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

Ernest Preston Goss, Harish Ramchandani

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

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