Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers

Ernest Goss, Harish Ramchandani

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Past studies have documented the failure of the Insurance Regulatory Information System (IRIS) to provide adequate warning of insurer financial distress or insolvency. As a result, scholars have examined alternative parametric and non-parametric models to predict insurer insolvency. This study uses a neural network, a non-parametric alternative to past techniques, and shows how this methodology predicts insurer insolvency more effectively than parametric models.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalJournal of Economics and Finance
Volume19
Issue number3
DOIs
StatePublished - Sep 1995

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Insurer
Prediction
Insolvency
Logit regression
Discriminant analysis
Neural networks
Regression analysis
Methodology
Financial distress
Nonparametric model
Insurance
Warning
Information systems
Parametric model

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)
  • Finance
  • Economics and Econometrics

Cite this

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