Insurance Risk Assessment Research Articles

A Modeling Study of Insurance and Real Estate Risk Assessment in the Context of Global Climate Change

A Modeling Study of Insurance and Real Estate Risk Assessment in the Context of Global Climate Change

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

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

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

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.

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.

Fraud risk assessment in car insurance using claims graph features in machine learning

The article proposes a process for claims assessment in car insurance, which makes it possible to calculate the fraud rate on the annual set of claims using a reduced set of attributes and graph vertex properties. This approach improves the security of insurance companies’ assets against fraudulent attacks. A method for constructing a claims graph and extracting additional features from it for evaluation is described. It is shown that in order to build a graph, it is not necessary to have data on the connection of the claim participants. Two tests were carried out on a real opensource datasets with labelling of fraudulent cases. The results of the first one show the increase in classification metrics when using attributes obtained from the graph. The application of the proposed approach resulted in doubling the area under the Precision-Recall curve. The experimental results demonstrated high quality metrics for fraud detection, with a Recall rate of 83.33% and a Specificity rate of 91.05%. The second test confirmed the possibility of determining the insurance fraud level based on decision rule, which includes the condition of claims being connected to each other. The rule is able to detect claim groups with a high concentration of fraud, in which every second participant is a fraudster.

Enhanced Insurance Risk Assessment using Discrete Four-Variate Sarmanov Distributions and Generalized Linear Models

This research paper investigated multivariate risk assessment in insurance, focusing on four risks of a singular person and their interdependence. This research examined various risk indicators in non-life insurance which was under-writing for organizations with clients that purchase several non-life insurance policies. The risk indicators are probabilities of frequency claims and correlations of two risk lines. The closed forms of probability mass functions evaluated the probabilities of frequency claims. Three generalized linear models of four-variate Sarmanov distributions were proposed for marginals, incorporating various characteristics of policyholders using explanatory variables. All three models were discrete models that were a combination of Poisson and Gamma distributions. Some properties of four-variate Sarmanov distributions were explicitly shown in closed forms. The dataset spanned a decade and included the exposure of each individual to risk over an extended period. The correlations between the two risk types were evaluated in several statistical ways. The parameters of the three Sarmanov model distributions were estimated using the maximum likelihood method, while the results of the three models were compared with a simpler four-variate negative binomial generalized linear model. The research findings showed that Model 3 was the most accurate of all three models since the AIC and BIC were the lowest. In terms of the correlation, it was found that the risk of claiming auto insurances was related to claiming home insurances. Model 1 could be used for the risk assessment of an insurance company that had customers who held multiple types of insurances in order to predict the risks that may occur in the future. When the insurance company can forecast the risks that may occur in the future, the company will be able to calculate appropriate insurance premiums.

Climate risk assessment in insurance: A USA and Africa Review

Climate risk assessment in insurance is a critical component of understanding and mitigating the impacts of climate change on the insurance industry. This paper provides a comprehensive review of climate risk assessment practices in the insurance sector, focusing on both the United States (USA) and Africa. The study explores the unique challenges and opportunities each region faces in the context of climate-related risks. In the USA, a mature and well-established insurance market grapples with the increasing frequency and severity of climate-related events. Hurricanes, wildfires, and floods have become more prevalent, necessitating a thorough evaluation of risk models and actuarial practices. The paper delves into how American insurers are adapting to these challenges through advancements in technology, data analytics, and collaboration with climate scientists. The role of regulatory frameworks and government initiatives in shaping the industry's response is also explored. Contrastingly, the African insurance landscape faces its own set of challenges in the wake of climate change. The continent is particularly vulnerable to extreme weather events, such as droughts and storms, impacting both agriculture and infrastructure. The paper investigates the unique considerations in climate risk assessment for African insurers, including the need for localized data, community-based resilience initiatives, and the potential for innovative insurance products that cater to the specific needs of the population. A comparative analysis highlights the disparities in resources, infrastructure, and regulatory frameworks between the two regions. It also underscores the importance of international collaboration in sharing best practices and facilitating the transfer of knowledge and technology. The paper emphasizes the role of insurers not only as financial risk mitigators but also as key stakeholders in promoting climate resilience and sustainability. This paper provides valuable insights into the evolving landscape of climate risk assessment in insurance, offering a nuanced understanding of the challenges faced by the USA and Africa. The findings aim to inform policymakers, insurers, and other stakeholders, fostering a proactive approach to climate resilience in the global insurance industry.

Abnormal Driving Detection using GPS Data.

Given a GPS dataset comprising driving records captured at one-second intervals, this research addresses the challenge of Abnormal Driving Detection (ADD). The study introduces an integrated approach that leverages data preprocessing, dimensionality reduction, and clustering techniques. Speed Over Ground (SOG), Course Over Ground (COG), longitude (lon), and latitude (lat) data are aggregated into minute-level segments. We use Singular Value Decomposition (SVD) to reduce dimensionality, enabling K-means clustering to identify distinctive driving patterns. Results showcase the methodology's effectiveness in distinguishing normal from abnormal driving behaviors, offering promising insights for driver safety, insurance risk assessment, and personalized interventions.

Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning

A hallmark of information technology use in disaster management is the wide adoption of complex information systems for risk assessment, portfolio management, and ratemaking in catastrophe insurance. Whereas the importance of catastrophe insurance to disaster preparedness is beyond dispute, catastrophe insurers are increasingly reckoning with the potential impact of inequality in insurance practices. Historically, the presence of inequalities in insurance, from redlining to pricing disparity, has had a devastating impact on minority communities. Even recently, people living in predominantly African American communities can still be charged more for the same insurance coverage than people living in other communities. Whereas the fairness of insurance ratemaking is studied in general, we identify a unique challenge for catastrophe insurance that sets it apart from other lines of insurance. Drawing upon the recent advances in machine learning for fair data valuation, we reveal striking connections between the two seemingly unrelated problems and lean on insights from machine learning to study the fairness of ratemaking methods for catastrophe insurance. Our results indicate the potential existence of disparate impact against minorities across existing methods, pointing to a unique solution that can satisfy a few commonly assumed properties of fair ratemaking for catastrophe insurance.

Penalty Points for Violation of Traffic Rules versus Motor Insurance Premium. Analysis of New Legal Solutions

An amendment to the Polish Road Traffic Code introduced a possibility for the Insurance Guarantee Fund and insurance companies (offering motor liability and motor hull insurance) to access information in the Central Drivers Registry relating inter alia information concerning road traffic offences and to penalty points imposed as a result of committing such offences. Insurance companies may consult the Registry with the Funds assistance. At the same time, under the amended Polish Insurance and Reinsurance Act, insurance undertakings may process those data (i.e. driving offences or violations of road traffic regulations and penalty points issued as a result thereof) only for the purpose of insurance risk assessment and pricing with a view of concluding motor insurance contracts (excluding carrier's liability insurance). Similar solutions have been adopted in other countries, including EU Member States. The article is an attempt at explaining and conducting an initial evaluation of the new mechanisms, as well as at answering a question about possible impact thereof upon motor insurance premiums.

Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants

We derive consistency, asymptotic normality, and standard error estimation for the tail conditional allocation, also known as the marginal expected shortfall, under minimal conditions and thus geared toward widest applicability. These advances have become possible due to a newly developed technique that hinges on compound sums of concomitants. An insurance inspired numerical study has been designed to illustrate the performance of the obtained results.

Special Issue “Quantitative Risk Assessment in Life, Health and Pension Insurance”

The high volatility in financial markets, together with the ultra-low interest rates environment and the increased expectation of life, constitute serious threats for providers of long-term investment guarantees and lifelong benefits [...]

Inference for the tail conditional allocation: large sample properties, insurance risk assessment, and compound sums of concomitants

Inference for the tail conditional allocation: large sample properties, insurance risk assessment, and compound sums of concomitants

Evaluation and Interpretation of Driving Risks: Automobile Claim Frequency Modeling with Telematics Data

With the development of vehicle telematics and data mining technology, usage-based insurance (UBI) has aroused widespread interest from both academia and industry. The extensive new driving behavior features make it possible to further understand the risks of insured vehicles, but pose challenges in the identification and interpretation of important ratemaking factors. This study therefore analyzes telematics data to predict UBI claim frequency. Specifically, based on 75 features theoretically related to driving risk, we employ both Poisson regression and several machine learning models to model claim frequency. After selecting the best model, we analyze feature importance, feature effects, and the contribution of each feature to the prediction from an actuarial perspective and illustrate the specific impact of driving behavior features through some visualization tools. The methodology and conclusions of this study not only provide a more accurate prediction, but also improve the practical value of machine learning in insurance risk assessment. This empirical study, based on the telematics data of policyholders in mainland China, shows that XGBoost greatly outperforms the traditional models and detects the complex relationships in the nonlinear and interactive effects of various features fully. In addition, according to the contribution of each feature, we develop a customer segmentation plan to distinguish high-risk drivers, which can serve as a guideline for insurance companies to achieve ex-ante risk management.

Comparative Evaluation of Probability Distribution Models of Flood flow in Lower Niger Basin

The choice of optimum probability distribution model that would accurately simulate flood discharges at a particular location or region has remained a challenging problem to water resources engineers. In practice, several probability distributions are evaluated, and the optimum distribution is then used to establish the quantile - probability relationship for planning, design and management of water resources systems, risk assessment in flood plains and flood insurance. This paper presents the evaluation of five probability distributions models: Gumbel (EV1), 2-parameter lognormal (LN2), log pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value (GEV) using the method of moments (MoM) for parameter estimation and annual maximum series of five hydrological stations in the lower Niger River Basin in Nigeria. The choice of optimum probability distribution model was made on five statistical goodness – of – fit measures; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR), and probability plot correlation coefficient (PPCC). The results show that GEV is the optimum distribution in 3 stations, and LP3 in 2 stations. On the overall GEV is the best – fit distribution, seconded by PR3 and thirdly, LP3. Furthermore, GEV simulated discharges were in closest agreement with the observed flood discharges. It is recommended that GEV, PR3 and LP3 should be considered in the final selection of optimum probability distribution model in Nigeria.

Comparative Evaluation of Probability Distribution Models of Flood flow in Lower Niger Basin

The choice of optimum probability distribution model that would accurately simulate flood discharges at a particular location or region has remained a challenging problem to water resources engineers. In practice, several probability distributions are evaluated, and the optimum distribution is then used to establish the quantile - probability relationship for planning, design and management of water resources systems, risk assessment in flood plains and flood insurance. This paper presents the evaluation of five probability distributions models: Gumbel (EV1), 2-parameter lognormal (LN2), log pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value (GEV) using the method of moments (MoM) for parameter estimation and annual maximum series of five hydrological stations in the lower Niger River Basin in Nigeria. The choice of optimum probability distribution model was made on five statistical goodness – of – fit measures; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR), and probability plot correlation coefficient (PPCC). The results show that GEV is the optimum distribution in 3 stations, and LP3 in 2 stations. On the overall GEV is the best – fit distribution, seconded by PR3 and thirdly, LP3. Furthermore, GEV simulated discharges were in closest agreement with the observed flood discharges. It is recommended that GEV, PR3 and LP3 should be considered in the final selection of optimum probability distribution model in Nigeria.

Déjà Vu - A New Coronavirus Challenge.

Déjà Vu - A New Coronavirus Challenge.

Health insurance risk assessment using cognitive mapping and multiple‐criteria decision analysis

AbstractHealth insurance risk analysis is crucial to the development of the insurance industry, allowing insurance companies to anticipate potential losses in health insurance's inverted product life cycle. The health insurance risk evaluation process is, however, carried out in various ways, which limits the application of transparency, fairness, and justice principles in the calculation of risk rewards. By combining cognitive mapping and measuring attractiveness by a categorical‐based evaluation technique (i.e., MACBETH, a multiple‐criteria decision analysis technique), this study sought to create a nonparametric and distinctive decision support system for individual private health insurance risk analysis. The proposed system facilitates greater transparency in the calculation of health insurance risk rewards. The results of a real‐world application of this system were analyzed and discussed in face‐to‐face group meetings with independent experts who work for two of the largest insurance companies in Portugal, thereby incorporating realism into the proposed evaluation mechanism. The advantages and limitations of this assessment framework are also discussed.