Title

Bank Loan Default Predictive Models with Logistics Regression & Support Vector Machine

Presenter Information/ Coauthors Information

Shuk Ping Wong, Minnesota State University, MankatoFollow

Presentation Type

Poster

Student

Yes

Track

Finance/Insurance Application

Abstract

Risk Management is one of the most crucial areas for banks. Banks are constantly working on effective models to estimate the likelihood of whether a customer could default to maintain a sustainable and profitable business. Although credit scoring is a common indicator for bankers, some financial datasets simply do not come with this variable. This study builds a logistic regression model and a support vector machine (SVM) model to predict whether the loan borrower will default based on different categorical variables. The performance of the models is compared based on accuracy and efficiency. The importance of variables is ranked as a discussion with the result.

Start Date

2-11-2020 1:00 PM

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Feb 11th, 1:00 PM

Bank Loan Default Predictive Models with Logistics Regression & Support Vector Machine

Volstorff A

Risk Management is one of the most crucial areas for banks. Banks are constantly working on effective models to estimate the likelihood of whether a customer could default to maintain a sustainable and profitable business. Although credit scoring is a common indicator for bankers, some financial datasets simply do not come with this variable. This study builds a logistic regression model and a support vector machine (SVM) model to predict whether the loan borrower will default based on different categorical variables. The performance of the models is compared based on accuracy and efficiency. The importance of variables is ranked as a discussion with the result.