Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans

Agustin Pérez-Martín, Agustin Pérez-Torregrosa, Alejandro Rabasa and Marta Vaca
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Agustin Pérez-Martín: Economic and Financial Studies Department, Miguel Hernández University of Elche, 03202 Elche, Spain
Agustin Pérez-Torregrosa: Economic and Financial Studies Department, Miguel Hernández University of Elche, 03202 Elche, Spain
Alejandro Rabasa: Operations Research Center, Miguel Hernández University of Elche, 03202 Elche, Spain
Marta Vaca: Economic and Financial Studies Department, Miguel Hernández University of Elche, 03202 Elche, Spain

Mathematics, 2020, vol. 8, issue 11, 1-16

Abstract: Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time.

Keywords: credit scoring; feature selection; big data; data mining (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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