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- What is Insurance Data Analytics?
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Wondering about insurance data analytics?
You’ve come to the right place.
According to a study, “the Big Data Market is to grow at 12% CAGR to reach $267 billion by 2025“.
What is big data and how does data analysis work?
Big data is exactly what it says on the tin – large volumes of structured or unstructured data. This data can be literally anything that has value to an individual or company.
As a result, the source of such big data can be anything from Google search results to personal data gained by businesses as part of their normal operation.
The term ’big data‘ has only been around for the last 20 years or so despite companies storing ’big data‘ since the advent of the personal computer.
What the data actually comprise is not really that important since, without adequate means to interpret this data, it is effectively useless. And that‘s where data analysis comes in.
In order to process these huge volumes of data, and present the results in a manner that can be read and understood by people, specialist data analysis software is required.
In the early days, data analytics tools were fairly basic and could only analyze a small range of data patterns. However, due to the extremely valuable nature of this data, the industry has pushed the development and analytic tools have grown incredibly sophisticated over the years.
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These days a single ’off-the-shelf‘ piece of software can provide accurate predictive data analytics for a whole range of different businesses. Many of these tools even have the power to interpret totally unstructured data sources that even include corrupted data as well.
But just how does the global insurance industry use big data analytics to improve the way it operates? Let‘s take a closer look to find out.
Data analytics in the insurance industry
The insurance industry has been one of the leading growth engines of the entire big data analysis industry. Much like the financial services industry, it now uses analytics in almost every stage of its day-to-day operations.
One of the main uses of data analysis for an insurance company is in carrying out more detailed risk assessments. So far, big data analytics have been used to identify trends that help to determine more precisely how much of a risk each applicant represents.
While the exact scale of the personal information that the insurance industry holds on us is not fully known, they have confirmed that they use a variety of different data sources that include everything from police crime data to our social media accounts.
So let‘s consider one example of just how much the use of big data is improving the accuracy of risk assessments.
A person who has no prior claims and no criminal record might decide to take out an insurance policy on their new car. In the past, that person would undergo a risk assessment based on such factors as age, their existing record, the make and age of their car, etc.
Today, thanks to the access to far more data, insurance companies can also factor in such things as climate, statistical data relating to the number of accidents involving that make of car, and even whether the area the applicant lives in has seen a recent spike in car crime.
All these factors can help insurance companies price their policies more accurately in accordance with the risk.
Fraud detection rates have climbed enormously since the introduction of sophisticated data analytic tools into the insurance industry. Fraudulent insurance claims cost the industry a whopping $80 billion each year, forcing the price of premiums up in order to pay for it.
Big data analytics allow insurance companies to identify patterns of past behavior that help them to determine if an applicant is likely to make a fraudulent claim. These data patterns include everything from analysis of the frequency and types of past claims to whether an applicant has past convictions for fraud etc to recognize fraudulent claims.
Such predictive modeling will then trigger red flags during the application process to help agents determine whether or not to seek more information or refuse to issue a policy completely.
In the same way, companies can use insurance analytics to process claims while trying to detect instances of fraud.
One such example is trawling past claim data to determine whether there is a pattern to the events that led to these claims. So if, for example, a claimant happens to have had their home burglarized after leaving the identical back window open, the system will flag this information for further investigation.
Improving Customer Experience
Believe it or not, insurance companies are not just sitting around all day trying to figure out how to avoid paying out money. Like any business, they only survive and prosper thanks to their customers.
Big data analytics tools can be used in a variety of different ways to improve customer experience and also help companies to gain new ones.
The main way this sophisticated technology is helping insurance companies to improve their customer experience is by helping to tailor-make policies to fit each individual client. This helps them avoid selling fixed policies that often don‘t suit their customer‘s exact needs.
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If you have ever felt annoyed that you have bought insurance that protects you against something you don‘t need then you will know what I mean.
These days, insurers‘ computer systems use clients’ personal data to create personalized policies. Customers will be automatically offered additional options to insure them against eventualities that predictive analytics has determined are a risk to them.
So if a client is going on holiday abroad, but has health problems, the system will automatically offer the person medical coverage that is suitable for both them and the place they are going to.
By offering customers what they actually need, insurance companies can offer better protection and not waste customers‘ time and money trying to sell them the protection they don‘t need. Not only does this increase the chance that customers will remain with the company, but it also means they are likely to recommend friends and family too.
Help Streamline Internal Processes
The ability of data analytics to streamline internal processes saves insurance companies massive sums of money each year.
Insurance companies use big data analytics to analyze such things as how well particular policies are selling, correlate customer feedback, which policies receive the most claims, and customer behavior towards various sales techniques/promotions, etc.
Since all this data can be processed in a short space of time, managers can examine up-to-date information on what particular things are doing well and what aren‘t. This allows them to refine products as well as better train staff in how to sell products etc.
How big data analytics can improve insurance in the future?
Big data use cases in the insurance industry are forecast to grow dramatically over the next few decades. And given how advances in data analytics will lead to massive improvements in all the examples I have just given, it is hardly surprising why.
When it comes to determining risk analysis, for example, big data could be collected from smart devices that will become standard in everything from cars to watches. The potential data volumes are enormous and would be available in real-time too.
Sophisticated analytic tools would be able to quickly identify patterns and use predictive software to determine risk.
Insurance companies could even offer pay-as-you-go insurance policies. So, for example, if a person who rarely uses their car plans to drive to see their relatives in a distant town, his/her insurance policy could be activated just for the journey.
This would save them money in the long term as they would not be paying for insurance that they didn‘t use. While the car wasn‘t being used it could have a simple fire and theft policy attached which would cost the bare minimum.
Since insurance companies would be able to accurately predict risk, they would be able to offer such forms of insurance just from the savings they would make from accurately detecting fraud and making high-risk individuals pay more.
All of these policies could factor in real-time eventualities such as weather, road conditions, time of day, and crime statistics in the local area to accurately determine risk.
This way, policyholders who are responsible, and therefore of low-risk, would not be penalized because of those high-risk individuals who are.
Fraud detection will also grow much more accurate. Since the number of sources and the amount of big data will also increase over time, companies will be better at using data analytics to identify fraud.
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This kind of data could be anything from a profile picture on a person‘s social media showing information that contradicts what they have said in a claim, to location data from a smart car that shows a person was home at the time they claimed their home was burglarized.
The possibilities of improving the way the insurance industry does business are nearly endless. The only question is how much of our data should insurance companies be allowed to access?
Is the use of big data solutions for insurance right?
The final point in our discussion of the use of data analysis in the insurance industry concerns the ethical component.
Governments around the world are now beginning to take more and more interest in how insurance companies use our data. All developed countries now have laws concerning how companies use personal data.
The incentive to use as much of our personal data as possible to help insurance companies make more money is worryingly attractive. As the recent leaks on the NSA‘s hidden data collection programs reveal, not keeping a careful eye on organizations can lead to worrying cases of ’over bending‘ of the laws that aim to protect citizens‘ right to privacy.
The potential for technology to one day allow insurance companies to, for example, turn on microphones on smart devices in our homes and listen to what we are saying, just in case we discuss our insurance claim, is extremely disturbing.
As companies have repeatedly shown in the past, the bottom line is that their responsibility to their shareholders comes first. So whether it is not taking care of the environment or allowing us our privacy, companies have and will continue to attempt to bend the law.
Thankfully, governments are attempting to address the problem. In 2016 the United Kingdom‘s Financial Conduct Authority requested information on how insurance companies are actually using big data. Likewise, the International Association of Insurance Supervisors has also requested similar information from insurance companies.
The intention is that by understanding exactly how data analytics is being used in the insurance industry, governments and organizations can issue guidelines and pass laws to help companies find the right balance between personal privacy and improving their operations.
After all, big data analysis is an enormously powerful tool that promises to bring huge benefits to us all. Given that this power is set to increase dramatically as we create more and more personal data, insurance companies need to act responsibly to see that it is not misused.
If you are planning to use data analytics for your business processes, you will need skilled software developers with expertise in using the right development tools and frameworks to give you a competitive advantage in the insurance market.
DevTeam.Space can help you here via its field-expert software developers community. Write to us your initial project requirements and one of our managers will get back to you for further assistance.
Frequently Asked Questions
Big data analytics is the interpretation of large pools of data by computer software programs with the aim of finding patterns that offer actionable insights.
The insurance industry was one of the early beneficiaries of big data analytics. Many of its processes are heavily reliant on technology which helps with everything from risk assessment and internal and external data management to identifying fraud.
You will need to create a project plan and onboard developers. You can find developers with industry experience at DevTeam.Space.