Abstract
Credit risk assessment has become a growing necessity in the banking sector. Data mining techniques need to be deployed, in order to enable lenders to produce an efficient and objective estimation of a customer’s creditworthiness. The purpose of this paper is to propose a methodology that performs a two-level data processing using Random Forest and Support Vector Machine, to accurately pinpoint creditworthiness of the clients involved. The random forest will be utilized to create an accurate credit scoring model which will be further refined using the support vector machine. The proposed methodology will help achieve results with minimized false positives.
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Roy, A.G., Urolagin, S. (2019). Credit Risk Assessment Using Decision Tree and Support Vector Machine Based Data Analytics. In: Mateev, M., Poutziouris, P. (eds) Creative Business and Social Innovations for a Sustainable Future. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-01662-3_10
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DOI: https://doi.org/10.1007/978-3-030-01662-3_10
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