Big Data in Insurance

Big Data and analytics is essential to Insurance industry because primarily it works on the principles of risk, hence setting policy terms or performing a real-time time analysis of data forms a basic need. For e.g. identifying fraud or setting policy terms based on the combination of incoming data, social media data, geographical data, historical data becomes a need. Bringing in weather sensor data and other related data into the dataplaform helps customers can be made aware of the disasters or catastrophes so that they can safeguard their properties and lives in some cases.

Bigdata Experts at Sentienz understand the game of scale, we understand that insurance companies need a platform to support their specialized analytics solutions. Also we respect the existing IT Infrastructure and allow smooth adoption. Our platform enables you to fasten the road to advanced analytics.

Bigdata “What” Data

Insurance sector deals with unstructured data as well as structured data. Key use cases like fraud identification and setting policy terms can be realized only when data from social media, demographic data , internal historical data are seen together. Sentienz data platform Argo helps in ingesting data from varied source to achieve the analytics goal.

Bigdata Insurance Usecase


A significant portion of a company’s loss adjustment expense ratio goes to defending disputed claims. Insurers can use analytics to calculate a litigation propensity score to determine which claims are more likely to result in litigation. You can then assign those claims to more senior adjusters who are more likely to be able to settle the claims sooner and for lower amounts.


By gaining a more complete understanding of a customer by analyzing all of the available data, insurance companies can become more efficient in offering us products and services which will meet our needs

Fraud detection

Through profiling and predictive modelling. Variables within each claim are matched against the profiles of past claims which were known to be fraudulent

Risk of cancelling or leaving

As with policy underwriting or fraud detection, this is done by comparing the data on a customer’s activity to that of customers who have cancelled their policies in the past.

Setting policy premiums

Insurers must set the price of premiums at a level which ensures them a profit by covering their risk, but also fits with the budget of the customer – otherwise they will go elsewhere

Improved Decision

Making offers or incentives, for e.g. fit bit monitor along with discount on medical insurances, data gathered from fit bit will help the insurance co in return.


To lower costs, insurers often implement fast-track processes that settle claims instantly. But settling a claim on-the-fly can be costly if you overpay. Any insurer who has seen a rash of home payments in an area hit by natural disaster knows how that works. By analyzing claims and claim histories, you can optimize the limits for instant payouts. Analytics can also shorten claims cycle times for higher customer satisfaction and reduced labour costs. It also ensures significant savings on things such as rental cars for auto repair claims.


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