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

Agustin Pérez-Martín,Marta Vaca,Agustin Pérez-Torregrosa,Alejandro Rabasa

doi:10.3390/math8111971

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.

Highlights

The granting of a loan implies a risk for the financial institution, which arises due to the possibility that the borrower does not comply with the repayment of the loan; that is, the possibility of delinquency
Financial institutions need to provide a response to their potential borrowers, and so prediction models must meet two requirements: computational efficiency and predictive efficacy. We study this situation as a big data problem, and we consider a variable selection method for the model in order to reduce the volume of data, maintaining the model’s efficiency with the aim of increasing its computational efficiency
We used our own design with the Institute of Fiscal Studies (IEF) dataset, using some synthetic variables such as the target variable and borrowed amount

Summary

Introduction

The granting of a loan implies a risk for the financial institution, which arises due to the possibility that the borrower does not comply with the repayment of the loan; that is, the possibility of delinquency.Financial institutions face a decision problem of whether to grant the loan to the client, which implies an assessment of the probability of each applicant presenting delinquency problems. An institution attempts to calculate an uncertain event with data from the past. In other words, this is an estimation or prediction problem known as credit scoring. Any credit rating system that enables the automatic assessment of the risk associated with a banking operation is called credit scoring This risk may depend on several customer and credit characteristics, such as solvency, type of credit, maturity, loan amounts and other features inherent in financial operations. It is an objective system for approving credit that does not depend on the analyst’s discretion

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