Insurance carriers are lucky ones. Why? Because they are sitting on piles of data and are able to gain the most out of them to move their businesses forward. Financial, personal, and health details — all this sensitive information represents a valuable asset that requires careful handling
But there is one subtle point. As a rule, insurers operate huge volumes of heterogeneous data. Historical, ordinary files, receipts, and many many others. Obviously, the more data you operate, the more challenges you face when making them suitable for further usage.
Big data — that’s the topic we’d like to discuss today. When do data become big? How do insurance companies use big data and what value do they gain from it? Why do Machine Learning models perform great and which BD processing challenges exist? Find answers to these and other big data services questions in this blog post.
The Watershed Moment or When Data Become Big
Have you ever wondered where the line between regular and big data is? Spoiler alert: it doesn’t even exist! At least, there are no generally accepted facets when data become big, and we can’t say that it’s conditional one hundred gigabytes and beyond.
However, although there’s no clear boundary, we can highlight an aspect that indicates that we deal with big data. One can say that when the inbound data flow is so fast and the volume is so huge, that we have to resort to more advanced processing tools and storage. Just because regular ones cannot handle them, or they can, but it takes a lot of precious time to process.
Speaking about the insurance industry, such companies fall into the category of those lucky ones operating large data volumes, put simply, big data. Client details, information from a variety of smart devices, data from Electronic Health Records, and other sources — insurers’ aim is not only to gather and accumulate the info, they also need to transform it into a valuable asset, for example, to calculate premiums.
If you are an insurer, you, like no one else, understand that data processing flow must be well-established, fast, and efficient. However, it’s impossible to pull it off with standard data processing tools, such as Microsoft SQL Server or Postgres integrated with your existing insurance solutions.
About the main differences between the approaches to regular and advanced tools, we’ll speak a little later. Now that we’ve figured out what big data is, let’s first sort out which profit it yields to the insurance business.
Squeezing the Maximum Out of Information. The Role of Big Data in Insurance
Risk Assessment and Precise Premium Calculations
To evaluate possible risks and offer a potential insuree an appropriate premium, insurance carriers rely on a variety of different data. Let’s consider several big data use cases in insurance below.
For example, there is a potential client who wants to insure their house somewhere in Hawaii. Besides personal customer data, their solvency, and trustworthiness assessment, it’s essential to evaluate the situation in the region, too. Such as the probability of tsunamis, hurricanes, or volcano eruptions, and criminal rate.
To do that, you need to analyze historical data related to such incidents for, say, the last decade. Just imagine the scope of data that needs to be processed and analyzed!
Another vivid example related to big data applies to health insurance. To offer a premium, you have to take into account a variety of factors. Age, current health condition, chronic diseases, predisposition to illness, genetics, and many more. Sure thing, to offer an adequate premium, you can’t do without a pile of data related to one client.
Here, it’s the very moment to look into your client base, see how many insurees you have, and realize the volume of data you operate.
Learn if Insurance Data Analytics Is Essential for the Industry
Personalization Is the New Normal
Compare ten years ago and today customer’s expectations. Nowadays, clients prefer a personalized approach, when a provider takes note of their preferences and desires. If they don’t gain it from one provider — they will switch to another more flexible one, who stands ready to adapt to customer requests quickly.
That’s what big data can definitely help you with! It’s understandable that insurers have huge client bases, and providing an individual offer for each customer is not an easy task to tackle. However, with the power of tools appropriate to big data handling, this becomes a breeze. As a result, by analyzing piles of data, you are empowered to assess all possible risks and prepare unique offers to customers, which won’t go undervalued.
Read why Hyper-Personalization in Insurance Is a Modern Must-Have
Fraud Detection and Prevention
With the capabilities of advanced tools, fraud detection and prevention have become much easier than a decade ago. First, because these tools are designed to process piles of information within just a few minutes. This allows you to detect suspicious operations promptly and take measures immediately.
Second, through the help of big data in the insurance industry, you are able not only to detect fraud but also to prevent it. By analyzing previously detected abnormal patterns, robust tools build forecasts of possible incidents in the future. Without any doubt, being aware of potential issues, prevention becomes far easier. In addition, you won’t have to deal with negative consequences, no matter the scale.
Workflows Optimization
This is the last but far not the least point we’d like to mention when speaking about the benefits of big data. It’s obvious that with the emergence of advanced tools, insurers don’t have to extract, process, and analyze data manually — these can be easily entrusted to specific systems. Thereby, freeing themselves of a huge piece of monotonous work and human errors that are inevitable on the way.
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However, the capabilities of the tools went far beyond. Now, by leveraging AI algorithms, you can not only gather and analyze data. They give you an opportunity to alleviate your life even more — by setting predefined criteria, you can shift the duties of compensation claims approvals to the system, too. But it will do it much faster and more efficiently.
Data vs. Big Data Processing: Key Differences Explained
Data Processing Speed
Using traditional data processing tools for huge datasets we should be prepared for delays. Because they are not designed for big volumes of information. There are two options: whether the process takes too long, like a day or two. Or the server won’t stand the load and will refuse to work at all.
What’s in the result? You’ll have to purchase more powerful servers suitable for big data handling. This entails huge expenses, incomparable with adopting specialized tools.
In contrast to the traditional data processing approach, such a framework as Apache Spark works with server clusters. The tool allows parallelization of data processing, therefore, speeding it up significantly. And voila, instead of tedious waiting, you receive the result just in several minutes.
Lack of Personalization and Low Risk Evaluation Precision
The more data you operate — the more precise results you are empowered to have. Since standard tools can process quite limited datasets, it would be naive to expect precise calculations and forecasts.
Consequently, this will lead to improper premiums definition. Whether it will be lower than it should be in reality, which negatively affects the insurance company’s bottom line, or it will be higher, which will displease the client. Agree, both options are so-so.
Moreover, as we discussed above, clients expect a personalized approach and settle for nothing less. With a small amount of data, personalization is an unrealistic goal, which also must be taken into account.
Also, there is a particular category of clients that need special insurance products, for example, digital nomads that expect their programs to be eligible in different countries. Yes, it’s a small and specific group of people, but who said that they are not worthy of attention?
Not Much Room for Innovation
If you have advanced marketing platforms or actively use IoT systems, you’ll inevitably face the hurdles related to integration. We don’t say that traditional data processing systems are not subject to integrations at all — no. It’s still possible, but there are too many underwater rocks on the way.
Therefore, it makes little sense in putting titanic efforts and an influx of huge resources to circumvent the restrictions and establish stable functioning of the entire ecosystem. In our opinion, it’s much more reasonable to adopt a specifically designed system right away, than tilt at windmills.
Machine Learning. Why Performs Great on Big Data
It’s unlikely that we can squeeze much value out of standard approaches, such as time-series analysis. With big data leveraged in the insurance sector, your capabilities expand significantly, and hence, the value we might gain. Here’s why.
Big data implies the usage of a huge number of parameters we can apply to build Machine Learning models. Therefore, the system will consider all the details and interconnections even if a human doesn’t attach importance to them.
As a result, the chance to miscalculate is really miserable. Risk management, fraud prevention, personalization — all these advantages we mentioned above will be available with the power of the synergy of big data and Machine Learning.
It’s completely different if we work with a limited number of parameters. Obviously, nobody forbids you from applying ML on regular data, and it will work for sure. However, this approach is far from reliable and even dangerous, since the model will consider only a small piece of information. Is there a chance to see the entire picture and elaborate your further strategy relying only on its fragments? It’s extremely doubtful.
Explore more about The Power of Machine Learning in Insurance
The More Data — The More Problems. Challenges in Leveraging Big Data in Insurance
Although big data expands your business opportunities and enhances risk management, these benefits are feasible with the right approach only. Let’s go over some intricacies it’s better to know before you start leveraging big data for insurance.
Not Every Storage Is Suitable for Big Data
Imagine that your insurance company has its own vehicle fleet, and you have continuous inbound data flow from all telematics devices. Therefore, all these details must be stored somewhere. And here’s where the shoe pinches.
Traditional databases are not capable of storing huge datasets. Even if by some miracle you manage to fit them, most probably, you’ll fail in their processing. And don’t forget that data flow is continuous, so when you run out of storage space, your database won’t be able to accumulate more, and you’ll have to look for scaling opportunities urgently. Obviously, this translates into additional expenses.
The solution is adopting an automatically scalable database, such as Hadoop, Amazon, or Google Cloud storage. These platforms offer distributed systems for data storage and automatically scale when needed.
More Responsible Approach to Data Quality
The more data you operate, the more problems with data quality you risk to have. If your insurance company has a complex infrastructure and extracts different types of data from multiple sources, including IoT devices, there are thousands of probabilities that something will go awry. For example, you may simply overlook that a device malfunctioned or any other process failed.
Such malfunctions directly affect the precision of calculations and forecasts. To prevent this from happening, you must pay decent attention to the quality of ETL processes.
Data Integration
When speaking about big data and the insurance industry, the workflow implies extracting information from a variety of sources. Therefore, the flow of data collection and processing is quite sophisticated.
First, it requires the implementation of complex ETL processes to ensure decent data quality. Second, because of the necessity to aggregate our data so they could bring tangible value. To do that, you need to proceed with multiple integrations of both internal and external platforms, which is effort- and time-consuming.
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Substantial Costs
The last but not least point is the cost. More complex ETL processes, advanced data management and visualization tools, and various integrations — all these things require considerable resources.
Additionally, remember that your staff must be well-versed in big data intricacies and tools. Highly proficient data engineers command higher salaries, so be prepared for that.
At the End of the Day
Data require careful handling, especially if we deal with sensitive ones. However, the more data you manage, the more complex the process becomes, and this is a significant challenge faced by many insurance companies worldwide.
Remember, this pain can be resolved. Well-elaborated strategy, flawless ETL processes, appropriate tools, and proficient staff — that’s what you need on the way to precise premium calculations and accurate forecasts.
At Velvetech, we have a professional team of big data engineers who have already helped dozens of customers optimize their data management, streamline operations, and achieve accurate results. Contact us, we’ll gladly help you transform your data challenges into opportunities!
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