Abstract
Machine learning is a broad field that encompasses a wide range of techniques and algorithms that can be used to perform a wide variety of tasks. The selection of an appropriate algorithm to be used in a particular application can be challenging due to the complexity of the various techniques that are available as well as the high cost of implementing and debugging sophisticated models. In this paper, we examine the use of multiple machine learning algorithms on an Australian dataset that consists of a collection of loan applications from prospective borrowers with differing credit scores. Our goal is to provide comprehensive information about the performance of these models in order to assist financial firms in selecting the most effective model for their needs. To accomplish this goal, we compare the performance of the various models on the classification task and identify the most accurate and effective model based on the overall obtained performance. Our results suggest that XGBoost Classifier, Bagging Classifier, and Support Vector Machine are among the most effective models that can be used for this task based on their superior accuracy when compared to other machine learning algorithms.
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This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (Alkhawarizmi/2020/01).
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Akil, S., Sekkate, S., Adib, A. (2024). A Synthesis on Machine Learning for Credit Scoring: A Technical Guide. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_9
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