Credit Risk Optimization Model for Lending Decisions (Meta-Heuristic Approach)

This repository contains the code and resources for the research project focused on optimizing credit risk assessment using heuristic and meta-heuristic algorithms. This project was conducted under the guidance of Prof. Akhilesh Kumar from the Department of Industrial Engineering & Management.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Algorithms
• Results
• Acknowledgements

Introduction

The main objective of this project is to develop optimal heuristic algorithms and integrate meta-heuristic approaches, particularly evolutionary algorithms, to identify eligible customers for loan approval in a credit crunch environment. Credit crunch is a condition where bank is catered with limited amounts of funds and is demanded with hefty loan approvals at the same time. By leveraging these advanced techniques, the model aims to enhance the accuracy and efficiency of credit risk assessment.

Features

• Implementation of various heuristic algorithms for credit risk assessment.
• Integration of evolutionary algorithms and other meta-heuristic approaches.
• Experimental amalgamation and hybridization of distinctive algorithms to anticipate early convergence.
• Performance evaluation metrics and visualization tools.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/Credit-Risk-Optimization.git

2. Navigate to the project directory:

   cd Credit-Risk-Optimization

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To use the credit risk optimization model, follow these steps:

1. Prepare your dataset and place it in the data directory.
2. Run the main script to train the model and evaluate its performance:

   python main.py

3. View the results and performance metrics generated in the results directory.

Algorithms

The project utilizes the following algorithms:

• Genetic Algorithm (GA)
• Particle Swarm Optimization (PSO)
• Simulated Annealing (SA)
• Other heuristic and meta-heuristic methods

Contributing

Contributions are welcome! If you have any ideas or improvements, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Commit your changes:

   git commit -m 'Add some feature'

4. Push to the branch:

   git push origin feature-branch

5. Create a new Pull Request.

Acknowledgements

This project was conducted under the guidance and support of Prof. Akhilesh Kumar from the Department of Industrial Engineering & Management. Special thanks to all contributors and supporters of this research. Metawa, N., Hassan, MK., Elhoseny, M. (2017) Genetic algorithm based model for optimizing bank lending decisions [Journal: Expert Systems with Application Expert Systems With Applications 80 (2017) 75–82 J. Kennedy and R. Eberhart, "Particle swarm optimization," Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1948 vol.4, doi: 10.1109/ICNN.1995.488968.