Implications and solutions of Big Data in Insurance

By Srikanth
15 Min Read
Big data technologies in insurance

Big data in Insurance can really be applied to a number of different areas. You can apply big data technologies for improved performance or data processing or more information. One really exciting thing about this is the new realm of data science. It is about exploration. It is using very non-traditional techniques in order to find an answer that you didn’t even know existed in your data whether it is structured or unstructured.


And without the use of this big data technology, this kind of capability would not have been remotely feasible just a few years ago. With a lot of clients that the insurance companies are working with, there’s a very strong focus on the opportunity for usage-based insurance.

An example of that would be, as a driver, you would either have a device in your car or it would be an app that you would run from your smartphone that monitors your driving patterns as well as how much you are driving in order to collect all of that data, likely in a big data technology, in order to then determine what’s an appropriate premium for you to pay based upon your driving style and the amount that you drive.

So, this is clearly a benefit for the insurance companies in terms of how they make underwriting decisions but then also for the drivers to be able to pay a lower premium based upon their own usage. So, the success of a company can lie in their deep ability to both strategize and deploy big data technologies.

Let us discuss in detail about the role of big data analytics in the Insurance industry.

The role of big data analytics in the Insurance industry

Insurers have always cared about big data technologies. If you look back over the last three centuries, the actuaries have done rating calculations, pricing calculations and they have the analysis of data to do that. But, the opportunity now or even the necessity now is to apply that to customer satisfaction.

If you look at the recent world insurance report, only twenty-nine percent of the customers worldwide say they have a positive customer experience from their insurance companies. That means there is a huge opportunity to apply big data analytics to customer satisfaction.

Opportunities in Big data technologies

Let us see five opportunities for insurance companies to improve customer satisfaction by using big data analytics.

Implications and solutions of Big Data in Insurance 1

1.   The first one is Personalized pricing. Use data and analytics to tailor prices more closely to the customer’s particular needs and this works very well in various domains of the insurance industry like health insurance, life insurance, p&c insurance, etc.,

2.   The second area is increasing agent effectiveness. Now, whether that’s an agent in a contact centre or elsewhere, this is the opportunity to bring the consumer data to their fingertips. So, they know more about the particular customer they are dealing with and can start to think about what is perhaps the next sale.

But, even before that, deliver stronger customer service. Metlife is a good example here. Metlife developed the Metlife wall and that’s essentially a large screen full of all data sources consisting of various pieces of consumer data that is available to the agent when the customer calls in.

3.   The third area is improving the online experience. Now, I mentioned the world insurance report earlier, which also showed that fewer than 30% of the customers are satisfied with the insurance companies. It’s possible therefore to improve those and Hiscox is a good example here. They have spent a lot of time tracking customers’ behaviour on their website and amending the website accordingly using predictive analytics. They have been able to increase conversion rates as a result of that.

4.   The fourth area is Value-added services. What can you package together with the policy to provide additional value to the customer, preferably at low cost to the insurance industry?

An example of that might be in terms of flood risks and flood alerts. If your consumer data shows a customer is at risk of real-time flooding, then you can obviously help them in advance of any possible flood by talking about how to take preventive measures.

If then there is a real flood alert, why shouldn’t it be the insurance company that is the first to inform based on their real-time data sets that the flood is coming, enabling the householder or the business to take those preventative measures…?

5.   The fifth area is finding new markets or customer segments using the unstructured data to ferret out additional opportunities. You have to find areas of products and services that customers are increasingly moving into and you can then negotiate deals on behalf of the customers.

Steps to deal with unstructured data

It is not too difficult for an insurance company to deal with these issues of unstructured data of consumers and start taking these real-time opportunities. Let us see some key steps that they can take to deal with these.

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1.   The first is to appoint a leader. Make somebody responsible for this. They might be called the chief data officer. But, they focus on the data and they focus on the predictive analytics of data and that is their day job.

2.   The second one is to invest in a single customer view. Be able to pull together all of the consumer data you have about your customers in one place. That doesn’t mean you need to invest in a huge data warehouse. It just means that you need to able to bring together the unstructured data from various resources in one place at the point of need.

3.   The third thing to do takes a little longer. That is to develop a ‘test and learn’ culture. It’s about creating a joint team’s business and IT working together, marketing and IT working together to have a look at an area of interest, to carry out the predictive analytics, to run some results, to try some of them to test it with customers.

If it works, great, extend that. If it doesn’t, that’s fine, you’ve learned something and you move on. But, it’s a cultural change and it takes time. That’s how things go with big data analytics.

4.   The fourth area is to look outside the insurance industry, look externally for sources of skills or sources of data that can be got from elsewhere. There are multiple datasets out there. The governments of the USA and various other countries make consumer data available.

There are also multiple other third party sources. You can also focus on various social media platforms to collect consumer data. Look outside, use that consumer data to enhance that you already have internally. And finally, that point about data privacy and security, it is critical to developing transparent privacy policies.

You really do need to get the unambiguous consent of your customers to be able to use their unstructured data even though it’s to their advantage. And you need to consider such issues as encryption to ensure that the security.

But, the final step is about making sure you clear on how you are dealing with that security and privacy issue. Insurance companies have a problem with customers’ satisfaction. But, big data in insurance is part of the solution to that problem.

So, let us discuss some of the pros and cons of big data on insurance.

Fraud detection using Predictive analysis in the insurance sector

We are told that today, we are living in the age of big data in insurance where information is abundant and smart technologies help us process it. As from the above analysis and tips we have seen, one thing is very clear that, if there is an industry that can benefit from this golden age, it surely will be the insurance sector.

But, ask any firm or decision-maker, whether they are confident about risk management, fraud detection, fraud prevention, assessment or claims procedure, you’ll find that the abundance of data doesn’t necessarily mean more control. Occasionally it could mean more confusion in terms of efficiency and more uncontrolled risk.

Predictive modelling is used for the purpose of fraud detection across various industries. But, the industries that use it the most are certainly the banking and insurance sectors. Insurance companies use predictive modelling and big data in insurance to identify frauds and criminal intents.

It’s is a reliable way of identifying mismatches between the insured party and third parties involved in the claim. It can even be the social media accounts and online activities of the insured party that can help in detecting the frauds. So, with the identification of the frauds using big data and predictive modelling, the respective insurance companies can gain a more secure place in the market.

Automation opportunities in the insurance industry

With the introduction of new-age technologies like machine learning and artificial intelligence, every sector is updating its services to automation. And the insurance sector is no exception to it. Even in the past, some simple processes like compliance checks were used to automate in the insurance sector.

So, with the intervention of machine learning and artificial intelligence, many repetitive but complex processes were automated and some other complex tasks like insurance claims, property assessment, etc will be automated soon.

Enhancing Customer experience using predictive analytics

In any industry, speed and accurate services give a good experience to the customers. And the Insurance sector is no exception for it. Before the intervention of big data in insurance and analytics in the insurance sector, the insurers used to physically verify the damage of properties in order to make sure that it is eligible for insurance. This usually takes a lot of time and effort and in most of the cases, users get frustrated because of the delay.

But today, through a significant combination of big data, advanced deep learning algorithms and drone images, insurance companies are able to calculate precisely decipher property damage without the need for physical inspections. This redundancy of physical inspections changed the entire timeline of insurance approval and claims.

With this, customers are able to receive their insurance claim payment from their respective insurance companies in as little as 24 hours to 48 hours which eventually means a very good customer experience.

Drawing deeper consumer insights with the help of predictive modelling

The big data technology can be quite helpful in understanding deeply the behaviour of the customer. The extensive data collected from the user can be processed and used to improve and adapt the industry according to the needs of the users.

For example, if we consider health insurance, vast data can be collected from various sources like wearables and health trackers used by people. But this data is not exclusively used by the insurance companies. The data is processed in a generalized way.

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It is also a better idea to assist/alert the customers by tailoring dedicated insurance packages for their conditions, which were understood from the data that has been collected.

Taking Smart labour and financial decisions with the help of the big data technologies

Not so long from today, insurance companies are gonna employ more automation than labour. This also decreases the capital of the company. So, with the intervention of big data technologies, the insurers are becoming smart day by day in their labour and financial decisions.


So, literally, every industry established ever is counting on big data analytics to improve its performance and secure their place in their respective markets. The insurance industry, one of the first industries to adopt big data technologies, has a much larger scope for development in the future with the proper usage of big data in insurance and predictive modelling.

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