A Simple Credit Rating Prediction Model for FinTech Companies Using SMOTE and MRMR Techniques
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Abstract
FinTech companies have made the financial industry more efficient and have increased financial inclusion. However, it has also brought new risks to the financial system. Regulators, investors, and researchers are concerned that their financial difficulties could affect the financial system. Our study aims to delve deeper into the effectiveness of machine learning techniques in identifying early warnings of FinTech companies’ credit risk impairment. Using commonly employed accounting and market measures in the literature, we created various classifiers to predict FinTech credit ratings. Classification algorithms face a challenge when the number of observations between classes is not equivalent, affecting their performance. Due to the limited size of publicly traded FinTech stocks with an issuer-level credit rating, our database has few observations and is highly imbalanced. The results of our study show that the SMOTE oversampling technique improves the predictive power of machine learning algorithms and that feature selection algorithms such as MRMR allow the generation of less complex and easierto-understand models. Our results suggest that the KNN classification algorithm has higher accuracy in predicting FinTech’s credit ratings.
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