/AiML-credit-risk---loan-optimization-ml

This project focuses on leveraging advanced data analytics and machine learning techniques to develop a comprehensive credit risk assessment, credit scoring, and loan optimization framework.

Primary LanguageJupyter NotebookMIT LicenseMIT

Integrated Credit Risk Modeling and Loan Optimization with Advanced Segmentation

This project focuses on leveraging advanced data analytics and machine learning techniques to develop a comprehensive credit risk assessment, credit scoring, and loan optimization framework. The system is designed to support a Buy-Now-Pay-Later (BNPL) service offered by a financial service provider in partnership with an eCommerce company. The primary objectives include customer segmentation, credit risk modeling, and loan optimization.

Project Objectives

1. Customer Segmentation

  • Objective: Conduct in-depth customer segmentation using RFMS (Recency, Frequency, Monetary Value, and Standard Deviation of Amount Spent) scores.
  • Purpose: Classify customers into high-risk and low-risk segments to tailor the BNPL or loan service offerings.

2. Credit Risk Modeling

  • Objective: Develop machine learning models to predict credit risk and default probabilities.
  • Outcome: Provide a risk probability for each customer, aiding in the assessment of creditworthiness and default risk.
  • Credit Score Model: Create a credit score model based on risk probabilities, aligned with FICO standards. creditscore

3. Loan Optimization Model

  • Objective: Develop a model to determine optimal loan amounts, repayment periods, and other terms.
  • Outcome: Offer personalized financing options to customers, enhancing the BNPL service's value proposition.

Methodology

The project integrates supervised and unsupervised machine learning techniques, including logistic regression, decision trees, random forests, and clustering algorithms. These models are trained and validated using historical BNPL data and external credit bureau information. The ultimate goal is to embed these models into the BNPL platform, improving credit decision-making, customer satisfaction, and business performance.

Table of Contents

  1. Data Collection and Preprocessing
  2. Exploratory Data Analysis (EDA)
  3. Feature Engineering
  4. Weight of Evidence (WoE) Binning
  5. Feature Selection
  6. Model Development
  7. Model Evaluation and Selection
  8. Model Deployment and Integration
  9. Monitoring and Continuous Improvement
  10. Installation
  11. Usage
  12. Contributing
  13. License
  14. Acknowledgments

Data Collection and Preprocessing

Gather and preprocess historical BNPL application and repayment data. This includes data cleaning, handling missing values, and normalization.

Exploratory Data Analysis (EDA)

Analyze customer characteristics, behaviors, and credit profiles to identify patterns influencing credit risk and repayment.

  • Notebook: EDA

Feature Engineering

Create new features, including RFMS scores, based on insights from EDA to enhance the models' predictive power.

Customer Segmentation

Constructing a default estimator (proxy) By visualizing all transactions in the RFMS space to establish a boundary Where customers are classified as high and low RFMS scores.

  • Visualizing Transactions in RFMS space & Establishing boundaries.

rfms

Weight of Evidence (WoE) Binning

Apply WoE binning to transform features into a suitable format for machine learning models.

Feature Selection

Select the most relevant features using techniques like correlation analysis and recursive feature elimination.

  • Correlation Analysis

cor

  • Selected Features

features

Model Development

Develop various machine learning models to predict credit risk, score, and loan optimization metrics.

Model 1 ( GradientBoosting ) : Predictive Credit Risk probability estimator Model.

  • Model Evaluation model1

Model 2 ( Linear Regression ) : Credit Score (from risk probability estimates) Model.

Model Deployment and Integration

Deploy the selected models into the BNPL platform to enhance decision-making processes.

Monitoring and Continuous Improvement

Continuously monitor and refine the models to ensure they maintain high accuracy and effectiveness over time.

Installation

Prerequisites

  • Python 3.x
  • Virtual environment (e.g., virtualenv, conda)

Steps

  1. Clone the repository:

    git clone https://github.com/Daniel-Andarge/AiML-credit-risk---loan-optimization-ml.git

  2. Navigate to the project directory:

    cd AiML-credit-risk---loan-optimization-ml

  3. Create and activate a virtual environment:

    # Using virtualenv
virtualenv venv
source venv/bin/activate

# Using conda
conda create -n your-env python=3.x
conda activate your-env

  4. Install the required dependencies:

    pip install -r requirements.txt

Usage

Open the Jupyter notebooks in your preferred environment and follow the instructions. Customize the code based on your dataset and requirements.

Contributing

While this project is primarily a portfolio piece and not open for external contributions, feedback and suggestions are always welcome. If you have any thoughts or comments, reach out via email or LinkedIn.

License

This project is licensed under the MIT License.

Contact

Author

👤 Daniel Andarge