Non-life Insurance Sector Research Articles

A two-stage DEA window analysis model for evaluating the efficiency in the Czech non-life insurance sector

A two-stage DEA window analysis model for evaluating the efficiency in the Czech non-life insurance sector

Aggregate Loss Models to Calculate Risk Measures

The concept of aggregate loss models pertains to a stochastic variable representing the total sum of all losses encountered within a set of insurance policies. In the non-life insurance sector, it is employed to assess the potential losses that an insurance company may face when claims made by policyholders exceed the allocated claim reserves. The purpose of studying aggregate loss models is to ascertain risk measures such as standard deviation of premium principles, value at risk (VaR), and conditional tail expectation (CTE). These steps aid insurance companies in the management and quantification of risks associated with aggregate losses. The standard deviation of premium principles is calculated analytically by substituting expected values and variances, while VaR is estimated using the Monte Carlo method to determine quantile values and confidence intervals. CTE is evaluated by computing the average losses that surpass the VaR threshold. These distributions and parameters require the Pareto distribution, which characterizes claim sizes, and the Poisson or Negative Binomial distribution, which factors in the number of claims. It is crucial to carefully consider the selection of the appropriate distribution, as it plays a significant role in determining the accuracy and reliability of the model. Furthermore, other influencing factors, such as loading factors and confidence intervals, should also be taken into account. These factors have the potential to significantly impact the quantification of risk arising from the model.

The Roles of Insurance on Economic Growth in Malaysia: An ARDL Approach

Malaysia’s economic growth has been rising steadily over the years and one of the main contributors is the insurance sector. The literature has recorded mixed findings on the relationship between insurance and economic growth. This paper examines the relationship between the insurance sector and economic growth in Malaysia over an extended period of 1990-2021. This paper uses three proxies for the insurance sector i.e. life insurance premiums, nonlife insurance premiums, and total insurance premiums. The Autoregressive-Distributed Lag (ARDL) and Error Correction Model (ECM) approaches are employed to achieve the objective. While controlling for foreign direct investment, export, and import, this paper found that there is a positive long-run relationship between non-life insurance premiums and economic growth. Conversely, a negative short-run relationship is observed between life insurance premiums and economic growth. The total insurance premium has a negative short-run relationship with economic growth yet is insignificant for the long-run. The non-life insurance sector has a larger contribution compared to life insurance on Malaysia's economic growth as the market depends more on capital formation and investments, particularly in infrastructure, real estate, and capital markets. The insurance sector provides financial expansion in terms of payment for protection which results in an economic boost.

The Impact of Culture on the Demand for Non-life Insurance Penetration in Developing Countries: Panel Data Analysis

Purpose: The impact of insurance market activity within financial development is gaining more attention in academia, as the sector experiences growth within emerging markets. The paper aims to understand which macro-economic and social variables impact the growth or decline of the non-life insurance sector broadly across European countries with a view to provide recommendations to drive increased penetration across the region.
 Methodology: Using Fixed Effects Panel Data Regression and annual data from 1990 to 2021 on 10 countries, the study examines the explanatory factors of non-life insurance demand in European countries (Australia, France, Austria, Italy, Canada, Luxemburg, Denmark, Norway, Finland and Portugal).
 Findings: The study found that GDP, and urbanization and education rates have a significant negative impact on non-life insurance penetration and density; urbanization, religion, education level and rule of law can explain positively variation in non-life insurance density and penetration across countries. Countries with higher urbanization levels, higher education level, Christian or Buddhist beliefs and more effective rule of law spend more on non- life insurance than other countries. The control of corruption and government effectiveness explain negatively variance in non-life insurance.
 Unique Contribution to Theory, Practice and Policy: Notably, governments can develop the non-life insurance sector through policies that support urbanization.Similarly, ensuring an environment that promotes economic freedom (such as low tariff, high personal choice, low government spending and high security of property rights) could be an effective way of promoting non-life insurance demand. In contrast, policies that help to reduce the rate of urbanization may yield a double dividend: less population and congestion in cities and better opportunities for the development of non-life insurance markets. Also, countries with high level of education, can develop the development of non- life insurance demand. Among many socio-economic factors such as income, urbanization and education level, our analysis suggests that cultural dimensions such as beliefs and rule of law play a role.

Non-life insurance: The state of the art of determining the superior method for pricing automobile insurance premiums using archival technique

The pricing of insurance premiums in the non-life insurance sector remains a challenging and complex task. It demands a delicate balance between accurately estimating risk exposure and ensuring profitability for insurers. Generalised Linear Regression Models (GLMs) have become the preferred methods for premium price modelling in the motor insurance sector. While the approach of using a single superior model on which predictions are based ignores the use of robust estimator models. This paper examines various methodologies and sheds light on superiority of twenty-two models compared to each other for pricing automobile insurance. These methods vary from traditional actuarial methods to the modern statistical models such as machine learning algorithms. By using archival technique, their inferiority and superiority are explored, considering the ever-changing landscape of risk factors and market dynamics. Furthermore, it highlights the potential benefits of leveraging these methods and the mechanism for pricing short-term insurance, particularly in motor vehicle insurance. It also develops a framework that can be used in pricing to cater to risk analysis constituents to mitigate uncertainties and provide good services to clients. Our findings show that ANN, NN, XGB, random forest (RF) are superior models, and we conclude that the modern statistical methods can accurately estimate the risk exposure as compared to traditional methods such as the GLMs.

Contribution of the non-life insurance sector to the economic growth of Nepal: Analysis from the EGLS approach

Nepal’s non-life insurance sector holds immense potential to drive economic growth and boost the nation towards a secure financial future. Embracing this potential is a goal and a pivotal catalyst for substantial change. The study aims to determine how the non-life insurance sector can drive economic growth in Nepal. The methodology of this study uses quantitative analysis of financial data from 2013 to 2022 from 20 non-life insurance companies in Nepal and econometric modeling to assess the sector’s impact on economic growth. Using Panel EGLS (Estimated Generalized Least Squares) regression analysis, the findings show that with one-unit increments in total investment, total premium, and total tax paid, GDP is expected to change by approximately 591.52, –920.54 and 8,470.65 units, respectively. In contrast, the coefficient for total profit is –910.3477 and is not statistically significant. The study’s main conclusion implies that the insurance sector contributes to the country’s economic growth by investing in productive activities and paying taxes to the government. Still, it also imposes a cost on the economy by charging high premiums to the insured. The profitability of the insurance sector does not affect the GDP, which indicates that the insurance sector is competitive and efficient or that other factors determine the GDP besides the insurance sector. This study contributes to a deeper understanding of the non-life insurance sector’s role in Nepal’s economic development and informs evidence-based policy decisions.

Determinants of Productivity of Conventional and Takaful Non-Life Insurers in Egypt: A Two-Stage Malmquist Productivity Index

This research aims to analyze total factor productivity and the determinants influencing the productivity of conventional and takaful non-life insurers in Egypt from 2012 to 2021. We used a two-stage Malmquist productivity index to analyze panel data from 17 non-life insurers of both types. The first stage investigated total factor productivity (TFP) utilizing the Malmquist productivity index (MPI) technique of the DEA model. The second stage utilized panel regression analysis to discover the insurer-specific factors influencing productivity. The findings indicate that TFP in the Egyptian non-life insurance sector has progressed by 5% per year from 2012 to 2021. However, the TFP experienced a decrease in 2014 and 2018, with negative growth rates of 3% and 2%, respectively. The decomposition of TFP reveals that technological change (Tch) primarily accounts for overall progress. The change in technical efficiency (EFFch) negatively impacts TFP. Furthermore, on average, takaful insurers showed higher TFP compared to conventional insurers. The findings also showed that the variables of insurer size, reinsurance, insurer age, claims rate, leverage, market share, and operational type significantly influence the growth of total factor productivity for Egyptian non-life insurers. As far as we know, this study is the first empirical evidence for both types of non-life insurers in Egypt regarding estimating TFP and its influencing factors. Consequently, this study will assist policymakers and both conventional and takaful insurers in formulating effective policies aimed at enhancing insurer productivity in Egypt.

Optimum Portfolio Construction Using Single Index Model: An Empirical Study of Nepal Stock Exchange

Purpose: The purpose of this paper is to construct the optimum portfolio of eight sectors listed in NEPSE applying the Sharpe’s single index model.
 Design/Methodology/Approach: Adopting the judgement sampling method, eight sectors were selected for the study. The study period was taken from august 2018 to august 2021 for the three years period as this time frame is the recent bull period. During this period the market index increased from around 1100 to an all-time high index 3200. To construct the optimum portfolio, daily returns of the market and sectors were used. Cut off rate (C*) was calculated in order to form the optimum portfolio using daily basis data. Those sectors whose excess return to beta ratio is higher than cut off rate were included in the optimum portfolio.
 Findings: The study revealed five sectors- non-life insurance, hydro power, finance, microfinance, and development bank-form the optimum portfolio. In terms of ratio of investment in the optimum portfolio, the proportion of investment is found higher in the finance sector (36.6%) and lower in the development bank sector (11%). Furthermore, in terms of unsystematic risk, non-life insurance (3.681) and hydro power sector (3.508) are found as risky sectors compared to other sectors. This study exclusively used the bull period data to form the optimum portfolio applying the Sharpe’s single index model in a daily basis data.
 Implications: The finding can help the investors to make informed decisions aligned with their risk tolerance, return expectation and overall investment objectives.

Does Insurance Sector Matter for Economic Complexity?

The study examines the impact of the insurance market on economic complexity in 28 OECD nations within a period of 1995–2020. The study also examines whether the impact of life insurance on economic complexity would be different from that of the non-life insurance sector within the insurance market. The results based on pooled mean group (PMG) estimators reveal that the insurance sector influences economic complexity positively. This finding is further substantiated after employing panel co-integrating regression and method of moment quantile regression (MM-QR). The study concludes that the insurance sector is a key instrument in upgrading the economic complexity of an economy. Since the distributional impact of economic complexity also depends on economic and financial risk, the insurance sector can assist in mitigating the risks and uphold the productive knowledge structure needed to enhance national product sophistication.

Determinants of financial performance in Nepalese nonlife insurance companies: A panel data analysis

The financial performance of insurance companies plays a fundamental role in driving the overall economy towards economic development and progress. The study aims to examine the impact of financial performance indicators on the Return on Equity (ROE) and Return on Assets (ROA) of nonlife insurance companies. In the methodology of study, 13 nonlife insurance companies have been considered, and panel data have been analyzed for a 14-year period (2008–2021). The fixed effects model was estimated using the E-Views software package. The panel data analysis results point to a noteworthy and favorable impact on ROA, explaining 92.75% of its variance. The results show that there is a strong positive relationship between ROA and four key factors: gross premium, retention ratio, expense ratio, and combined ratio. This underscores the importance of enhancing elements like gross premium, retention ratio, expense ratio, and combined ratio to elevate ROA. The conclusion of the study provides useful insights for improving the financial performance and competitiveness of nonlife insurance companies in Nepal. The study reveals the key success factors that affect the profitability and efficiency of the insurance sector. This suggests that nonlife insurance companies in Nepal can improve their profitability by focusing on increasing their gross premium, retention ratio, reducing expense ratio, and decreasing combined ratio. The findings have important implications for enhancing the performance and competitiveness of the nonlife insurance sector in Nepal.

How is Fintech reshaping the traditional financial markets? New evidence from InsurTech and insurance sectors in China

We examine the effect of Fintech on the market structure of traditional financial markets, and focus particularly on InsurTech and the insurance sector. We find that InsurTech has significantly reduced the non-life insurance sector's market concentration but plays a limited role in the life insurance sector's market structure. The results are not driven by potential reverse causality and remain unchanged when we employ an instrumental variables approach and use an alternative supply-side InsurTech index. We further explore the underlying mechanisms and find that, instead of competing directly with insurance companies, Fintech companies provide insurance technologies to traditional insurers and help them lower entry barriers and reduce operating costs. Our paper sheds light on how InsurTech is reshaping traditional insurance sectors, and the results are generalizable to Fintech and financial markets.

Risk-sharing function in internal capital markets: Evidence from intragroup reinsurance activities

This study explicitly investigates the risk-sharing function of internal capital markets by analyzing intragroup reinsurance (a substitute for capital) activities in the United States' non-life insurance sector. We find supporting evidence that intragroup reinsurance participants are generally associated with smoother income flows, and such an income smoothing effect exists for both ceding and assuming firms. Further to prior studies, we find that internal reinsurance ceded exerts both direct and indirect effects on premiums growth in the aftermath of the financial crisis in 2007 and 2008. In addition to direct capacity support, the reduction in income volatility is another channel through which intragroup reinsurance enhances ceding firms' premiums growth. In the presence of market turmoil, the risk-sharing function can lower member firms' insolvency risk and thus enable them to pursue business growth.

Contingent factors of the coinsurance function of internal capital markets: Evidence from the US nonlife insurance industry

By analyzing intragroup reinsurance activities in the US nonlife insurance sector from 1999 to 2016, we provide evidence that the coinsurance function of internal capital markets is contingent on internal capital providers’ financial resources and the relative sizes of capital receivers within the group. We demonstrate that insurance groups commonly use intragroup reinsurance (a substitute for capital) to support insurers that sustain underwriting losses. Larger insurers are more likely to obtain internal reinsurance if their affiliated insurers hold more financial resources. Our findings show that the financial capabilities of group members providing support affect the feasibility of the coinsurance function through the activities of internal capital markets. Group members with greater influence are more likely to benefit from the coinsurance function.

Switching intention and switching behavior of adults in the non-life insurance sector: Mediating role of brand love

In this digital era, customers in the insurance sector always look for better insurance products and services at an affordable price. When customers are unsure about service, they switch over to a better service provider. This behavior is more relevant to non-life insurance. However, the switching behavior of customers is hampered by certain switchover barriers such as “brand consciousness”, “brand pride”, “brand loyalty”, etc. This study focuses on exploring switching intentions and switching behaviors of adults in India keeping “brand love” as a mediator. A structured questionnaire was employed to collect the primary data from adults having non-life insurance products to analyze switching intentions and switching behaviors. The collected data were analyzed employing SPSS software and Hayes Process Model and appropriate statistical tools. The study results show that the switching intentions of adults vary based on their age, annual income, and education. Mean scores reveal that the lesser the age, the higher the intention to switch over. Further, based on annual income, adults who earn up to Rs 2 lakhs annually have more switching-over intentions (Mean score: 3.9719) followed by adults who earn Rs more than 2 lakhs to 5 lakhs annually (Mean score: 3.7590). Mean scores of education levels regarding switching intentions are higher among more educated adults and less among those who are qualified up to the school level.

Social and Environmental Risks Integration into Underwriting of Non-Life Insurance: A Review of Sustainable Finance in Indonesia

Regulation of the Financial Services Authority of Indonesia (POJK) Number 51/POJK.03/2017 concerning the Implementation of Sustainable Finance for Financial Services Institutions, Issuers, and Public Companies was officially enacted for the insurance sector as of 1 January 2020 and requires the insurance sector, including the general insurance sector, to carry out the implementation of sustainable finance into its business activities. This study intends to review the extent of the implementation that has been carried out, particularly in relation to the social and environmental risk management principles. Using a literature review, data specifically related to the underwriting process were gathered and analyzed. Results of this study suggested that even though 86% of 44 respondents from the previous research had already confirmed if principles of social and environmental risk management had been implemented at that time, vast majority of the non-life insurers in Indonesia had not yet executed the said integration into the underwriting process. The sustainable finance implemented by Indonesia’s non-life insurance sector is still significantly focused on short-term financial performance rather than the long-term one.

An examination of the non-life insurance sector in Indias perceptions of service quality

With the liberalization in insurance sector, service quality has become an important means of differentiation and path to achieve business success. Insurance sector plays a vital role in India’s service sector. The present paper is an attempt to study the perception of customer service quality offered by public and private sector general insurers using SERVPERE scale. A modified SERVPERE questionnaire on five point Lickert scale from 1 (strongly disagree) to 5 (strongly agree) on a sample of 200 respondents has been used to study the perception of customer service quality offered by public and private general insurers. The study showed that six factors play a vital role in influencing the perception of customers toward service quality of general insurance company. The study indicated that among the various service quality dimensions, Empathy (with the largest β value) is the best predictor, followed by Reliability, Responsiveness, Assurance, Tangibles and Convenience.

ФУНКЦІОНАЛЬНИЙ ФІНАНСОВИЙ БЕНЧМАРКІНГ НА СТРАХОВОМУ РИНКУ УКРАЇНИ

The article determines the importance of benchmarking as a tool for forming strategic decisions in the insurance business at the current stage. Functional benchmarking of financial parameters was chosen as an applied component of the study. Insurance companies as financial institutions operate exclusively with cash flows, which explain the approach chosen. Each sector of the Ukrainian economy has suffered from the war with russia. But the depth of losses and the time of the fall in individual industries differed significantly. The conducted analysis showed that the losses of the insurance business were immediate and very significant. Inefficiency and low financial stability were characteristic of most insurers in Ukraine even in the pre-war period. The market falls during the war only emphasized the previously accumulated problems. Benchmarking of financial parameters of TOP10 insurance companies was carried out based on real data of the non-life insurance sector. The first half of 2022 was chosen for the analysis, which is explained by heavy market and financial losses (related to the war) of all insurance market participants. A methodological problem (formation of statistical data and their systematization) was identified during the research. The methodological approach of conducting functional financial benchmarking, which was formed by the authors, made it possible to eliminate this problem. The main compared financial parameters, sources of initial information and presents the benchmarking procedure are defined. The basic financial parameters in comparison allow us to identify the strengths and weaknesses of the activities of insurers in today’s difficult conditions. The analysis showed that the market is in the conditions of a liquidity gap, which arose due to a sharp decrease in the receipt of insurance premiums and an increase in insurance payments. The problem of low capitalization, which is a common threat to all market participants, was identified. The problem of low efficiency also occurred in the pre-war period and worsened during the war. It is possible to predict a decrease in the number of market participants under the influence of the outlined problems in the near future. A number of solutions for overcoming crisis manifestations in the insurance market in the war and post-war periods are suggested in the article.

Financial Growth of Private Non-life Insurers in India: A Trend Analysis

In the non-life insurance sector, General Insurance Business Act of 1972 was approved by the Indian Parliament through amalgamation of 107 general insurance companies operating on that time. In the non-life insurance segment, there are 6 public sector insurers and the rest 28 are private insurers. Apart from these, there is a sole national re-insurer, namely general Insurance Corporation of India (GIC). In this context, the study attempts to analyze the financial growth of the private non-life insurers in India from 2010-11 to 2019-20. Varied results are observed in respect of selected performance indicators of private non-life insurers in India. Overall, we found no significant change in financial recital indicators during the study epoch. To conclude, it may be stated on the whole, it may be stated that despite momentous positive enlargement in FDIR, the private non-life insurers in India have not shown acceptable recital with respect to financial indicators during the time under study.

Impact analysis of macro-economic factors on non-life insurance sector in India

The development of global economy has pushed up the importance of insurance industry in the growth of an economy. This paper intends to study the non-life insurance sector with an objective to identify the macro-economic factors that influence its growth. Time series data of 37 years is considered using Johansen & Engle Granger Cointegration and Ordinary Least Square method. It was found that Final Consumption Expenditure plays a negatively significant role in influencing non-life insurance sector in India. The practical implication of this study lies in controlling the responsible factors through appropriate policy measures to ensure a sustainable growth of the non-life Insurance sector.

Catastrophic and systemic risk in the non-life insurance sector: A micro-structural contagion approach

Borrowing from the interbank contagion literature, we propose a model to study the stability of non-life insurance sector in presence of catastrophic events. These events are increasingly common, and cause a large amount of damage in short periods. To account for this risk we introduce random and correlated reinsurance claims. We show in a simulation study that the sector is particularly sensitive to random correlated insurance claims, and the threat of systemic risk emerges. The risk persists even with highly diversified network structures. The work is relevant for regulators to define macro-prudential policies, and for practitioners to measure credit risk.