2,505 research outputs found
From Physical to Cyber: Escalating Protection for Personalized Auto Insurance
Nowadays, auto insurance companies set personalized insurance rate based on
data gathered directly from their customers' cars. In this paper, we show such
a personalized insurance mechanism -- wildly adopted by many auto insurance
companies -- is vulnerable to exploit. In particular, we demonstrate that an
adversary can leverage off-the-shelf hardware to manipulate the data to the
device that collects drivers' habits for insurance rate customization and
obtain a fraudulent insurance discount. In response to this type of attack, we
also propose a defense mechanism that escalates the protection for insurers'
data collection. The main idea of this mechanism is to augment the insurer's
data collection device with the ability to gather unforgeable data acquired
from the physical world, and then leverage these data to identify manipulated
data points. Our defense mechanism leveraged a statistical model built on
unmanipulated data and is robust to manipulation methods that are not foreseen
previously. We have implemented this defense mechanism as a proof-of-concept
prototype and tested its effectiveness in the real world. Our evaluation shows
that our defense mechanism exhibits a false positive rate of 0.032 and a false
negative rate of 0.013.Comment: Appeared in Sensys 201
Analyzing the Vehicle Insurance Ecosystem in Estonia using Actor-Network Theory
This paper focuses on analyzing the vehicle insurance ecosystem in Estonia that integrated Actor Network Theory (ANT) as theoretical lens to investigate potential transformations in the said ecosystem and to gauge the influence of insurance-oriented telematics technology. The paper combined an interpretive approach applied to a case study method. The study offers insights which are useful to facilitate the alignment of insurance-oriented telematics technology and its practical implementation within social systems. This study is one of the first to examine insurance-oriented telematics technology as a socio-technical process in Estonia
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Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data
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 paramete rs that capture characteristics of automobile usage and which may affect claiming behaviour . We propose implement ing 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 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
Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model
In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area
HUK-COBURG: The Implementation of an AI-Enabled Behavioural Insurance Business Model using Geo-Spatial Data
Automotive insurance is undergoing digital transformation that exploits new forms of big data and Artificial Intelligence (AI) systems. Geo-spatial data from GPS and telematics systems enables innovative risk modelling to evaluate driver behaviour and leads to the creation of new insurance services and novel insurance business models. A research framework is proposed to analyse AI-enabled business models and applied to a detailed case analysis of behavioural insurance in HUK-COBURG. The results illustrate the application of geo-spatial data in an insurance context and demonstrate the utility of the research framework to analyse new AI-enabled business models. The analysis identifies important implementation issues and shows that the strategic logic, regulatory and ethical context are important elements of business models. The empirical analysis reveals the strategic properties and effects of the data flywheel concept, which has general applicability. The theory framework and empirical results have important implications for other markets and theoretical contexts
Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour
With the advancement in technology, telematics data which capture vehicle
movements information are becoming available to more insurers. As these data
capture the actual driving behaviour, they are expected to improve our
understanding of driving risk and facilitate more accurate auto-insurance
ratemaking. In this paper, we analyze an auto-insurance dataset with telematics
data collected from a major European insurer. Through a detailed discussion of
the telematics data structure and related data quality issues, we elaborate on
practical challenges in processing and incorporating telematics information in
loss modelling and ratemaking. Then, with an exploratory data analysis, we
demonstrate the existence of heterogeneity in individual driving behaviour,
even within the groups of policyholders with and without claims, which supports
the study of telematics data. Our regression analysis reiterates the importance
of telematics data in claims modelling; in particular, we propose a speed
transition matrix that describes discretely recorded speed time series and
produces statistically significant predictors for claim counts. We conclude
that large speed transitions, together with higher maximum speed attained,
nighttime driving and increased harsh braking, are associated with increased
claim counts. Moreover, we empirically illustrate the learning effects in
driving behaviour: we show that both severe harsh events detected at a high
threshold and expected claim counts are not directly proportional with driving
time or distance, but they increase at a decreasing rate
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