💳 Heterogeneous Graph Learning with Causal Recourse for MSME Credit Risk 🧠📈
Heterogeneous-Graph-Learning-with-Causal-Recourse-for-MSME-Credit-Risk is a next-generation AI framework designed to assess and mitigate credit risk for Micro, Small, and Medium Enterprises (MSMEs) using heterogeneous graph neural networks (HGNNs) combined with causal inference and recourse learning.
This system models complex financial ecosystems — connecting firms, transactions, industries, and socio-economic features — to enable interpretable, fair, and actionable credit risk predictions for inclusive financial decision-making.
🚀 Project Overview
Traditional credit scoring models often fail to capture relational dependencies (supplier networks, trade links, regional factors) and lack transparency in recommendations. This project integrates heterogeneous graph representation learning with causal recourse to provide not only accurate credit risk predictions but also prescriptive guidance — explaining why an MSME faces risk and how it can improve its creditworthiness.
✨ Key Features
🕸️ Heterogeneous Graph Learning: Models MSMEs, banks, suppliers, invoices, and demographics as interconnected nodes.
🔍 Causal Recourse Engine: Generates actionable insights showing how MSMEs can shift from “high risk” → “low risk.”
📊 Interpretable Predictions: Integrates causal attributions for transparency and regulatory compliance.
🧠 Dynamic Risk Scoring: Continuously updates credit scores based on new transactions and relationships.
⚖️ Fairness-Aware Modeling: Incorporates debiasing layers to prevent discrimination based on geography or enterprise scale.
💡 End-to-End Credit Pipeline: From graph construction to recourse generation, all modules are automated and explainable.
🧩 Core Components Module Description Graph Construction Layer Builds heterogeneous graphs from MSME datasets — linking firms, loans, suppliers, and transactions. Heterogeneous GNN Backbone Implements Graph Attention Networks (HAN, HGT, R-GCN) for multi-typed relational learning. Causal Inference Module Estimates causal effects using SCMs (Structural Causal Models) and do-calculus on graph embeddings. Recourse Generator Suggests feature-level interventions to help MSMEs improve credit scores ethically and feasibly. Risk Scoring & Evaluation Aggregates node embeddings into interpretable, time-sensitive risk scores. 🧠 Research Motivation
MSMEs form the backbone of emerging economies, yet limited data and biased scoring methods restrict their credit access. This project empowers financial institutions to:
Model real-world interdependencies using graph-based learning
Apply causal reasoning for fairness and interpretability
Deliver recourse-oriented feedback to borrowers
🧰 Tech Stack
Languages: Python 🐍
Frameworks: PyTorch Geometric / DGL / PyTorch Lightning
Libraries: NetworkX, Scikit-learn, Pandas, NumPy, DoWhy, EconML
Visualization: Matplotlib, Seaborn, TensorBoard, Neo4j for graph insights
📁 Project Structure 📁 data/ # MSME financial, network, and transaction datasets 📁 graph_builder/ # Graph construction and preprocessing scripts 📁 models/ # GNN architectures (R-GCN, HAN, HGT) 📁 causal_module/ # Causal inference and recourse computation 📁 evaluation/ # Metrics for accuracy, fairness, and explainability 📁 visualization/ # Graph analytics and causal plots 📁 results/ # Experimental outputs and reports
⚙️ Getting Started git clone https://github.com/yourusername/Heterogeneous-Graph-Learning-with-Causal-Recourse-for-MSME-Credit-Risk.git cd Heterogeneous-Graph-Learning-with-Causal-Recourse-for-MSME-Credit-Risk pip install -r requirements.txt python train.py --model HGT --dataset msme_data.csv --recourse True
📊 Experimental Focus Objective Focus Metric Graph Learning Node classification & link prediction AUC / F1 Credit Risk Default prediction accuracy ROC-AUC ≥ 0.90 Causal Explanation Feature influence ACE / CATE Recourse Generation Action feasibility Counterfactual validity Fairness Group parity SPD < 0.05 🌍 Real-World Impact
💼 Empowers banks and microfinance institutions to make transparent, data-driven lending decisions.
🌱 Supports financial inclusion for underrepresented MSMEs through causal explainability.
🏦 Provides ethical recourse guidance so businesses understand how to improve eligibility.
📊 Enables governments and fintechs to evaluate systemic financial risks in emerging markets.
🧠 Research Contribution
Introduces a heterogeneous graph-causal framework for explainable credit scoring.
Proposes causal recourse generation to empower borrowers with actionable feedback.
Establishes fairness-aware graph embeddings to reduce institutional bias in financial AI.
🤝 Contributing
We welcome contributions from:
Graph ML researchers
Causal inference experts
FinTech data scientists
Policy and risk analysts