Research and Event Control on Risk Factors of Auto Insurance Claim

2021 ◽  
Author(s):  
Ma Xin ◽  
Hang Li ◽  
Yingnan Liu
Keyword(s):  
Risk Factors ◽  
Insurance Claim ◽  
Auto Insurance

Using claims prediction model for road safety evaluation

2001 ◽  
Vol 28 (5) ◽  
pp. 804-812 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed
Keyword(s):  
Prediction Model ◽  
Road Safety ◽  
Claims Data ◽  
Safety Evaluation ◽  
Safety Analysis ◽  
Insurance Claims ◽  
Insurance Claim ◽  
Auto Insurance ◽  
Improvement Programs ◽  
Traffic Collision

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.

scholarly journals Modeling the Frequency of Auto Insurance Claims by Means of Poisson and Negative Binomial Models

2015 ◽  
Vol 62 (2) ◽  
pp. 151-168 ◽  
Author(s):  
Mihaela David ◽  
Dănuţ-Vasile Jemna
Keyword(s):  
Risk Factors ◽  
Count Data ◽  
Negative Binomial ◽  
Information Criteria ◽  
Count Data Models ◽  
Auto Insurance ◽  
Insurance Portfolio ◽  
Negative Binomial Models ◽  
Degree Of Risk ◽  
Binomial Models

Abstract Within non-life insurance pricing, an accurate evaluation of claim frequency, also known in theory as count data, represents an essential part in determining an insurance premium according to the policyholder’s degree of risk. Count regression analysis allows the identification of the risk factors and the prediction of the expected frequency of claims given the characteristics of policyholders. The aim of this paper is to verify several hypothesis related to the methodology of count data models and also to the risk factors used to explain the frequency of claims. In addition to the standard Poisson regression, Negative Binomial models are applied to a French auto insurance portfolio. The best model was chosen by means of the log-likelihood ratio and the information criteria. Based on this model, the profile of the policyholders with the highest degree of risk is determined

scholarly journals Estimating Major Risk Factor Relativities in Rate Filings Using Generalized Linear Models.

2021 ◽  
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Predictive Modeling ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Auto Insurance ◽  
Insurance Pricing ◽  
Modeling Techniques ◽  
Insurance Rate ◽  
Insurance Rates

Predictive modeling is a key technique in auto insurance rate-making and the decision-making involved in the review of rate filings. Unlike an approach based on hypothesis testing, the results from predictive modeling not only serve as statistical evidence for decision-making, they also discover relationships between a response variable and predictors. In this work, we study the use of predictive modeling in auto insurance rate filings. This is a typical area of actuarial practice involving decision-making using industry loss data. The aim of this study was to offer some general guidelines for using predictive modeling in regulating insurance rates. Our study demonstrates that predictive modeling techniques based on generalized linear models (GLMs) are suitable in auto insurance rate filings review. The GLM relativities of major risk factors can serve as the benchmark of the same risk factors considered in auto insurance pricing.

scholarly journals Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation

Risks ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 126
Author(s):  
Shengkun Xie
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Variable Importance ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Auto Insurance ◽  
Rate Regulation ◽  
Insurance Pricing ◽  
Learning Techniques ◽  
Insurance Rate

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model explainability may be essential to meet insurance pricing transparency for rate regulation purposes. This requirement may imply the need for estimating or evaluating the variable importance when complicated models are used. Furthermore, from both rate-making and rate-regulation perspectives, it is critical to investigate the impact of major risk factors on the response variables, such as claim frequency or claim severity. In this work, we consider the modelling problems of how claim counts, claim amounts and average loss per claim are related to major risk factors. ANN models are applied to meet this goal, and variable importance is measured to improve the model’s explainability due to the models’ complex nature. The results obtained from different variable importance measurements are compared, and dominant risk factors are identified. The contribution of this work is in making advanced mathematical models possible for applications in auto insurance rate regulation. This study focuses on analyzing major risks only, but the proposed method can be applied to more general insurance pricing problems when additional risk factors are being considered. In addition, the proposed methodology is useful for other business applications where statistical machine learning techniques are used.

scholarly journals Estimating Major Risk Factor Relativities in Rate Filings Using Generalized Linear Models

2018 ◽  
Vol 6 (4) ◽  
pp. 84 ◽  
Author(s):  
Shengkun Xie ◽  
Anna Lawniczak
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Predictive Modeling ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Auto Insurance ◽  
Insurance Pricing ◽  
Modeling Techniques ◽  
Insurance Rate ◽  
Insurance Rates

Predictive modeling is a key technique in auto insurance rate-making and the decision-making involved in the review of rate filings. Unlike an approach based on hypothesis testing, the results from predictive modeling not only serve as statistical evidence for decision-making, they also discover relationships between a response variable and predictors. In this work, we study the use of predictive modeling in auto insurance rate filings. This is a typical area of actuarial practice involving decision-making using industry loss data. The aim of this study was to offer some general guidelines for using predictive modeling in regulating insurance rates. Our study demonstrates that predictive modeling techniques based on generalized linear models (GLMs) are suitable in auto insurance rate filings review. The GLM relativities of major risk factors can serve as the benchmark of the same risk factors considered in auto insurance pricing.

scholarly journals Estimating Major Risk Factor Relativities in Rate Filings Using Generalized Linear Models.

2021 ◽  
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Predictive Modeling ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Auto Insurance ◽  
Insurance Pricing ◽  
Modeling Techniques ◽  
Insurance Rate ◽  
Insurance Rates

Predictive modeling is a key technique in auto insurance rate-making and the decision-making involved in the review of rate filings. Unlike an approach based on hypothesis testing, the results from predictive modeling not only serve as statistical evidence for decision-making, they also discover relationships between a response variable and predictors. In this work, we study the use of predictive modeling in auto insurance rate filings. This is a typical area of actuarial practice involving decision-making using industry loss data. The aim of this study was to offer some general guidelines for using predictive modeling in regulating insurance rates. Our study demonstrates that predictive modeling techniques based on generalized linear models (GLMs) are suitable in auto insurance rate filings review. The GLM relativities of major risk factors can serve as the benchmark of the same risk factors considered in auto insurance pricing.

Elevated level of circulatory sTLT1 induces inflammation through SYK/MEK/ERK signalling in coronary artery disease

2019 ◽  
Vol 133 (22) ◽  
pp. 2283-2299
Author(s):  
Apabrita Ayan Das ◽  
Devasmita Chakravarty ◽  
Debmalya Bhunia ◽  
Surajit Ghosh ◽  
Prakash C. Mandal ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Chronic Inflammation ◽  
Ex Vivo ◽  
Human Subjects ◽  
Critical Role ◽  
Atherosclerotic Cardiovascular Disease ◽  
Tnf Α ◽  
Artery Disease

Abstract The role of inflammation in all phases of atherosclerotic process is well established and soluble TREM-like transcript 1 (sTLT1) is reported to be associated with chronic inflammation. Yet, no information is available about the involvement of sTLT1 in atherosclerotic cardiovascular disease. Present study was undertaken to determine the pathophysiological significance of sTLT1 in atherosclerosis by employing an observational study on human subjects (n=117) followed by experiments in human macrophages and atherosclerotic apolipoprotein E (apoE)−/− mice. Plasma level of sTLT1 was found to be significantly (P<0.05) higher in clinical (2342 ± 184 pg/ml) and subclinical cases (1773 ± 118 pg/ml) than healthy controls (461 ± 57 pg/ml). Moreover, statistical analyses further indicated that sTLT1 was not only associated with common risk factors for Coronary Artery Disease (CAD) in both clinical and subclinical groups but also strongly correlated with disease severity. Ex vivo studies on macrophages showed that sTLT1 interacts with Fcɣ receptor I (FcɣRI) to activate spleen tyrosine kinase (SYK)-mediated downstream MAP kinase signalling cascade to activate nuclear factor-κ B (NF-kB). Activation of NF-kB induces secretion of tumour necrosis factor-α (TNF-α) from macrophage cells that plays pivotal role in governing the persistence of chronic inflammation. Atherosclerotic apoE−/− mice also showed high levels of sTLT1 and TNF-α in nearly occluded aortic stage indicating the contribution of sTLT1 in inflammation. Our results clearly demonstrate that sTLT1 is clinically related to the risk factors of CAD. We also showed that binding of sTLT1 with macrophage membrane receptor, FcɣR1 initiates inflammatory signals in macrophages suggesting its critical role in thrombus development and atherosclerosis.

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