Despite the development of new technologies in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. We consider a dataset composed by 51 different features extracted by 10 different drivers, evaluating the efficiency of the proposed method in driver identification. We also find the most relevant features able to discriminate the car owner by an impostor. We obtain a precision and a recall equal to 99% evaluating a dataset containing data extracted from real vehicle.

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the "pay-how-you-drive" paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.

In the last years, several safety automotive concepts have been proposed, for instance the cruise control and the automatic brakes systems. The proposed systems are able to take the control of the vehicle when a dangerous situation is detected. Less effort was produced in driver aggressiveness in order to mitigate the dangerous situation. In this paper we propose an approach in order to identify the driver aggressiveness exploring the usage of unsupervised machine learning techniques. A real world case study is performed to evaluate the effectiveness of the proposed method.

Several techniques have been developed in last years by automotive industry in order to protect drivers and car passengers. These methods, for instance the automatic brake systems and the cruise control, are able to intervene when there is a dangerous situation. With the aim to minimize these risks, in this paper we propose a method able to suggest to the driver the driving style to adopt in order to avoid dangerous situations. Our method is basically a two-level fuzzy systems: the first one is related to the driver under analysis, while the second one is a centralized server with the responsibility to send suggestions to drivers in order to prevent traffic incidents. We carried out a preliminary evaluation to demonstrate the effectiveness of the proposed method: we obtain of percentage variation ranging from 85.48% to 88.99% in the number of traffic incidents between the scenarios we considered using the proposed method and the scenario without the proposed method applied.

A specific kind of insurance that is emerging within the domain of cyber-systems is that of cyber-insurance. Cyber-insurance is the transfer of financial risk associated with network and computer incidents to a third party. Insurance companies are increasingly offering such policies, in particular in the USA, but also in Europe. The emerging trends in cyber insurance raise a number of unique challenges and force actuaries to reconsider how to think about underwriting, pricing and aggregation risk. Aim of this contribution is to offer a review of the recent literature on cyber risk management in the actuarial field. Moreover, basing on the most significant results in IT domain, we outline possible synergies between the two lines of research.