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Machine Learning for Credit Assessment in Mobile Credit Aggre-gators

Year: 2024

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13737342

Author: Ponomarev E.

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6911711577

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