How Big Data Can Change the Way We Buy Insurance

Factors like erratic and spiky driving – harsh acceleration and harsh braking adherence to planned routes and driving patterns – journey planning and JRM (Journey Risk Management), continuously driving without a minimum duration rest break, night driving, working hours, including driving hours on the road are all key inputs that make up driver and road risk analytics.- Mr. Vikram Puri, CEO, Transworld Technologies

Big Data is transforming the business world. With access to unprecedented amounts of information on consumer behavior, companies have almost limitless possibilities for refining products and services. As a very people-centric industry, insurance companies have many ways to refine their business practices using Big Data as the usage of math coupled with financial theory to analyze and understand the costs of risks have been the backbone of the insurance sector. The analytics undertaken by actuaries are critically important to an insurance company, the advent of modern technology as well and the data explosion that is currently taking place have expanded and reinvented the core disciplines of analysts. 

Like most companies in the financial services industry, life insurers collect a substantial amount of customer data during the application process but as with many other industries data collection, post underwriting, is marginal. A life underwriter actually is bound to hold the insurance contract throughout the life of the insured, irrespective of post under-writing risk becoming apparent. This forces the actuary to allow for such possibilities while computing premier.

In the case of general risk carriers, especially those who insure motor vehicles, the acquisition of data is almost non-existent. Plain surrogates are used to define risk, sometimes causing risk assessment to be ridiculously far off the mark. In India, the type of motor vehicle, its place of registration and its age mainly define the risk for the carrier – this definition is modulated only by the insured’s claim history. No weightage is given for driver behavior, age and personal characteristics – simple data that is absolutely necessary to correctly evaluate the risk carried by the insurer.

For example, the motor premium computed for two different persons, living in the same city, with cars of similar make, year and value would be exactly the same. It would not matter that one of them would drive their car 2500 kms a month and the other just 500 kms! On plain reading, the risk is 5 times higher of the insured car that runs 2500 kms. This differentiation gets more divergent if it were possible to know how much of the driving was during the day or night, on highways or city streets, at higher or lower speeds, or in which city the vehicle is actually parked.

Big Data has the potential to dramatically transform the motor and other general insurance landscape.

Although there is immense scope for big data and analytics in the insurance domain in building better cost and operational efficiencies, while improving the overall customer experience, this is currently challenged due to limited interactions between insurers and customers. Clearly, there are valid concerns around privacy of sensitive information relating to health, lifestyle and behavioral information of customers. A lot of these concerns and risks are mitigated by use of advanced data encryption and secure communication technologies, especially when it comes to data in the cloud.

The new customer channels and touch points are reforming the trends and methods of data collection as well as analysis. Instead of relying only on internal data sources such as loss histories, which was the norm, Insurers have now begun to analyze the individual. 

The speed of change in an industry that has long been characterized as a slow adopter of technology is gathering pace and we look at four big ways big data and analytics are paving the way to these changes like fighting fraud, improving customers’ health while reducing risk, providing an enriching customer experience, and personalized evidence-based policies.

Transworld Technologies has been the pioneer in providing risk indicators, predictive road safety inputs and in-vehicle driver assist technology, especially in the area of Supply Chain, Logistics and Fleet management.

For two decades, Transworld has led the road safety and driver analytics business in India, with their award winning road safety solution, the Mobile Eye and its online driver behavior measurement and monitoring portal, FleetView a cloud based analytical data store that receives fleet movement and driver behavior data from thousands of telematics devices in the field.  FleetView is an enterprise class web platform, which allows tracking of driving characteristics and driver behavior, permitting measurement against peers within and outside the organization.

Factors like erratic and spiky driving – harsh acceleration and harsh braking adherence to planned routes and driving patterns – journey planning and JRM (Journey Risk Management), continuously driving without a minimum duration rest break, night driving, working hours, including driving hours on the road are all key inputs that make up driver and road risk analytics.,

All of this Big Data can be sliced by time of day, journey areas (State, District), Road Category, empty /loaded km, zones, stoppages etc., to give a comprehensive risk rating number based on risk elements and distance driven and time stopped / parked. Software plug-ins allow Control Towers for contracted risk monitoring.

This Big Data can be used to create a flexible, advance deposit policy to reward low risk and penalize high risk. Risk is directly impacted by mileage, area of operation, road surfaces, time-of-day operation, driver behavior and experience.

Now connect this to personal information picked up from the web – social media, for instance. Living, drinking and driving habits will be scraped out or deduced from the number of Facebook posts about an office party or a tweet about a new pub.

Analysis of information across multiple channels will be used in combination with hypothesis-driven analytics to develop and tailor personalized products, services, deliv­ery methods and communications. Superior consumer experience drives valued cross-selling and persistency improvements. Companies can achieve this through a com­bination of consumer-centric design, brand­ing, and social media engagement.

With these capabilities, insurers will also model and test new products regularly, inventively and seamlessly; be it region-wise, as per specific customer cohorts, or specific time scales, to generate newer insights. Thus, a truly user-friendly and integrated experi­ence across channels will be offered to cus­tomers, while ensuring higher value for money for the organization.

Without a doubt, an underwriter who ignores Big Data acquisition and analytics does so at his extreme peril – time will show that those insurers who are right up there with a first mover advantage will acquire learning and customers far swifter than their rivals.

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