How to build a strong foundation for insurance data analysis

There’s no doubt that insurance data analysis is going to drive the future of the whole insurance industry, and we want you to have an edge in this growing field. Just as insurance customers pay their premiums with an eye on the future, you’ll also need to take the right steps to ensure the future of your analytics system - and your business. Here’s how you can lay a strong foundation.

As the number of covered assets increases, insurance agencies are faced with more and more information to analyze before awarding payments. With the digital and social media revolutions far underway, the data needed to make these decisions is out there waiting to be tapped. However, implementing a full-scale insurance data analytics solution requires you build a strong foundation on which your subsequent data mining results will rest.

Ideally, insurance data analysis is aimed at producing actionable intelligence used to make strategic decisions. The most common uses include market research and brand management, fraud detection, and customized risk assessment modeling.

What really happens during data mining?

Data mining is the process of discovering patterns in large data sets using a combination of artificial intelligence, machine learning, statistics, and database systems. The goal of data mining applications is to fish out the information relevant to your industry and transform it into a format you can use in making crucial decisions for your business.

It therefore follows that there are several insurance data mining tools out there. Though several boast the ability to traverse all probable questions insurance companies may need answered, most tools have their niche and are only known to perform well with specific insurance analytics.

Some key considerations

Though many data mining platforms promote fully automated systems - you cannot completely remove human analysis from the process. The information presented to you must be further acted upon by a subject matter expert in order to glean valuable insights and inform strategic decisions.

A data mining platform will go through large data sets and extract different patterns like data record groupings (cluster analysis), abnormal records (anomaly detection) and dependencies (association rule mining). Though this requires significant computing power to compile, it is not yet useful to your insurance company. Further algorithms and analytics are required to present the data in a way your team can use to make informed decisions.

Go for nothing but the best!

Insurance data analysis results are used to make decisions that will determine your profit margins and even result in the success or failure of a product offering. Therefore, you must ensure you get the best tool on the market to minimize the margin for error.

Another aspect to consider is how you acquire the data to analyze. The public has become increasingly concerned with how third party players use and access their data. To be on the safe side, go for a data mining tools that only use the permitted avenues to source your data and manipulates it in a way that won’t violate legal terms and conditions.

Getting past the do’s and don’ts

Clearly, insurance data mining is all about the finer details that are far too often time-consuming or confusing for a busy entrepreneur. The best way to navigate around this issue is by letting a trusted and innovative company handle your data mining needs.

Here at IKANOW we offer solutions that allow for the fusion and analysis of your data that yields insight into the trends of any insurance market. An additional feature is its ability to combine traditional, structured data with unstructured information gathered from social media and open web content, leading to more precise and real-time analytics. Our open source platform also allows for integration and development of customized risk assessment tools and visualizations that can be tailored to your specific needs.