Machine learning for credit risk management

Credit risk management is a critical function for financial institutions, involving the assessment of borrowers' creditworthiness to minimize the risk of default. Traditional methods have been effective but often lack the predictive power and flexibility that modern machine learning (ML) models can provide. This thesis explores the potential improvements offered by ML models in credit risk prediction and examines the challenges and limitations associated with their implementation.

The primary goal of this thesis is to identify the most effective ML models for credit risk prediction and to understand the potential challenges and limitations in implementing these models. The research question guiding this study is: "What are the most effective machine learning models for credit risk prediction, and what are the potential challenges and limitations of implementing machine learning in credit risk management?"

To address this question, the study combines a comprehensive literature review with practical experiments. The literature review focuses on widely used ML models such as Logistic Regression (LR) and Random Forests (RF), as well as methods to enhance ML performance like SMOTE (Synthetic Minority Over-sampling Technique). The experimental phase involves training and evaluating these models on two datasets: the German Credit Risk dataset and the Indicators of Heart Disease dataset. The models' performance is measured using various metrics such as accuracy and F1-score.

The key findings from this study include:

• Effectiveness of ML Models: Logistic Regression, Bayesian models, and Random Forests are particularly effective for credit risk prediction. Among these, Random Forests demonstrated high accuracy with extensive tuning.
• Performance Across Datasets: For the German Credit Risk dataset, Logistic Regression outperformed Random Forest in terms of F1-score, while Multi-Layer Perceptron was the best overall model. For the Indicators of Heart Disease dataset, the Bayesian model excelled in F1-score.
• Impact of Techniques: Techniques like SMOTE had minimal effect on small, low-imbalance datasets, but showed significant benefits for larger, more imbalanced datasets.

Despite the benefits, several challenges were identified:

• Data Quality and Availability: Securing comprehensive and high-quality datasets is often difficult, impacting the effectiveness of ML models.
• Model Interpretability: More complex ML models, though powerful, are harder to interpret, making it challenging to explain the results to stakeholders.
• Computational Resources: Some models require significant computational resources and time for training and tuning.
• Regulatory Compliance: Ensuring that ML models meet regulatory standards and maintain transparency is critical in the financial industry.

This thesis highlights the necessity of balancing traditional and advanced ML models in credit risk management. It emphasizes the importance of thorough data preprocessing and model tuning to achieve optimal performance. The insights provided by this study are valuable for future research and practical applications in credit risk management, offering a roadmap for integrating ML models into existing credit risk assessment frameworks.