Telematics | Big Data | Usage based Insurance

Industry: Automobile

Location: USA

Problems

During 2013, general-purpose insurance companies faced a crunch in business growth as the automobile-insurance market underwent a gradual decline. Specialty insurers were gaining traction from niche customer groups. The client needed to come up with a differentiated product/service to fuel the company’s growth. The client’s gross profit margin in automobile and trailer insurance during Q1 & Q2 of 2014 had decreased by 7% and 9% respectively. An obvious business solution was to offer differentiated insurance premiums to different customer groups to help tailor the client’s offerings according to more profitable customer groups.

Acquiring this capability depended on tracking driver behavior in each of the vehicle insured by the company. Another challenge that the client faced was filing multiple claims by customers who reported recurring accidents. This increased the service cost structure that the client had to bear. The company also wanted to promote safe driving by giving incentives to drivers for adopting a safer road behavior. Drivers would get bonus points for driving company insured vehicles safely. However, offering new services required the client to acquire real-time behavior observation technology to monitor each driver’s vehicle driving behavior. The client had a rough idea of what their company needed but was unsure of the technology solution that could deliver the business results.

  • The client had numerous statistics regarding road accidents of vehicles insured through their company. The company however lacked a tangible method to track driver behavior at an individual level. Tracking driver behavior at an individual level had become essential for offering new premium packages.
  • A significant percentage of automobiles and trailers, insured by the company, reported above-average accident ratio. It drove the company’s average profit margin from these vehicles as low as 2% per annum. To help identify the drivers who would likely exhibit risky driving behavior, thereby resulting in more number of accidents, Allied Consultants analyzed that driver ranking and behavior monitoring was essential to categorize drivers. It would allow the client to offer lower insurance premiums to low-risk drivers and a higher insurance premium to high-risk drivers.
  • After the initial assessment of the client’s business needs, the technology problem was narrowed down to the need for developing an innovative and real-time driver monitoring system.

Solutions

After careful evaluation, the client chose Allied Consultants as their technology partner.

  • First part of the solution was to select an appropriate device that can be plugged in the vehicle to acquire data, and relay it back to a server. Danlaw’s Data Logger was selected for this purpose. The second part of the solution was to manipulate/reuse the data stored in the server and provide actionable intelligence on the User Interface at the client’s end. MS Azure was used to building, deploy and manage the tracked data and deliver actionable features to the end client
  • The main technologies used in developing the solution were cloud computing (Amazon Web Services), 3G & 4G GSM networks, Microsoft .net Framework, Microsoft Azure, and Microsoft Windows Services.

Solutions

Key features

  • Overall Score
  • White points
  • Fuel [AED]
  • Km Driven
  • Number of Trips

Data from the OBD II port, extracted through a data logger device is relayed to the cloud using Smartphone Bluetooth over the GSM network. The data residing in AWS S3 is sent to the dumper using Windows Services. Modeled data was pulled from SQLdb that resulted in data representation on the UI. The data flow from the user’s vehicle is represented below.

Design/architecture of the solution
The primary challenge that Allied Consultant’s developers had to overcome was the parsing of data from the AWS database and conversion of this data into the modeled form. The challenge was overcome by developing code using the Microsoft .net framework.

Tech Stack

  • Amazon Web Services
  • 3G & 4G GSM networks
  • Microsoft .net Framework
  • Microsoft Azure
  • Microsoft Windows Services
  • Onboard Diagnostics II

Results and Benefits

  • Our product delivered actionable behavioral data such as Trip start, trip end, hard acceleration, extreme acceleration, hard brake, extreme brake, and speeding
  • Using the aforementioned data sets, multiple products features such as vehicle start and stop statistics, driving routes, driving path, and real-time behavioral data of the driver was now displayed on the UI at the client end.
  • Our solution was aimed at aggregating the data and delivering an overall driver-score to the client. The scores were based on predetermined criteria.
  • Our solution also enabled the client to launch multiple products & services including PAYD (Pay-as-you-Drive, a use-based insurance model based on driver habits) PAYD (Pay-how-you-Drive) Insurance, crash detection, and theft recovery services.
  • The client now obtains live feedback on driver behavior and vehicle-related events that occur on the road.
  • Overall, the solution also allowed the client to identify the most profitable customer groups and tailor the company’s offerings according to the customer group it services.

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