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Big data analytics in Insurance domain

The foundation of insurance industry is based on estimating future events and measuring the risk/value of these events; volume, velocity, veracity and variety of massive datasets has become an essential tool for insurers. With new data sources, such as telematics, sensors, government, customer interactions and social media, the opportunity to utilize big data is more appealing across new areas of this industry nowadays.

Big Data technologies are used comprehensively to determine risk, claims and enhance customer experience, allowing insurance companies to achieve higher predictive accuracy. Let’s take a look at the major uses of big data and its technologies in the insurance industry;


1. Risk Assessment 

One of the most important uses for insurers is determining policy premiums. Used mostly by automobile, home and health insurance companies, many insurers benefit from telematics, internet of things devices and wearables (jawbone, Fitbit, wrist watch etc) to track their customers to predict and calculate risks.

By using predictive modelling, the insurers can identify whether the drivers are likely to be involved in an accident, or have their car stolen, by combining their behavioural data with the exogenous factors such as road conditions or safe neighbourhoods.

A similar use can be seen in the world of health and life insurance due to the growing use of wearable technology. Activity trackers can monitor users’ behaviours and habits and provide ongoing assessments of their activity levels. Many insurers are now offering services and discounts based on the use of these devices; in fact in United states, Insurer John Hancock is offering a discount on health insurance when their customer use Fitbit wristbands that allow exercise tracking.

This has become single most motivational factors for the insurers to adopt big data analytics worldwide.As great as these uses sound, insurers should keep in mind to protect privacy of their customers and should take ethical concerns very seriously.

2. Fraud Detection

Insurers use Big Data to improve fraud detection and criminal activity through data management and predictive modelling. They match the variables in every claim against the profiles of past claims which were fraudulent so that when there is a match, the claim is pinned for further investigation.

These matches could also involve the behaviour of the person making a claim, the network of people that associate with (social media, credit reference agencies etc.) and partner agencies involved in the claim (e.g. vehicle repair shops). These complicated matches might drop beneath the radar of a human; however, they are successfully detectable by big data analysis.

3. Customer Insights

Acquiring a comprehensive understanding of customer behaviours, habits and needs from various sources is very strategic for insurers in order for them to anticipate future behaviours, to offer relevant products and to identify the right segmentations.

Information gained from call centre data, customer e-mails, social media, user forums and user behaviour while logged into the insurers’ sites enable insurers to build unique customer profile. Analytic systems can spot if a customer is about to leave by flagging up a high number of calls to a helpline.

Gaining customer insight with big data analytics not only provides predictions about when a customer is likely to leave, or shapes a customer’s policy; it can also help insurers to develop trusted relationships and engage customers in the right way with the accurate information. As a result of this strategic learning, insurers achieve positive outcomes such as solving customer problems real-time with the right approach and also upselling/ cross-selling products.

4. Marketing

After gaining a full understanding of customer behavior, insurance companies became more efficient in offering targeted products and services. This is done by offering personalized services and products such as lower priced premiums (mostly used by automobile, home and health insurance companies), contacting the customer for special offers when they are likely to leave or even offering a family package when a family is likely to have a baby.

5. Customer Experience

Loyalty programs are old news! Insurers now build personalized offers to their customers based on their preferences and behavioral data as well as offering them innovative services that streamline the purchase process.

Especially health insurance companies utilize apps’ and wearables’ data enabling them to proactively track their customers, while helping the customers to manage their health conditions/ chronic diseases. Scipt Hub Plus is a project enabling customers to get their prices for the drugs under their insurance plan at the location requested, when they get their medication from a physician. Cigna has partnered with BodyMedia to use their armband tracker for diabetes prevention and management, integrated with the customer’s insurance plan.

Another example is from the life insurance sector; Haven Life (an online provider term of life insurance), enables the users to make quick decisions on policies up to $1 Million through online questionnaires, prescription histories, state motor-vehicle records and other data sources, using big data technologies. 

6. Automation

Insurers used to automate simple processes such as compliance checks, data entry, or repetitive tasks that require less-initiative taking skills. With the rise of big data technologies, these simple tasks gave way to more complicated skills; such as loan underwriting, reconciliation, property assessment, claims verification, receiving customer insights, customer interactions and fraud detection to name a few.

With a move towards more intelligent automation, insurers can save a vast amount of time and money with the help of machine learning which trains data to improve algorithms and of course predictive analysis.

7. Smarter Labour and Finance

With the help of real-time analysis, insurers now can make daily adjustments to premium rates, premium strategies and underwriting limits by combining internal data (policy, regulations) with external data (social media, press, analyst comments) to optimize their finances and instant pay-outs.

Data mining techniques are also used to cluster and score claims to prioritize and assign them to the most appropriate employee based on their experience on claim complexity. This saves insurers a significant amount of labour-time and prevents them from high settlement amounts.


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