The emergence and growth of connected technologies and the adaptation of big data are changing the face of all industries. In the insurance industry, Usage-Based Insurance (UBI) is the most popular use case of big data adaptation. Initially UBI is started as a simple unitary Pay-As-You-Drive (PAYD) model in which the classification of good and bad drivers is an unresolved task. PAYD is progressed towards Pay-How-You-Drive (PHYD) model in which the premium is charged for the personal auto insurance depending on the post-trip analysis. Providing proactive alerts to guide the driver during the trip is the drawback of the PHYD model. PHYD model is further progressed towards Manage-How-You-Drive (MHYD) model in which the proactive engagement in the form of alerts is provided to the drivers while they drive. The evolution of PAYD, PHYD and MHYD models serve as the building blocks of UBI and facilitates the insurance industry to bridge the gap between insurer and the customer with the introduction of MHYD model. Increasing number of insurers are starting to launch PHYD or MHYD models all over the world and widespread customer adaptation is seen to improve the driver safety by monitoring the driving behavior. Consequently, the data flow between an insurer and their customers is increasing exponentially, which makes the need for big data adaptation, a foundational brick in the technology landscape of insurers. The focus of this paper is to perform a detailed survey about the categories of MHYD. The survey results in the need to address the aggressive driving behavior and road rage incidents of the drivers during short-term and long-term driving. The exhaustive survey is also used to propose a solution that finds the risk posed by aggressive driving and road rage incidents by considering the behavioral and emotional factors of a driver. The outcome of this research would help the insurance industries to assess the driving risk more accurately and to propose a solution to calculate the personalized premium based on the driving behavior with most importance towards prevention of risk. © 2019, The Author(s).