Big Data & IoT are Key to Advancement in the Insurance Sector

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By Nika Chizh, Senior Copywriter and Digital Marketer
Published on Jul 12, 2022

Insurance
Best Practices
8 min read

A new report by Reltio, cloud-native SaaS data platform, has some valuable Insurance statistics. Although you won’t find any intrinsically original insights, it does help see a bigger picture and points in which direction to move. Let’s have a look at the key report’s findings, while I will also try to show you how you can make the best use of them.

A few Insurance CIO trends we observe

Insurance CIO Mega Trends: Building Effective Digital Strategies in a Transforming Insurance Market encompasses the results of a survey in which 100 technology leaders from insurance companies across the U.S. and Canada took part. Among the respondents are C-level executives, mostly occupying a role in information technology.

So, what’s new? Data is king. I bet you already knew that. But what drew my attention upon having a closer look at the report is that the insurance leaders emphasize two particular development focuses, which are Big Data analytics and Internet of Things. Among the short list of the remarkable techs that are available to the Insurance companies nowadays (cloud computing, RPA, AI, Machine Learning, mobility, etc.), those two are the most prominent in the eyes of the chief decision-makers. Why so? You’re several minutes away from the answer.

Technologies that will shape the future of insurance

Big Data analytics in Insurance: The hunt for the insights is so on

I’ll start from the obvious one.

Data is a game changer for businesses. It shouldn't even be that big to carry a lot of weight. A nice example is one of our projects that carves insightful bits of data out of face-to-face interviews with app users or dynamic personalized questionnaires. Our client’s app helps the customers get honest feedback from their app users on what worked out just fine and what didn’t for them when using the application in question, i.e. provides a win-loss analysis.

So, despite the complex microservice-based structure and over 100 webpages, this app has nothing to do with big data, but, man, it speaks out loud. Let alone really huge amounts of data collected throughout multiple data sources. Those can be:

  • Databases (connected to your CRM, ERP, CPQ or other internal and external systems),
  • Web services (pieces of software that allow communication or data exchange between disparate software systems), or
  • Flat files (databases storing similar strings of data as records in a table with all the structure markup removed).

Altogether, multiple data sources generate a landslide of data (and a business may connect to 5 or 500 data sources, depending on their needs). No wonder we’re witnessing an unprecedented rise of the ETL/ELT technologies, visualization tools and complex BI solutions. These are the tools that help turn the landslide of raw data into readable and actionable insights for the companies. ETL or ELT solutions are kind of the backend of a BI system where all the data prep is happening, while visualization tools are all fancy dashboards and colorful charts - the frontend section. Together they form a Business Intelligence solution, which may come as a single package from a single vendor (such as Power BI or Tableau) or be custom made according to your specific needs.

However, to show you how big data generates big results, we’ll need a whole series of dedicated blog posts on the topic. Those will follow some day later on for sure (as we have so much to say, being an ETL engineering and BI services provider ourselves), but now - back to big data. Is there some real potential besides all the marketing blah-blah that both traditional carriers and insurance startups see in it? Yeap, that’s the case.

As insurers are among those lucky businesses literally sitting on the piles of data, use cases abound. Here are the most frequent ones according to our own experience.

Big Data & IoT use cases

  • Customer segmentation

    Big data help carriers better address their customers’ needs by segmenting them into specific groups (age, gender, geography, etc.). Those groups are then studied in depth to make personalized offerings.

    No rocket science. One just needs their data cleansed, verified and placed in a common target and a couple of nicely tuned algorithms. We see it around a lot and it works.

  • Risk assessment

    With tons of historic data on their hands combined with data techs, insurance players are better equipped to make accurate risk assessments.

    One of our clients approached us to address exactly the risk assessment issue. What made their case special is that it is a cyber risk assessment engine. We cannot wait to see it in action and share the outcomes with you.

  • Workflows optimization

    This one is a game changer hands down when we talk about highly repetitive processes, and there’s hardly any insurance company not dreaming of putting an end to them. Say, what if AI algorithms could approve claims for compensation based on a set of the pre-defined criteria? This is, by the way, another undergoing project of ours. Our client is implementing data techs to improve their car policy issuance processes. The new system would take an enormous load off the shoulders of the call center guys and streamline policy issuance workflows.

What we haven’t yet done at scale is the combination of IoT and Big Data. And this is where our next hero shows up.

IoT (Telematics included) is no magic, but it seems so

Our teams are working hard to combine the power of Big Data and IoT at scale. After reading the next story, you’ll see why. And you’ll also understand why carriers put exactly Big Data and IoT duo in their focus.

Once there was a little startup who wanted to make a difference in the car insurance industry. Big carriers took a greater market share, leaving only a small portion of the customers to the startup. But the big carriers didn’t know that the startup had the power of Big Data & IoT (and knew how to use it). So, what did the startup do with it?

They started collecting publicly available population size data on car crashes over the last 15 years. By the year 2021, they had over 100 mln car crashes going back 15 years operationalized across 27 states.

Every police reported crash gets its time and geolocation tag. The weather, the lane, the blind curve - all goes to the database to be later used for the most accurate risk assessment in the world. Thus, the startup cracks the loss experience.

The little startup goes to the big carrier and says: “Hey, we’ve cracked the loss experience. Why don’t you buy our know-how and let us have our market share?”

The big carrier agrees.

What happens next is that the big carrier uses the technology that the startup built for the risk assessment, but doesn’t fully understand its sheer awesomeness.

The carrier uses Telematics data to let their customers pay per mile, thus reducing premium payments. They think in such a manner they are increasing their attractiveness to the market.

“Hey, big carrier, – the little startup says, – but you rate all those drivers similarly. They all ran 500 miles this week, but half of them drove the safe routes and the other half chose the nastiest routes possible. It’s all in the algorithms I gave you. Why don’t you use that data to develop a more fair and competitive pricing strategy?”

“Ay, little startup. Ain’t nobody got time to mess up with it. By the way, I’ve already introduced pay per mile approach”

“Gimme my brilliant Big data & IoT powered technology back then! I’ll do the whole thing myself!” – the little startup said, got their know-how back and opened up a car insurance company named Loop. Since then, the little startup has been growing steadily, providing fair pricing and outstanding customer service, the power of Big Data and Telematics being at the core of their business.

The moral of this story is – if you know behavioral patterns and the context (which is what IoT provides), add some Big Data techs on top of it and you’ll be able to make all the difference in the world.

Data technologies: Promises are huge, but come at a cost

The revolution that the abundance of data and data technologies has brought is real. I’m not sure about next month or even a year, but very soon we’re going to see some dramatic shift in insurance servicing. Like embedded insurance when a client doesn’t have to stir a finger to get insured. For example, just by reading the geolocation data, the carrier can switch to a different type of insurance for the client traveling abroad. Or when the smart meters signal leaking pipes in the basement, the insurance company will be able to take preventive measures before the client has the basement flooded and shows up with a claim for a bigger compensation. This is what I mean when I say the power of Big Data and IoT will shape the future of Insurance.

It all sounds fantastic. Literally. But if you’re no newbie in data analytics, you know what stands behind those decisions that futureproof one’s company’s or household’s destiny. It’s a far less glamorous process of data collection, cleansing, verification, and transformation. In fact, data analysts spend 50 to 80% of their time simply preparing that data to be analyzed. Without that scrutiny that data engineers and data scientists apply in their jobs, the fancy universe of big data is in jeopardy. And with the rising complexity of data, the entire process isn’t getting any easier.

how data analysts work

Not to mention regulators! Any company that has ever tried using big data at scale will second – data is no easy game to play. GDPR and California Consumer Privacy Act won’t be of much help when you decide to try juggling your customer data globally. Quite the opposite, by being protective towards customers, regulators are abusive towards the innovators. But that’s a whole different story to tell.

I hope you liked this one. Stay tuned for more.

 

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