Insurance Management Research Articles

Testing The Difference of the Characteristics of the Research Sample on the Working Motivation of Insurance Agent Employees of Baoviet Life Corporation, Vietnam

The article provides the concepts of motivation, work motivation, insurance agent. Work motivation is not a new topic, which has been studied by many authors. However, the research team found a gap through studying the impact of sample characteristics on work motivation. The test results on qualitative survey data have shown that the importance of control variables such as gender, education level or working seniority has an influence on the independent variable that is the work motivation of university employees. Life insurance manager at Bao Viet Life Vietnam Corporation. Through the results of the analysis of survey data, the research team also proposes some recommendations to increase labor motivation such as: Create a reciprocal and supportive working environment or The remuneration regime needs to be closely associated with the employees. working seniority to increase labor motivation for insurance agent employees.

Anti-crisis management of insurance companies

The article analyzes various approaches to defining the concept of anti-crisis management, in which anti-crisis management is interpreted as a set of measures of the state (anti-crisis management at the macro level) and the management of a specific financial institution (anti-crisis management at the micro level). The data on the change in macroeconomic indicators of the world's leading economies and the risks to the global economy provoked by russia's full-scale war against Ukraine are collected and systematized. The current state of the world economy, the processes that the full-scale invasion initiated in it, the magnitude of the growth of energy prices, interest rates, inflation rates in the context of the world's largest economies, macroeconomic indicators of Ukraine, namely the scale of the collapse of the Ukrainian economy as a result of the war, the dynamics of GDP, inflation, and the forecast dynamics of the inflation rate are studied. The author assesses the current state of the insurance market, the approximate amount of its losses, the main indicators of insurers' activities, such as the volume of insurance premiums, insurance payments, the level of payments, the level of reinsurance, changes in the structure of the insurance portfolio of insurers, the unprofitability or profitability of a particular type of insurance, and the impact of global economic processes on the insurance market of Ukraine. The author analyzes the feasibility of taking anti-crisis management measures at the level of legislation, state regulation, control and supervision, management of insurance, investment and financial activities of Ukrainian insurers. The author examines the operational measures taken by the management of domestic insurance market participants in the first days of the war to reduce the impact of the most catastrophic threats to the financial condition of companies, life and health of their employees, etc. The author provides a list of the main anti-crisis management strategies for restoring the economic potential of insurers and analyzes the ways to reduce the impact of a full-scale invasion of the insurance market and restore the insurance market, and the measures taken by insurers today to adapt the activities of insurance companies in wartime.

Research on the Influence of Financial Digital Innovation on Small and Micro Enterprises

In recent years, digital economy has been deeply integrated into all fields of economic society, becoming a crucial engine to drive modern economy. Demand for online financial services is growing fast, for example, traditional financial services - credit, insurance and wealth management are operated via non-contact methods. In post-epidemic period (COVID-19), it is difficult for small and micro enterprises to obtain support from financial institutions through traditional financial models due to their light assets and lack of traditional mortgages and pledges. Therefore, they frequently encounter difficulties in operations. Under the background, this study focuses on analyzing how financial industry supports small and micro enterprises, and improves availability of financial services by digital means. Specifically, it selects supply chain bill platform as research case. Then, it describes significant role of supply chain bill in financing of small and micro enterprises from the perspective of innovative value of supply chain bill.

Research on Accurate Pricing Strategy of Insurance Industry under the Background of Big Data Era

Under the era of big data, big data technology has been widely used in all aspects of insurance precision marketing, product design, risk control, internal operation and so on, which effectively solves the practical problems faced by insurance companies in the transformation and upgrading of insurance companies in the big data era, and it is conducive to promoting the high-quality development of the insurance industry. The new generation of information technology represented by big data technology has promoted the progress of insurance science and technology, infiltrating into all aspects of insurance marketing, products, pricing, service and management. On the basis of a brief description of the application of big data in the insurance industry, and taking the insurance pricing research as the starting point, this paper analyzes the positive and negative effects of big data's technology application on the insurance industry. Finally, it focuses on the specific research on the accurate pricing strategy of the insurance industry under the background of big data era from three aspects: establishing big data actuarial thinking, establishing accurate pricing model and insisting on customer-centered.

Analysis of Financial Literacy and Its Effect on the Sustainability of MSME in Covid-19 Pandemic Era (Case Study on MSME in Pesawaran District)

MSME has an important role in economic development and growth as well as reducing unemployment in Indonesia, therefore it is very important to save MSME from the impact of the current COVID-19 pandemic. According to the Head of the MSME and Cooperatives Office of Pesawaran District, he stated that the main problem for business principals is a decline of income, means that more than fifty percent and the stagnation of business activities carried out. The purpose of this study is to produce the ability of business principals Pesawaran District to manage finances in order to maintain their business during the current covid-19 pandemic. This study uses the theory of financial literacy which is based on five aspects, namely basic personal finance, savings and loans, insurance, investment and risk management. The survey results in this study found that MSME business principals in Pesawaran District had carried out well the established aspects of financial literacy, so the business principals in Pesawaran District were able to survive the COVID-19 pandemic and could be an example for other business principals.

Designing a Dynamic Model to Evaluate the Efficiency of Social Capital in the Insurance Industry

This research is organized with the aim of designing a dynamic model to evaluate the effectiveness of social capital in the insurance industry using the method of content analysis, which in terms of purpose is practical and in terms of the method of collecting information, it is a part of qualitative research. The statistical population consists of insurance industry experts and managers of Iran's social security insurance branches in Tehran. For sampling in this research, the purposeful sampling method, which is non-probability sampling, was used. In addition, after collecting the information, the codes related to the written interviews were analyzed using thematic analysis method. The credibility of the research was obtained through the agreement of experts and validity with the help of consensus of opinions and face validity. Qualitative data analysis in this study was done with thematic analysis method. ATLAS TI software was used for qualitative content analysis. Also, by examining the data obtained from the text, 1 overarching theme, 5 organizing themes, and 21 basic themes were extracted. The results showed that all the themes identified from Expert opinions are relevant and there is favorable consensus about their importance.

Understanding the Price Behaviour of selected banks from banking sector in Bombay Stock Exchange

Over the past two decades, the globalisation trend has led to a significant increase in financial markets, particularly the stock market. Stock markets play an important role as an indicator of the economy's overall performance. Analyzing past price movements is how Technical Analysis predicts future price movements. It encourages the mobilization of funds, provides equity capital to the banking sector, and offers adequate returns for investors. The Indian banking sector has undergone significant changes in recent years, with banks diversifying from traditional deposit-taking and lending activities to more complex activities such as investment banking, insurance, and wealth management. Therefore, understanding the price behavior of banks listed on the Bombay Stock Exchange (BSE) is crucial for investors, policymakers, and researchers. By using stock market indicators, investors can observe significant market movements. The main objective of this paper is to analyze a top 5 bank within the banking sector and forecast its stock price behavior. Additionally, the paper aims to test the effectiveness of technical indicators such as RSI and MACD in predicting the stock's behavior. The study focuses on analyzing the stock price trends of selected banks over a period of five years (2017-2022).

Fintech in India: Exploring Opportunities and Challenges in Andhra Pradesh

This article discusses the recent growth of the Fintech industry in Andhra Pradesh, India, with over 120 start-ups focused on digital payments, lending, insurance, and wealth management. The state government has significantly promoted Fintech innovation and adoption through its Fintech Accelerator Program and partnerships with companies such as Paytm and Visa. The Fintech industry is expected to maintain its growth trajectory in 2023, with the government's support and a thriving start-up ecosystem contributing to this growth. A literature review of Fintech in India highlights the industry's rapid growth, particularly in digital payments and lending, and its potential to expand access to financial services for underserved segments of the population. The review also identifies regulatory challenges and lack of trust as barriers to Fintech adoption, the role of Fintech in promoting financial literacy and education, and its potential to address the credit gap in India.

Managing Chinese and Russian healthcare development using a predictive Gray model and an autoregressive relationship model

This article examines the possible risks for health insurance funds due to an ageing population and the prevalence of infectious diseases in the world. By analyzing the structural differences between the existing health insurance funding systems in China and Russia, and using a Gray's model and an autoregressive model to forecast the existing flows of health insurance funds in Russia and China. The results of the forecasting model are then incorporated into a subsequent management model in order to propose optimisations for the future management of health insurance systems in both countries. The novelty of this paper lies not only in the analysis of the structure of the health insurance financing system, but also in the use of a predictive model to propose optimised management decisions for the future development of health insurance. The results provide theoretical support as well as guidance for national health insurance management. The results of the optimized management model for Russian health insurance show that the optimized solution not only improves the financing gap well, but also leaves a balance.

Managing Chinese and Russian healthcare development using a predictive Gray model and an autoregressive relationship model

This article examines the possible risks for health insurance funds due to an ageing population and the prevalence of infectious diseases in the world. By analyzing the structural differences between the existing health insurance funding systems in China and Russia, and using a Gray's model and an autoregressive model to forecast the existing flows of health insurance funds in Russia and China. The results of the forecasting model are then incorporated into a subsequent management model in order to propose optimisations for the future management of health insurance systems in both countries. The novelty of this paper lies not only in the analysis of the structure of the health insurance financing system, but also in the use of a predictive model to propose optimised management decisions for the future development of health insurance. The results provide theoretical support as well as guidance for national health insurance management. The results of the optimized management model for Russian health insurance show that the optimized solution not only improves the financing gap well, but also leaves a balance.

Страхування відповідальності власників транспортних засобів в Україні в контексті євроінтеграції та страхового менеджменту

Mandatory civil liability insurance for owners of land vehicles in Ukraine provides social protection for road accident participants and contributes to European integration processes. The purpose of the research is to analyze the current condition of the car liability insurance market in Ukraine, identify problems and substantiate practical recommendations for improving insurance management, taking into account European integration processes. The mandatory liability insurance for owners of land vehicles in Ukraine was systematized. The structure of the car insurance portfolio of insurance companies in Ukraine was analyzed. The impact of war and quarantine on the structure of the insurance portfolio and the level of digitization of the car insurance market in Ukraine is substantiated. The main indicators of the market of mandatory liability insurance for land vehicles owners in Ukraine are analyzed. The necessity to increase the size of insurance sums to European standards is substantiated. The recommendations regarding the use of the direct losses compensation system for all participants in the market of mandatory liability insurance for owners of ground vehicles under internal contracts are given. Proposals to improve the quality of liability insurance under an internal agreement by introducing non-cash payments with private individuals when paying compensation and changing the procedure for calculating the cost of repairing cars when damage is established are substantiated.

Quality Evaluation of Medical Insurance Management in Public Hospital – The Case of a Hospital in Hebei Province, China

This paper studied the establishment of a quality evaluation system for medical insurance management in a public hospital in Hebei, China, as an example. The paper used the analytic hierarchy process (AHP) as well as the balanced scorecard approach, and the model developed suggests that the hospital should pay more attention to the patient perspective when evaluating the quality of medical insurance management.

FinGAN: Chaotic generative adversarial network for analytical customer relationship management in banking and insurance

FinGAN: Chaotic generative adversarial network for analytical customer relationship management in banking and insurance

Methodological challenges in assessing loss and damage from climate-related extreme events and slow onset disasters: Evidence from India

Estimations of losses and damages (L&Ds) from climate-related extreme events and slow-onset disasters have received significant attention in all the Conference of Parties (CoP) meetings in the last two decades, particularly after the CoP-13 held in Bali in 2007. Society incurs two types of L&D, i.e., economic and non-economic, and the latter is mostly found ignored in L&D assessment reports, because it cannot be estimated directly, either in pecuniary or non-pecuniary terms. While the financial part of L&Ds has taken centre stage in international negotiations, estimations of ex-ante and ex-post L&Ds could assist policymakers in assessing unavoided and unavoidable L&Ds, provide inputs to demand compensation, insurance, and risk management options, and facilitate further progression of L&D scholarship. Although the computation of L&Ds is imperative, a few estimations are available from developing nations, including India, because of a lack of clarity in the existing definition, methods and several limitations associated with estimating L&Ds. Hence, this paper points out methodological issues that emerged while assessing post-disaster L&Ds in India from extreme events and slow-onset disasters, particularly cyclonic storms, floods, and droughts. This study, on the one hand, provokes scholarly communities and policymakers to address these methodological gaps to further increase the number of L&D studies, and on the other hand, furnishes suggestions to redefine L&D and to improvise estimation methods, so that both economic and non-economic L&D indicators may be covered to a large extent.

Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records

Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method for drivers and transportation companies to ensure driving safety. Conventionally, the relationships between ADBs and accident-related records are analyzed when assessing driving risk. However, such records typically overlook driver responsibility for driving risks and depend considerably on the person producing the data (e.g., police officers or insurance managers). Foremost, conventional approaches do not consider non-accident situations that comprise most driving scenarios. Thus, we propose a novel driving risk assessment method that uses only ADB data. In this method, interpretable latent risk factors are extracted from ADB data via sparse non-negative matrix factorization (NMF), and then the driving risk score is computed on a scale of 0–100. The proposed method was validated by adopting a real-world application to assess the driving risk of bus drivers in South Korea and by conducting an evaluation performed by transportation experts in conjunction with the Korea Transportation Safety Authority. Results revealed that the proposed method can discriminate between high- and low-risk driving, thus providing clear guidelines to improve driving. Then, the proposed driving risk score assessment method using NMF was compared with existing machine learning-based risk assessment methods. The proposed method outperformed the conventional methods in terms of driving risk discrimination and interpretability. This study can provide risk assessment guidelines based on driving behavior records and contribute to the application of machine learning in transportation safety management.

Social insurance and earnings management: Too rich to be good.

We examine the relationship between social insurance contributions and earnings management for publicly listed firms in China. Our empirical results show that the social insurance contributions burden significantly reduces the degree of earnings management by reducing the level of free cash flow. Additionally, the negative relation between social insurance contributions burden and earnings management is more pronounced when the internal and external social insurance pressures are high and when the firms are large non-state-owned enterprises. We also discuss the heterogeneity among firms for different financing constraints, external financing environment, regional marketization, and internal and external corporate governance. Finally, we further find that under the dual collecting system, although the social security administration is a better collecting agency, the local tax bureau acting with full responsibility is more effective than the collecting system.

The Impact of GATS on the Insurance Sector: Empirical Evidence from Pakistan

Abstract This study aims to analyse the effects of the General Agreement on Trade in Services (GATS) on the investment activities of the Pakistani insurance sector. First, we analyse the impact of GATS on the overall and company-wise return on investment of the insurance sector of Pakistan. In a panel data setting, the traditional fixed and random effect models are unsuitable for dealing with the possible issues, including endogeneity, unobserved heterogeneity, profit persistency, and the correlation between lagged dependent variables and independent variables. Therefore, we apply the Generalised Method of Moments (GMM) system estimator to the panel data from 1984 to 2018. In the Arellano-Bond framework, the estimated results indicate that the overall investment returns of the Pakistani insurance sector have increased after the GATS membership. However, company-wise analysis reveals mixed evidence. The average returns of IGI Insurance Limited and EFU General Insurance Limited have increased significantly after the GATS membership. These results support one of our empirical conjectures that a rising tide lifts some boats more than it does others. Then, we apply Markowitz’s Portfolio Theory to estimate the portfolio returns and the associated risk using the financial statements data from 1984 to 2018. The estimates of portfolio analysis reveal that the portfolio returns of the Pakistani insurance sector improve by 6.70 percentage points after the membership. However, the associated portfolio risk also increases by 11.13 percentage points, 7.40 times higher than the Pre-GATS risk. Despite the better returns, there is an intensive increase in investment volatility after GATS membership. This study provides a valuable reference for insurance managers and the Ministry of Finance and Securities & Exchange Commission of Pakistan to control and improve the insurance sector’s performance in Pakistan.

Measuring Financial Efficiency of Insurance Companies in Indonesia Using Stochastic Frontier Analysis Approach

This paper aims to analyze the efficiency value and compare the efficiency ratio between sharia insurance and conventional insurance companies, both life insurance and general insurance in Indonesia, for 2018-2020. The research applies the Stochastic Frontier Analysis (SFA) method, which uses total capital and expenses as input variables, and total income as output variables. The efficiency values of Islamic and conventional insurance companies' results were compared using an independent sample t-test statistical test. The population of this research is all Islamic and conventional insurance companies listed on the website of the Financial Services Authority (OJK). The sample used is 19 sharia insurance companies (15 life insurance companies and four general insurance companies). The sample of conventional insurance is 23 companies (22 life insurance companies and one general insurance company). The results showed that the efficiency value of Islamic insurance companies (0.6549) was 0.0697 lower than conventional insurance (0.7246). It can be concluded that the efficiency of conventional insurance is better than Islamic insurance. Islamic insurance management capabilities are lower than conventional insurance companies’ management capabilities.

The challenge of healthcare big data to China’s commercial health insurance industry: evaluation and recommendations

BackgroundChina’s social medical insurance system faces challenges in financing, product coverage, patient health responsibility sharing and data security, which commercial health insurance companies can help address. Confronting accelerated population aging, the rapid increase of patients with chronic diseases and the maternal and child healthcare needs created by the three-child policy, the Chinese government has encouraged the development of commercial health insurance. But China's commercial health insurance companies face financial sustainability problems, limited product ranges and high operating costs. At the same time, the informatization level of China's healthcare industry, and the value of healthcare big data, is increasing. We analyze and describe the potential application of healthcare big data in the life cycle of China's commercial health insurance system and provide specific action plans for Chinese commercial health insurance companies; identify the challenges to commercial health insurers; and make recommendations for the application of big health data by commercial health insurers. Our recommendations inform healthcare policy makers on the development of commercial health insurance and the improvement of the healthcare financing system. We not only verify the value of healthcare big data, but also identify specific ways that healthcare big data plays in the development of commercial health insurance. Based on the research results, we recommend new policies for government and new uses of healthcare big data for commercial health insurance institutions. The benign development of commercial health insurance will improve the level of health services in China.MethodsBy interviewing health insurance managers (including actuaries, product managers, business executives, information technology medical workers, and commercial health insurance personnel) and by accessing research papers, industry reports, news reports and public information disclosure documents about commercial health insurance, we describe the impact of healthcare big data on the life cycle of commercial health insurance products and processes.ResultsWe identify the issues and challenges of commercial health insurers in the use of healthcare big data, and advance specific strategies to expand the use of healthcare big data. In the life cycle of commercial health insurance products, healthcare big data can improve premium income, control medical costs and increase operational efficiency. First, healthcare big data can increase premiums, products and services by attenuating moral hazard and adverse selection problems, where high quality clients over-pay and high-risk clients underpay for health insurance. Second, healthcare big data can reduce medical expenses compensation pay-outs by promoting the establishment of a management medical system. Finally, the use of healthcare big data improves operational efficiency by increasing payment speeds, identifying fraud and increasing claim verification processes through automating payments and reducing offline processes. We discuss the obstacles to obtain healthcare big data confronting commercial health insurance companies. The sharing and data mining of healthcare big data brings privacy risks to the insured and there are significant differences in data standards and quality of healthcare big data that limit the application of healthcare big data in commercial health insurance. We recommend that national, regional and local government departments coordinate policies to facilitate the cooperation between commercial health insurance companies and regional healthcare big data platforms. In terms of technology, we recommend the establishment of data sharing platforms and data exchange mechanism across institutions and regions according to nation-wide standards and specifications. Government management departments should establish healthcare big data standards and specification system, promote the construction of healthcare big data and ensure the integrity, authenticity and reliability of health data. We recommend data quality continuous improvement and management mechanisms that combine technology and management. Government regulation should oversee commercial health insurance institutions and establish data security management systems to monitor and supervise the privacy of personal data.ConclusionsHealthcare big data can play an important role in the development of China's commercial health insurance industry. Healthcare big data can increase commercial health insurers’ financial viability while providing improved, and cost-effective, products and services. By providing more and better information to insurers, healthcare big data attenuates the asymmetric information problem, addressing moral hazard and adverse selection problems. By combining hospital and medical organization management information systems with insurers’ data management, healthcare big data can help insurers set sustainable premiums, control medical costs and promote operational efficiency. At present, the informatization degree of China's healthcare industry remains limited. To improve the performances, products and services of commercial health insurers, we recommend government reforms in healthcare big data, such as expanding medical industry cooperation; further developing the processes of applying healthcare big data; augmenting data sharing; addressing privacy risks; setting data standards; and improving data quality.

Role of digital health insurance management systems in scaling health insurance coverage in low- and Middle-Income Countries: A case study from Nigeria

BackgroundIncreasing global commitment to Universal Health Coverage (UHC) in the past decade has triggered UHC-inspired reforms and investments to expand health service coverage in many Low- and Middle-Income Countries (LMICs). UHC aims to ensure that all people can access quality health services, safeguard them from public health risks and impoverishment from out-of-pocket payments for healthcare when household members are sickAimThis paper reviews the role of health insurance as a policy tool to address health financing as a contributory mechanism for accelerating the achievement of UHC in LMICs. We focus on Nigeria's legal framework for health insurance coverage for its whole population and the role of technology in facilitating enrollment to health insurance schemes.MethodsFrom May to July 2022, we adopted a cross-sectional case study design combining: (i) a literature review of the effects of UHC with (ii) document analysis of health insurance systems in Nigeria, and (iii) secondary analysis of health insurance datasets to understand experiences of deploying MedStrat, a locally-developed digital health insurance management system, and its features that support the administration of health insurance schemes in multiple states of Nigeria. We drew on contemporary technology adoption models to triangulate diverse data analyzed from literature and documents reviews and from health insurance datasets to identify: (i) enablers of adoption of digital insurance schemes, (ii) the contribution of digital technology to expanding access to health insurance, and (iii) further scalability of digital insurance intervention.ResultsPreliminary findings suggests that digital insurance management systems can help to increase the number of enrollees for insurance especially among poor households. Three contextual enablers of adoption of digital insurance schemes were a favourable policy environment, public-private-partnerships, and sustained stakeholder engagement and training.Discussion and conclusionKey elements for successful scaling of digital health insurance schemes across Nigeria and similar contexts include: (i) ease of use, (ii) existing digital infrastructure to support electronic insurance systems, and (iii) trust manifested via data encryption, maintaining audit trails for all data, and in-built fraud prevention processes. Our findings affirm that digital health technology can play a role in the attainment of UHC in LMICs.