Strategic Predictions and Explanations By Machine Learning : The Prediction Model of Credit Default Swaps for the Telecommunication Service Sector

Many machine learning (ML) models can make predictions regarding credit default swaps (CDS) for the telecommunication (telco) service sector. However, some ML algorithms can only offer a black-box model. It is crucial to explain the prediction result for strategic decisions. We compare various the state of arts, including deep learning (transformers), gradient boost machine (GBM), and Xgboost, plus different explainable tools: Variable Importance (VI) Partial Dependent Plots (PDP), Local Individual Conditional Expectation (LIME), Interpretable Model-agnostic Explanations (ICE), and Shapley values for the prediction model. Moreover, we also conducted a hyperparameter search of the prediction model by leveraging high-performance computing (HPC). Our experiment results show that the Xgboost provides the best solution with fewer constraints. We aim to find an optimal solution for strategic CDS investment decisions.