Big Data is transforming the business world. With access to unprecedented amounts of information on consumer behaviour, insurance companies have almost limitless opportunities for refining their products and services.
By Vikram Puri, CEO, Transworld Technologies
As a very people-centric industry, insurance companies have many ways to refine their business practices. The industry uses Big Data, combining math with financial theory to analyse and understand the costs of risks. This has been the backbone of the insurance sector. The analytics undertaken by actuaries is critically important to an insurance company. The advent of modern technology and the data explosion that is currently taking place have together 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 the fact that post underwriting, certain risks become apparent. This forces the actuary to allow for such possibilities while computing the premium.
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 resulting in the risk assessment turning out 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 person’s claim history. No weightage is given for driver behaviour, age and personal characteristics – the basic 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 drives his or her car 2500km a month and the other just 500km! On plain reading, the risk is five times higher on the insured car that runs 2500km. This differentiation gets more divergent if it were possible to know how much of the driving is 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. There is immense scope to use Big Data and analytics in the insurance domain to build better cost and operational efficiencies, while improving the overall customer experience. But this move 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 behavioural information of customers. A lot of these concerns and risks are mitigated by the use of advanced data encryption and secure communication technologies, especially when it comes to data in the cloud.
The new customer channels and touchpoints are reforming the trends and methods of data collection as well as of its analysis. Instead of relying only on internal data sources such as loss histories, which was the norm, insurers have now begun to analyse the individual.
The speed of change in an industry that has long been characterised as a slow adopter of technology is gathering pace. We look at a few big ways that Big Data and analytics are paving the way for changes like fighting fraud, improving customers’ health while reducing risk, providing an enriching customer experience, and personalised 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 management, logistics and fleet management.
For two decades, Transworld has led the road safety and driver analytics business in India, with its award winning road safety solution called Mobile Eye, as well as its online driver behaviour measurement and monitoring portal, FleetView. The latter is a cloud based analytical data store that receives fleet movement and driver behaviour data from thousands of telematics devices in the field. FleetView is an enterprise class Web platform, which allows the tracking of driving characteristics and driver behaviour, permitting measurement against peers within and outside the organisation.
Factors like erratic driving (harsh or sudden acceleration and braking), the adherence to planned routes and driving patterns, journey planning and JRM (journey risk management), continuously driving without a minimum period of rest, night driving, and overall working hours of the driver are all key inputs that make up driver and road risk analytics.
All this Big Data can be sliced on the basis of the time of day, journey areas (state, district, etc), road category, whether empty or loaded, travelling zones, stoppages, etc, to give a comprehensive risk rating number based on risk elements, distance driven and time parked. Software plugins allow control towers to monitor risks.
This Big Data can be used to create a flexible, advanced deposit policy to reward low risk insurers and penalise high risk prospects. Risk is directly impacted by mileage, area of operation, road surfaces, time-of-day operation, driver behaviour and experience.
Now connect this to personal information picked up from the Web – social media, for instance. A person’s lifestyle such as a tendency to drink and drive can be deduced from the number of Facebook posts about an office party or a tweet about a new pub. Analysis of information from multiple channels can be used in combination with hypothesis-driven analytics to develop and tailor personalised products, services, delivery methods and communications. Superior customer experience can drive valued cross-selling. Companies can achieve this through a combination of consumer-centric design, branding, and social media engagement.
With these capabilities, insurers can model and test new products regularly and seamlessly, whether based on regions, specific customer groups, or specific time scales, to generate newer insights. Thus, a truly user-friendly and integrated experience across channels can be offered to customers, while ensuring higher value for money for the organisation.
Without a doubt, an underwriting firm that ignores Big Data acquisition and analytics does so at its own peril – time will show that those insurers who are right up there with a first mover advantage will acquire relevant data and customers much faster than their rivals.
