Credit Score Analysis Using Machine Learning

Financial risk management is increasingly focusing on credit risk analysis. The difficulty in this situation is to find insights into the performance of the finance sector using machine learning algorithms because those insights have a direct impact on the sector’s viability. For the evaluation of the credit risk of the customer dataset, numerous credit risk analysis techniques are employed. In particular, using private datasets, we suggest a novel classification methodology for ML-driven credit risk algorithms and their performance ranking. In order to predict the likelihood of a loan default, we create binary classifiers based on machine learning models using actual data. The 10 most relevant features from each of these models are chosen after conducting a performance analysis on the given data set. These features are then utilized in the modeling process to ensure that binary classifiers are stable.