Failure threats of insurance companies: A case study of financial environments of Jordan

  • Received June 10, 2021;
    Accepted July 27, 2021;
    Published August 10, 2021
  • Author(s)
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  • Article Info
    Volume 18 2021, Issue #3, pp. 113-126
  • Cited by
    3 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

Insurance firms are known to have unique financial failure characteristics that affect the financial environment of the countries. Therefore, the purpose of this study is to assess the validity of the model used in predicting the financial failures of insurance companies. The model is believed to help in stabilizing the financial environment of the countries by predicting any collapses in the insurance sector. A discriminate regression technique was used to test 28 indicators chosen from 11 financial failure model parameters. 11 parameters of the model are the following: solvency, profitability, operational capabilities, structural soundness, capital expansion capacity, capital adequacy, reinsurance and actuarial issues, management soundness, capital expansion capacity, earnings and profitability, and liquidity. The results of the study proved that 22 variables from 11 parameters were significant; the study also validated the use of the financial failure model as a stable predictor of the financial failure of ASE insurance firms. The stability of the insurance industry is interpreted through the minimum deviation between the real and measured performances. The deviation was present in 3 out of 95 observations, and it affected only 3 firms out of 19, 1 firm out of 3 turned out to be affected by the risker deviation which is the type II error distorted observation. To conclude, the study by mentioning that insurance firms are not threatened by failure or distress and the financial failure model is a valid prediction model.

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    • Table 1. Group statistics
    • Table 2. Equality of group means
    • Table 3. Correlation pooled within group matrices
    • Table 4. Equality of covariance matrices
    • Table 5. Summary of canonical discriminant functions
    • Table 6. Classification statistics
    • Table 7. Comparison of real performance vs. predicted performance
    • Conceptualization
      Hussein Mohammad Salameh
    • Data curation
      Hussein Mohammad Salameh
    • Formal Analysis
      Hussein Mohammad Salameh
    • Investigation
      Hussein Mohammad Salameh
    • Methodology
      Hussein Mohammad Salameh
    • Project administration
      Hussein Mohammad Salameh
    • Resources
      Hussein Mohammad Salameh
    • Supervision
      Hussein Mohammad Salameh
    • Validation
      Hussein Mohammad Salameh
    • Visualization
      Hussein Mohammad Salameh
    • Writing – original draft
      Hussein Mohammad Salameh