MACHINE LEARNING FOR CREDIT ASSESSMENT IN MOBILE CREDIT AGGRE-GATORS

Abstract This paper investigates the application of machine learning (ML) techniques for credit assessment in mobile credit aggregators (MCA). Various ML algorithms such as logistic regression, decision trees, random forests, gradient boosting and neural networks are reviewed in terms of their applicability and accuracy in the context of mobile lending platforms. Real-world examples of the use of ML in such applications are analyzed. Challenges associated with the implementation of MO in modern MCA are discussed.