Abstract
Digital transformation after the pandemic is a must if a company wants to survive in a highly competitive environment. Machine Learning (ML) applications are no strangers to Digital Transformations, and banks are looking for ways to improve efficiency by means of similar technologies. In this work, we propose a machine learning model for predicting the credit default using the LendingClub public dataset. The accepted loans include data ranging from 2007 to 2017. For this purpose, we implement support vector machines and logistic regression models. The results showed that support vector machines is a high accurate model (93%) for predicting the credit default.
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References
George, N.: All lending club loan data (2019). https://www.kaggle.com/datasets/wordsforthewise/lending-club
Giannaklis, I.: The five main imperatives for banks in a post-pandemic era (2021). https://www.worldfinance.com/banking/the-five-main-imperatives-for-banks-in-the-post-pandemic-era
Mohammad, M.: Credit risk analysis in peer to peer lending data set: Lending club. Digit. Commonds 1(1), 48 (2019). https://digitalcommons.bard.edu/cgi/viewcontent.cgi?article=1299 &context=senproj_s2019
Osei, S.: Accuracies of some learning or scoring models for credit risk measurement. ResearchGate 1(1), 15 (2021). https://www.researchgate.net/publication/350067975_Accuracies_of_some_Learning_or_Scoring_Models_for_Credit_Risk_Measurement
Satchidananda, S., Simha, J.B.: Comparing Decision Trees with Logistic Regression for Credit Risk Analysis. International Institute of Information Technology, Bangalore (2006)
Sebastian, D.: Big data and machine learning in central banking. Working Papers 1(930), 26 (2021). https://www.bis.org/publ/work930.htm
Suthaharan, S.: Support vector machine. In: Machine Learning Models and Algorithms for Big Data Classification. ISIS, vol. 36, pp. 207–235. Springer, Boston, MA (2016). https://doi.org/10.1007/978-1-4899-7641-3_9
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Malagon, E., Troncoso, D., Rubio, A., Ponce, H. (2022). Machine Learning Techniques in Credit Default Prediction. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13612. Springer, Cham. https://doi.org/10.1007/978-3-031-19493-1_17
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DOI: https://doi.org/10.1007/978-3-031-19493-1_17
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