A Map of the European Insurance Sector – Are There any Borders

Timotej, Jagric; Bojnec, Štefan; Vita, Jagric

doi:10.24818/18423264/52.2.18.17

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Some applications have been in the broad financial field, however, the research scope, and especially the goals, differs considerably from studying the banking sector, e.g., [38][39][40], up to financial studies of the corporate sector [41]. Insurance industry's sectoral properties and European integration were studied [42]. For insolvency prediction, the SOM have not been applied yet, to our knowledge.…”

Section: Scr = Scr Nonli F E + Scr LI F Ementioning

confidence: 99%

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New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

Jagrič,

Zdolšek,

Horvat

et al. 2023

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Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools enable them to discover more and to intervene in a timely manner. We propose an artificial intelligence approach using Kohonen’s self-organizing maps. The dataset used for development and testing included yearly financial statements with 4058 observations for European composite insurance companies from 2012 to 2021. In a novel manner, the model investigates the behaviour of insurers, looking for similarities. The model forms a map. For the obtained groupings of companies from different geographical origins, a common characteristic was discovered regarding their future financial deterioration. A threshold defined using the solvency capital requirement (SCR) ratio being below 130% for the next year is applied to the map. On the test sample, the model correctly identified on average 86% of problematic companies and 79% of unproblematic companies. Changing the SCR ratio level enables differentiation into multiple map sections. The model does not rely on traditional methods, or the use of the SCR ratio as a dependent variable but looks for similarities in the actual insurer’s financial behaviour. The proposed approach offers grounds for a Suptech tool of predictive generation to support early detection of the possible future financial distress of an insurance company.

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Section: Scr = Scr Nonli F E + Scr LI F Ementioning

confidence: 99%

“…− Sufficient resolution of the final topology must be archived in order to make separations process efficient. − We used our previous experience on modelling European banking and insurance sectors as guidance [39,42].…”

Section: Scr = Scr Nonli F E + Scr LI F Ementioning

confidence: 99%

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

Jagrič,

Zdolšek,

Horvat

et al. 2023

Information

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show abstract

Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting

Carta¹,

Ferreira²,

Podda³

et al. 2021

Expert Systems with Applications

131

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Dependence relationship between insurance demand and some economic, financial, and socio-demographic factors: Evidence from different groups of European countries

Athanasiadis,

Šulista,

Mrkvička

2023

Insurance Markets and Companies

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The insurance sector is a significant component of the economy and its financial system. Therefore, sound growth and protection of the insurance industry against systemic risks are critical requirements for any country’s social and economic development. The paper analyzes the dependence between insurance demand represented by insurance penetration and various factors from economics, finance, socio-demographics, and institutions. The analysis is conducted within certain clusters of European countries, which are determined by functional clustering analysis concerning the magnitude and shape of the insurance penetration curves. The dependence is analyzed via linear mixed-effect models. The analysis shows significantly different dependencies between the clusters, proving the existence of different conditions for different European insurance markets, especially concerning economic growth, income, financial development, and unemployment. In contrast, interest rates, inflation, urbanization, and education do not play a significant role in these insurance markets. The institutional development seems insignificant for all clusters except for certain economies in transition. The findings imply that there is a need for countries across Europe to identify country-specific determinants of insurance. In that respect, European policymakers and managers can direct specific policies based on the identified determinants’ relationship with insurance, especially in developing countries.

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A Map of the European Insurance Sector – Are There any Borders

Cited by 3 publications

References 5 publications

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting

Dependence relationship between insurance demand and some economic, financial, and socio-demographic factors: Evidence from different groups of European countries

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