Insurance Risk Research Articles

Прогнозирование страховых рисков с использованием искусственного интеллекта

В статье рассматривается влияние искусственного интеллекта на страховой бизнес в современной России. Проанализированы такие методы применения искусственного интеллекта, как нейронные сети, машинное обучение и глубокое обучение для анализа данных и прогнозирования рисков. Выделяются преимущества использования искусственного интеллекта, например точность анализа данных. Также рассматриваются ограничения, такие как проблема кибербезопасности. Описано регулирование государства страховой отрасли и последствия этого. Особое внимание уделяется определению основных видов страхования и факторов, влияющих на динамику страхового рынка. Составлен прогноз динамики страхового рынка на 2024 год. This article examines the impact of artificial intelligence on the insurance business in modern Russia, as artificial intelligence contributes to the development of various industries, including insurance. The article describes such methods of using artificial intelligence as neural networks, machine learning and deep learning for data analysis and risk forecasting. The advantages of using artificial intelligence are highlighted, for example, the accuracy of data analysis. However, limitations such as the issue of cybersecurity are also being considered. The article describes the regulation of the insurance industry by the state and the consequences of this. Special attention is paid to the definition of the main types of insurance and the factors influencing the dynamics of the insurance market. Methods of predicting dynamics using artificial intelligence are described and one of the methods is selected. Using the chosen method, a forecast of the dynamics of the insurance market for 2024 has been compiled.

The Safety of Outpatient Health Care : Review of Electronic Health Records.

Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. To measure AEs in the outpatient setting. Retrospective review of the electronic health record (EHR). 11 outpatient sites in Massachusetts in 2018. 3103 patients who received outpatient care. Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. Retrospective EHR review may miss AEs. Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

Optimizing insurance risk assessment: a regression model based on a risk-loaded approach

Optimizing insurance risk assessment: a regression model based on a risk-loaded approach

Research on Natural Disaster Risk Assessment and Insurance Decision-Making Using EWM-TOPSIS and ARIMA Models

Research on Natural Disaster Risk Assessment and Insurance Decision-Making Using EWM-TOPSIS and ARIMA Models

Insurance Premium Prediction Using Machine Learning

RiskInsight represents an innovative company poised to transform the world of insurance by applying state-of-the-art machine learning techniques. The focus of this project is the Insurance Risk Assessment System, a high-level framework carefully designed using the Python programming language and the sklearn library. Anchored by unprecedented accuracy and operational efficiency, RiskInsight enters the field of risk assessment with unwavering determination. Using the power of advanced algorithms inspired by artificial neural networks, the system navigates huge historical data and evaluates real-time risk dynamics. The cornerstone of RiskInsight is its in-depth analysis of various risk factors carefully curated in a robust database. Combining machine learning capabilities and domain expertise, RiskInsight seeks to redefine the boundaries of risk assessment by transcending traditional paradigms. The goal is to bring about a paradigm shift in super forecasting and provide stakeholders with insights that promote fairer payment structures and informed decision-making. With its innovative design and flexible functionality, RiskInsight promises to catalyze major changes in the insurance ecosystem. By providing accurate payment forecasts and advanced risk management strategies, the platform aims to enhance the efficiency of insurance operations. A testament to its transformative potential, RiskInsight is poised to usher in a new era of efficiency and equity in the insurance industry and sets a precedent for technological innovation in risk assessment and predictive systems. Keywords: Machine Learning, Fairer Payment Structure, Efficiency, Transformative Potential Acronyms: sklearn - Scikit-learn

Today’s Finance Markets in Conditions of Poly-Crisis and Crisis of Cost of Living

The article provides characteristics, statistics and factual material on countries connected with the current global economic crisis of the cost of living. The notion of the current poly-crisis was defined as a sum total of old and new risks in their close interaction and their adverse impact on global and national economy and finance markets. The focus was made on tightening of monetary and credit policy of national central banks in 2022–2023 in view of combating inflation, which resulted in more expensive credits, higher profitability of state treasury bonds, bankruptcy of corporations and declining standard of living of people. On the background of the global poly-crisis the author assessed macro-prudential policy of the Bank of Russia and standing of its banking system, including market of preferential mortgage with state support. Within the frames of crisis-management risks of Russian insurance and re-insurance industries were shown, as well as non-credit life insurance as a today’s driver of insurance market growth. In view of import-substitution policy an important innovation of the Moscow exchange – the informational-analytical, financial and trade terminal ‘Trade Radar’ was put forward. The author showed economic essence, trends and tools of de-dollarization policy of Russia and friendly countries, including BRICS project aiming at development of common supranational payment unit.

Cyber Risk in Insurance: A Quantum Modeling

In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to deal with non-commutative event paths. We investigate the classification of cyber claims according to different cyber risk behaviors to enable more precise analysis and management of cyber risks. Additionally, we examine how historical cyber claims can be utilized through the application of copula functions for dependent insurance claims. We also discuss classification, likelihood estimation, and risk-loss calculation within the context of dependent insurance claim data.

Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis

Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis

Evaluation Of Property Insurance Risk Caused by Disasters Based on Analytic Hierarchy Process and Entropy Weight Method

Evaluation Of Property Insurance Risk Caused by Disasters Based on Analytic Hierarchy Process and Entropy Weight Method

ЧИ ПОТРІБНЕ УКРАЇНІ СПЕЦИФІЧНЕ РЕГУЛЮВАННЯ ВИКОРИСТАННЯ ГЕНЕТИЧНОЇ ІНФОРМАЦІЇ ДЛЯ ОЦІНКИ РИЗИКІВ У СТРАХУВАННІ?

Introduction. The article examines the specifics and role of genetic information for insurance risk assessment in the life insurance market in Ukraine. Problem Statement. The insurance market developments of different countries are increasingly characterized by the adoption of specific regulations regarding the features and conditions of use of genetic information. Therefore, the issue of regulating the rights and obligations of all participants in insurance relations regarding the use of such information of future owners of insurance policies for underwriting in insurance requires a comprehensive solution taking into account the interests of all parties. The purpose of the research is to evaluate the necessity of application of specific regulations on the insurance market, specifically in the field of using genetic data for insurance purposes. Methods. The sources of materials were scientific publications, analytical studies, as well as legislation in the field of regulation of the use of genetic information for the assessment of insurance risks. The research paper used the following empirical methods, such as analysis, synthesis, grouping, description, comparison, theoretical generalization. Results. The results show that currently some risks exist in Ukraine: firstly, the genetic discrimination, since most life insurance companies are interested in the genetic information of policyholders, and can request it from any third parties: therefore, there is a possibility of using it to assess insurance risk; secondly, an information asymmetry, which is a consequence of greater awareness of insurance companies about the insurance risks than that of the policyholders. After all, policyholders may not inform the insurance company about all the genetic data (for example, the results of genetic studies) that describe their genetic predisposition to future changes in health. Conclusions. This study substantiated factors which confirm the relevance of introducing legislative regulation regarding the use of genetic information (including the results of genetic analyzes) for underwriting in insurance.

Uniform asymptotics for a risk model with constant force of interest and a random number of delayed claims

Consider an insurance risk model with constant force of interest under the assumption that an immediate claim is accompanied by a random number of delayed claims. In the presence of heavy tails on claim distributions and dependence structures among modelling components, we study the uniform asymptotics for an insurer's ruin-related quantities, where the uniformity holds for all time horizons varying in a relevant interval.

Actuarial Risk Management Practices and Firm Performance: The Mediating Role of E-Service Innovation

Actuarial Risk Management Practices and Firm Performance: The Mediating Role of E-Service Innovation

Multivariate regularly varying insurance and financial risks in multidimensional risk models

Abstract Multivariate regular variation is a key concept that has been applied in finance, insurance, and risk management. This paper proposes a new dependence assumption via a framework of multivariate regular variation. Under the condition that financial and insurance risks satisfy our assumption, we conduct asymptotic analyses for multidimensional ruin probabilities in the discrete-time and continuous-time cases. Also, we present a two-dimensional numerical example satisfying our assumption, through which we show the accuracy of the asymptotic result for the discrete-time multidimensional insurance risk model.

Capturing key interdependences among supply chain disruptions and mitigation strategies to enhance firm performance

PurposeThis paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.Design/methodology/approachThis study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).FindingsThe results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.Originality/valueOur results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

A Study of Insurance Company Underwriting Risk Based on EWM and TOPSIS Methods

This study proposes a risk assessment model called CPSF, which aims to assess insurance risk in areas where extreme weather is frequent. The model adopts a multidimensional and comprehensive assessment system, considering climate change, demographic and socio-economic, and financial conditions as the main factors, and constructs corresponding assessment indicators. The study establishes a scientific and reliable risk assessment system through the steps of data source, data pre-processing, indicator establishment, weight determination, and comprehensive assessment. By applying the model to two regions, namely, Southeast China and Southeast United States, where extreme weather often occurs, the insurance risk situation of these regions is analyzed and the future trend is predicted, which provides an important reference for insurance companies and policy making.

Navigating Insurance Challenges: A Decision Tree Model for Insurance Pricing and Risk Strategy

Amid escalating natural disasters intensified by climate change, accurately assessing risks and determining insurance rates in disaster-prone areas present significant challenges to the insurance industry, impacting the underwriting decisions and strategies correspondingly. This article employs a frequency-loss degree framework to quantify meteorological disaster risk as the Annual Average Loss (AAL), which was classified into three categories (severe, moderate, and mild) further. Utilizing a Poisson Distribution to process extreme event data and integrating it with data from typical years into a comprehensive prediction model, this approach facilitates enhanced risk assessment and premium estimation. A decision tree model was further established to explore insurance institution’s underwriting decisions and risk strategies on the basis of monetary benefits and mental utility respectively, highlighted by case studies in Australia and the Philippines. The findings underscore the necessity for insurers to incorporate advanced decision-making tools taking utility factors into account, to navigate the complexities of risk and pricing in vulnerable areas efficiently. A tendency was observed among insurers to prefer conservative strategies in medium-risk areas, like the Philippines, over aggressive underwriting in high-risk zones, such as Western Australia. This suggests further research into expanding the model's applicability and delving into the evolving influence of climate change on the insurance industry.

Risk Assessment and Insurance Underwriting Decision Model: A Case Study of Gansu Province and Florida

Extreme weather is driving up economic losses and insurance costs, underscoring the urgency for enhanced disaster risk management and policy reform for sustainability. This study presents a novel Risk Assessment Model for analyzing insurance underwriting risk in specific regions, driven by the increasing economic losses and insurance costs due to extreme weather. The model integrates a comprehensive evaluation system with four primary and twelve secondary indices, applying the Entropy Weighting Method (EWH) and Analytic Hierarchy Process (AHP) to allocate weights and quantify risk (Q_unfavourable) effectively. It also adopts the As Low as Reasonably Practicable (ALARP) principle for setting underwriting decision thresholds, based on risk and profitability. Case studies from Gansu Province, China, and Florida, USA, validate the model's practicality and its potential to enhance insurers' underwriting strategies and policy decisions across varied regions, marking a significant improvement in underwriting risk management and strategic insurance policy formulation.

Insurance Risk Assessment Model based on Game Theory-Combined Weighting-TOPSIS

With the increasingly severe climate change and intensifying severity of weather events and natural disasters, ensuring the sustainable development of the property insurance industry becomes particularly important. The policy distribution of insurance companies has become a key issue. This paper proposes a novel Game Theory-Combined Weighting-TOPSIS model, which weights indicators using The Entropy Weight Method and the CRITIC method, combines the weights of the former two through game-based combined weighting, and leverages the advantages of the TOPSIS method in handling multi-criteria decision-making problems. Among them, taking China as an example and applying the model, it is found that southeastern China and coastal areas have higher scores and are suitable for insurance. Through this approach, we can scientifically and reasonably assess the risk levels in different regions, provide decision support for property insurance companies to optimize policy distribution, and promote the healthy development of the insurance industry.

Insurance payor status and risk of major adverse cardiovascular and cerebrovascular events after metabolic and bariatric surgery

BackgroundPatients with Medicare/Medicaid insurance receive metabolic and bariatric surgery (MBS) at lower rates than privately insured (PI) patients. Although studies on some surgical procedures report that Medicare/Medicaid insurance confers increased postoperative complication rates and a longer length of stay, less is known about these outcomes after MBS. Among often-feared postoperative complications are major adverse cardiovascular and cerebrovascular events (MACEs). Although these events are rare after MBS, they have a significant impact on morbidity and mortality. ObjectivesThis study aimed to examine the effect of insurance payor status on MACEs after MBS. SettingThe Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS). MethodsHCUP-NIS was queried for cases including sleeve gastrectomy or Roux-en-Y gastric bypass between 2012 and 2019. Bivariate associations between patient-level factors and MACEs were assessed via Rao-Scott χ2 tests. Adjusted and unadjusted risks of insurance payor status for MACEs were evaluated using logistic regression. ResultsIncidence of MACEs was higher in both Medicare (.75% versus .11%; P < .001) and Medicaid (.15% versus .11%; P < .001) groups than in the PI group. After adjustment for high-risk demographics, high-risk co-morbidities, socioeconomic variables, and hospital factors, insurance status of Medicare (odds ratio [OR]: 1.60, 95% confidence interval [CI]: 1.23, 2.07; P = .0026) or Medicaid (OR: 1.55, 95% CI: 1.12, 2.16; P = .0026) remained an independent risk factor for MACEs. ConclusionsOur findings underscore the significance of Medicaid/Medicare payor status as an independent predictor of postoperative MACEs in MBS. The results of this study can have a significant impact on deepening our understanding of socioeconomic and health system–related issues that can be targeted to improve outcomes in both MBS and other surgical specialties.

Factors Influencing Underwriting Performance of the Life and Non-Life Insurance Markets in South Africa: Exploring for Complementarities, Nonlinearities, and Thresholds

ABSTRACT Underwriting is crucial for insurers’ performance and sustainability, yet empirical evidence on factors influencing its performance has received limited attention. Therefore, this study investigates this phenomenon considering South Africa’s life and non-life insurance sectors between 2013–2019. Using the generalized method of moments and the bootstrap quantile regression techniques for analysis, the findings show that insurance size, market share, and investment income significantly and positively impact underwriting performance. However, premium growth weakens the underwriting performance of both sectors. Also, non-life insurance shows a positive relationship for reinsurance, while life insurers present an inverse relationship. Underwriting risk negatively impacts non-life performance across quantiles, while life insurer risk was negative in higher quantiles. More so, initial solvency levels weaken underwriting performance, while extreme increase improves it, implying a direct U-shaped relationship. Hence, highly solvent insurers are more likely to succeed in underwriting operations. Additionally, market share complements return on assets in promoting underwriting performance. Policy recommendations are discussed.