The Experts below are selected from a list of 9021 Experts worldwide ranked by ideXlab platform
Mario V Wuthrich - One of the best experts on this subject based on the ideXlab platform.
-
boosting poisson regression models with Telematics car driving data
2021Co-Authors: Guangyuan Gao, He Wang, Mario V WuthrichAbstract:With the emergence of Telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with Telematics car driving information. In this paper, we propose two data-driven neural network approaches that process Telematics car driving data to complement classical actuarial pricing with a driving behavior risk factor from Telematics data. Our neural networks simultaneously accommodate feature engineering and regression modeling which allows us to integrate Telematics car driving data in a one-step approach into the claim frequency regression models. We conclude from our numerical analysis that both classical actuarial risk factors and Telematics car driving data are necessary to receive the best predictive models. This emphasizes that these two sources of information interact and complement each other.
-
boosting poisson regression models with Telematics car driving data
2020Co-Authors: Guangyuan Gao, He Wang, Mario V WuthrichAbstract:With the emergence of Telematics car driving data, insurance companies start to boost classical actuarial regression models for claim frequency prediction. In this paper, we propose two data-driven neural network approaches that process Telematics car driving data to construct driving behavior risk factors. Neural networks simultaneously accommodate feature engineering and regression modeling. We conclude in our numerical analysis that both classical actuarial risk factors and Telematics car driving data is necessary to receive the best predictive models. This indicates that these two sources of information interact and complement each other.
Guangyuan Gao - One of the best experts on this subject based on the ideXlab platform.
-
boosting poisson regression models with Telematics car driving data
2021Co-Authors: Guangyuan Gao, He Wang, Mario V WuthrichAbstract:With the emergence of Telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with Telematics car driving information. In this paper, we propose two data-driven neural network approaches that process Telematics car driving data to complement classical actuarial pricing with a driving behavior risk factor from Telematics data. Our neural networks simultaneously accommodate feature engineering and regression modeling which allows us to integrate Telematics car driving data in a one-step approach into the claim frequency regression models. We conclude from our numerical analysis that both classical actuarial risk factors and Telematics car driving data are necessary to receive the best predictive models. This emphasizes that these two sources of information interact and complement each other.
-
boosting poisson regression models with Telematics car driving data
2020Co-Authors: Guangyuan Gao, He Wang, Mario V WuthrichAbstract:With the emergence of Telematics car driving data, insurance companies start to boost classical actuarial regression models for claim frequency prediction. In this paper, we propose two data-driven neural network approaches that process Telematics car driving data to construct driving behavior risk factors. Neural networks simultaneously accommodate feature engineering and regression modeling. We conclude in our numerical analysis that both classical actuarial risk factors and Telematics car driving data is necessary to receive the best predictive models. This indicates that these two sources of information interact and complement each other.
Helmut Krcmar - One of the best experts on this subject based on the ideXlab platform.
-
open security issues in german healthcare Telematics
2010Co-Authors: Ali Sunyaev, Jan Marco Leimeister, Helmut KrcmarAbstract:Developments in German healthcare Telematics aim at connecting existing information systems of various service providers and health insurers via a common network. Such a linking of different systems and infrastructure elements creates a complex situation that has to deal with high priority requirements for data security, data safety, and data integrity as it concerns sensitive data such as personal medical information or administrative operational data. This paper provides a security analysis of the German healthcare Telematics infrastructure under development and derives security measures to overcome the identified vulnerabilities. This analysis of open issues in the security concept of German healthcare Telematics might be helpful for both future research and practice in healthcare information systems security
-
integration of patient health portals into the german healthcare Telematics infrastructure
2009Co-Authors: Sebastian Duennebeil, Christian Mauro, Ali Sunyaev, Jan Marco Leimeister, Helmut KrcmarAbstract:In this paper we describe a generic model of a patient health portal, which is suitable to implement patient access to the evolving German healthcare Telematics infrastructure. The portal uses the Telematics as a communication infrastructure to ensure the concise and secure exchange of medical data between professional medical personnel and patients. We aim at providing patients an application platform model for using and enhancing their data by processing or extending them with medical services offered via the internet or with local medical appliances. We show that a) specific functionalities (such as data import/export from/to the Telematics) for patient health portals can be derived from the legal foundation in the German law b) the portal is conceptually suited to provide a link between the public health information infrastructure and other (maybe commercial) applications in the e-health environment via Personal Health Records (PHR) and c) patients’ rights can be mapped with a common data model.
Richard Welch - One of the best experts on this subject based on the ideXlab platform.
-
process innovation with disruptive technology in auto insurance lessons learned from a smartphone based insurance Telematics initiative
2015Co-Authors: Jens Ohlsson, Peter Handel, Richard WelchAbstract:Insurance Telematics or usage-based insurance (UBI) is a potential game-changer for the insurance industry, especially for innovating auto-insurance. In order to achieve and sustain UBI for auto insurance, insurers are called upon to innovate the marketing and sales processes of the UBI product, as well as related processes such as risk assessment and price calculation. In this chapter, we demonstrate the insurer’s process innovation with smartphone-based insurance Telematics, using the example of the “If SafeDrive” campaign which was commercially conducted by the insurer If P & C in Sweden. The results show that although disruptive technology can trigger process innovation, such innovation cannot succeed and be sustained without fundamental changes in a company’s structure, business model and business strategy. We further propose a capability layer model for understanding the insurer’s process innovation behaviour. This chapter provokes the critical thinking with regard to the exploration and exploitation of disruptive technology into process innovation. Further, the chapter contributes new knowledge to the research of process innovation with disruptive digital technologies.
-
insurance Telematics opportunities and challenges with the smartphone solution
2014Co-Authors: Peter Handel, Isaac Skog, Johan Wahlstrom, Farid Bonawiede, Richard Welch, Jens Ohlsson, Martin OhlssonAbstract:Smartphone-based insurance Telematics or usage based insurance is a disruptive technology which relies on insurance premiums that reflect the risk profile of the driver; measured via smartphones with appropriate installed software. A survey of smartphone-based insurance Telematics is presented, including definitions; Figure-of-Merits (FoMs), describing the behavior of the driver and the characteristics of the trip; and risk profiling of the driver based on different sets of FoMs. The data quality provided by the smartphone is characterized in terms of Accuracy, Integrity, Availability, and Continuity of Service. The quality of the smartphone data is further compared with the quality of data from traditional in-car mounted devices for insurance Telematics, revealing the obstacles that have to be combated for a successful smartphone-based installation, which are the poor integrity and low availability. Simply speaking, the reliability is lacking considering the smartphone measurements. Integrity enhancement of smartphone data is illustrated by both second-by-second lowlevel signal processing to combat outliers and perform integrity monitoring, and by trip-based map-matching for robustification of the recorded trip data. A plurality of FoMs are described, analyzed and categorized, including events and properties like harsh braking, speeding, and location. The categorization of the FoMs in terms of Observability, Stationarity, Driver influence, and Actuarial relevance are tools for robust risk profiling of the driver and the trip. Proper driver feedback is briefly discussed, and rule-of-thumbs for feedback design are included. The work is supported by experimental validation, statistical analysis, and experiences from a recent insurance Telematics pilot run in Sweden.
Montserrat Guillen - One of the best experts on this subject based on the ideXlab platform.
-
improving automobile insurance ratemaking using Telematics incorporating mileage and driver behaviour data
2019Co-Authors: Mercedes Ayuso, Montserrat Guillen, Jens Perch NielsenAbstract:We show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. This paper provides a methodology including a transition pricing transferring knowledge and experience that the company already had before the Telematics data arrived to the new world including Telematics information.
-
the transition towards semi autonomous vehicle insurance the contribution of usage based data
2018Co-Authors: Montserrat Guillen, Ana M PerezmarinAbstract:The use of advanced driver assistance systems and the transition towards semi-autonomous vehicles are expected to contribute to a lower frequency of motor accidents and to have a significant impact for the automobile insurance industry, as rating methods must be revised to ensure that risks are correctly measured. We analyze Telematics information and usagebased insurance research to identify the effect of driving patterns on the risk of accident. This is used as a starting point for addressing risk quantification and safety for vehicles than can control speed. Here we estimate the effect of excess speed on the risk of accidents with a real Telematics data set. We show scenarios for a reduction of speed limit violations and the consequent decrease in the expected number of accident claims. If excess speed could be eliminated, then the expected number of accident claims could be reduced to half of its initial value, applying the average conditions of our data. As a consequence, insurance premiums also diminish.
