Big Data dalam industri Asuransi

Big Data in Insurance Sector

29 Apr 2014, Harnath Babu - CIO, Aviva Life India, DATAQUEST
Humans and computers have collectively been generating data for last many decades and the data has become an integrated part of every organization-be it small or big. As the importance and value of data increases for an organization so does the burgeoning of silos within the enterprise.
Big data is a combination of transactional data and unstructured data. While technologies have been maturing to handle the volumes of transactional data, it is the unstructured data which is being generated from various sources of interactions adding complexity to the overall picture. Till now technologies have helped mastering the art of managing volumes of transaction data but it is the non-transactional data that is adding heterogeneity and momentum attributes to the ever-growing data pool and pose significant deciphering and analysis challenges to the enterprises.
With the ubiquity of modern and social technologies riding on the Internet, conventional businesses are dramatically converting to digital resulting into the big bang of data. The source of data for the enterprises are no longer constricted to corporate data warehouses but are available outside the perimeters of the organization. From the sciences to healthcare, from banking to Internet, the sectors may be diverse yet together they tell a similar story: The amount of data in the world is growing fast outrunning not machines but our imaginations.
The insurance industry too is no different than any other business with respect to its struggle to get a good handle on its data for decades, both on the transactional and the risk management sides. Many insurers have embraced analytics and have treaded at leveraging data and analytics to solve basic distribution and pricing issues. However, the industry has largely ignored opportunities to increase customer engagement and loyalty and have missed learnings from other sectors that have successfully leveraged on analytics to drive the revenues upside in this context.

Despite having the lowest insurance penetration in the country, the companies are fiercely competing with each other and are targeting for a finite pool of customers. Growth strategies are framed on the basis of the ability to eat an existing pie of customer base from competition. A valid reason could be the skewed demographics of the country and the population which can afford to have a compulsion to buy insurance for protection of life and assets. Companies have been spending time and efforts to come out with better products and pricing to attract customers on the basis of their own experiences and competition benchmarking. Distribution model for companies too are getting diverse ranging from conventional adviser channels, banks, brokers, and online. Today consumers understand what products they need so they can purchase those products on their own without taking any advice from professionals. Also, the process of buying insurance is much easier now than even a decade ago, as web and mobility-based sales are vaulting up. Having said that it becomes inevitable for companies to extract more value out of the data generated from these complex and diverse interactions with customers and partners. Global insurance carriers are experimenting and exploiting the big data and various use cases are emerging out showcasing the exponential value which it is bringing for the companies to better understand customers and resonate with them.
In the earlier world of insurance, distribution agents knew their customers and communities personally and were closely acquainted with inherent risks of offering different type of insurance to customers. Today, relationships have become decentralized and virtual. Insurers can access and leverage upon the massive data being generated from these virtual channels and quantify risks and build behavioral models based on customer profiles cross referencing with specific type of products, eg, risks can be identified on the basis of demographics, employment statistics, etc. Using analytical techniques such as pattern analysis and insights from social media, companies are now doing a better job of fraud detection. Example, analyzing the behavior of a beneficiary across similar type claims submitted by the same person and extend this to the social graph of the person to look at similar activities amongst connected individuals to derive a network of fraudulent people instead of an individual.

With the enormous data available across multiple channels of business including website clicks, social media, core transactional data, interactions with call center, emails, portals, agent reports, and various other sources it is becoming possible to get a holistic and 360 degree view of the customers. This can help insurers increase and cross sell various products and personalized services based on the needs and budgets of the customers. Companies have taken further steps ahead by analyzing the unstructured data from social media and speech analytics from call centers conversations to improve their sentiment analysis and achieve better brand value and gain competitive advantage.

This is further helping in increase of customer satisfaction, revenue per customer or a household and better NPS scores for the companies.
Until recent times insurers have been coming out with standard pricing for automobile policies based on conventional actuarial models considering variables such as vehicle type, driver age and location but there were no methods to assess risks by looking at individuals driving patterns. With the telematics data basis, it is now possible to get a direct insight on driving patterns of customers and thus helping the companies to reduce premiums for safe drivers. This data can further help in accessing the claims in case of an accident by correlating with third party traffic and weather data.
Another interesting use case of utilization of social media to introduce product offerings and services is emerging where insurers are moving themselves away from conventional marketing campaigns such as television and print media instead they are choosing social media for targeting specific customer segments and on the basis the success upgrade to broader markets and segments of prospective customers. Additionally Social networking data can be mined to determine which customers have the most influence over others within social networks; this helps companies determine which are their most important and influential customers.
Overall, there is more than enough evidence to demonstrate that the big data approach is a potential game changer in the insurance industry. Insurers, regardless of size, specialty, or location, should explore the possibilities, keeping in mind the impact of big data.

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