Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Fazlollah Soleymani, Houman Masnavi and Stanford Shateyi
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Fazlollah Soleymani: Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
Houman Masnavi: Intelligent Materials and Systems Lab, University of Tartu, 50411 Tartu, Estonia
Stanford Shateyi: Department of Mathematics and Applied Mathematics, School of Mathematical and Natural Sciences, University of Venda, P. Bag X5050, Thohoyandou 0950, South Africa

Mathematics, 2020, vol. 9, issue 1, 1-15

Abstract: Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k -nearest neighbors (KNN) with k = 1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.

Keywords: lending portfolio; machine learning; classifications; k-nearest neighbors; maturity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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