3 Citations (Scopus)

Abstract

Financial institutions suffer from risk of losing money from bad customers. Specifically banking sectors where the risk of losing money is higher, due to bad loans. This causes economic slowdown of the nation. Hence credit risk assessment is an important research area. In this paper research methodology based framework using diagnostic and cross sectional study is used for risk analysis. Empirical approach is used to build models for credit risk assessment with supervised machine learning algorithms. The Logistic Regression and Neural Network classification models are implemented and evaluated using are evaluated using chi square statistical test. This study infers the significance of using machine learning algorithms to predict bad customers. Logistic Regression has shown better performance for the data set and parameters which are considered for this work.

Original languageEnglish
Pages (from-to)3649-3653
Number of pages5
JournalAdvanced Science Letters
Volume23
Issue number4
DOIs
Publication statusPublished - 01-01-2017

Fingerprint

Credit Risk
Logistic Regression
Risk Assessment
Risk assessment
risk assessment
Learning algorithms
Learning systems
Logistics
Learning Algorithm
logistics
credit
Machine Learning
Customers
Chi-squared test
Statistical tests
Banking
Risk Analysis
banking
money
Risk analysis

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Health(social science)
  • Mathematics(all)
  • Education
  • Environmental Science(all)
  • Engineering(all)
  • Energy(all)

Cite this

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title = "Credit risk assessment using machine learning algorithms",
abstract = "Financial institutions suffer from risk of losing money from bad customers. Specifically banking sectors where the risk of losing money is higher, due to bad loans. This causes economic slowdown of the nation. Hence credit risk assessment is an important research area. In this paper research methodology based framework using diagnostic and cross sectional study is used for risk analysis. Empirical approach is used to build models for credit risk assessment with supervised machine learning algorithms. The Logistic Regression and Neural Network classification models are implemented and evaluated using are evaluated using chi square statistical test. This study infers the significance of using machine learning algorithms to predict bad customers. Logistic Regression has shown better performance for the data set and parameters which are considered for this work.",
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language = "English",
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Credit risk assessment using machine learning algorithms. / Attigeri, Girija V.; Pai, M. M.Manohara; Pai, Radhika M.

In: Advanced Science Letters, Vol. 23, No. 4, 01.01.2017, p. 3649-3653.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Credit risk assessment using machine learning algorithms

AU - Attigeri, Girija V.

AU - Pai, M. M.Manohara

AU - Pai, Radhika M.

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AB - Financial institutions suffer from risk of losing money from bad customers. Specifically banking sectors where the risk of losing money is higher, due to bad loans. This causes economic slowdown of the nation. Hence credit risk assessment is an important research area. In this paper research methodology based framework using diagnostic and cross sectional study is used for risk analysis. Empirical approach is used to build models for credit risk assessment with supervised machine learning algorithms. The Logistic Regression and Neural Network classification models are implemented and evaluated using are evaluated using chi square statistical test. This study infers the significance of using machine learning algorithms to predict bad customers. Logistic Regression has shown better performance for the data set and parameters which are considered for this work.

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