the URL is essential to avoid 404 errors from the server.

▶️ How to Use

1. Open the link in a web browser.
2. Navigate through tabs to review:
   • Portfolio segmentation and risk exposure
   • Feature importance and model explainability
   • Scenario stress-testing outcomes
3. Use this dashboard for executive reporting and governance purposes.

This allows non-technical stakeholders to monitor portfolio health and model performance without needing to run scripts.

📁 Repository Structure

🛠 Technical Stack