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ML-Based Customer Risk Analysis for Banking: Achieving 80% Recall on Imbalanced Data

Banking Risk Analysis

Project Overview

This project focuses on customer risk prediction in a banking environment using machine learning models. The primary challenge was dealing with imbalanced data where the minority class represents customers with a higher risk of default. Our goal was to achieve high recall, specifically targeting 80%, while maintaining acceptable levels of precision and accuracy.

Objectives

  • Goal: Achieve 80% recall on high-risk customers.
  • Dataset: Imbalanced banking dataset.
  • Models Used: CART, Random Forest (RF), Gradient Boosting Machine (GBM), LightGBM, and BalancedRandomClassifier.
  • Evaluation Metrics: Accuracy, Precision, Recall, F1-Score.

Approach

  1. Data Preprocessing:

    • Handled missing data.
    • Performed feature scaling and encoding for categorical variables.
    • Addressed the class imbalance using specialized techniques.

  2. Imbalanced Data Handling:

    • Implemented RandomUnderSampler to reduce majority class size.
    • Used TomekLinks to remove overlapping data points and further refine the dataset.

  3. Modeling:

    • Tried several machine learning models: CART, RF, GBM, and LightGBM.
    • The most effective model for handling imbalance was BalancedRandomClassifier.

  4. Hyperparameter Optimization:

    • Applied hyperparameter tuning to the BalancedRandomClassifier using grid search to optimize performance.

  5. Model Evaluation:

    • The BalancedRandomClassifier provided the best results.

Dataset

The dataset used in this project can be downloaded from Kaggle:

Credit Card Approval Prediction Dataset

  • Customer Demographics: Age, gender, occupation, etc.
  • Financial Indicators: Credit history, balance, transaction patterns.
  • Target Variable: Customer risk level.

For privacy reasons, the dataset is not included in this repository.

Results

  • Best Model: BalancedRandomClassifier
  • Final Performance Metrics:
    • Accuracy: 74%
    • Precision: 72%
    • Recall: 80%
    • F1-Score: 76%

How to Run

Prerequisites

Install the necessary dependencies by running:

pip install -r requirements.txt

Running the Code

  1. Clone the repository:
git clone https://github.com/aysecnkci/banking-risk-analysis-imbalanced-data.git
  1. Run the Jupyter notebook to preprocess the data, train the model, and evaluate it:
jupyter notebook risk_analysis_banking_imbalanced.ipynb

Repository Structure

├── README.md
├── requirements.txt
├── notebooks/
│   └── risk_analysis_banking_imbalanced.ipynb

Future Work

  • Experiment with deep learning models to improve recall.
  • Further tune hyperparameters to explore better performance.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Thanks to the contributors and the machine learning community for resources and support.