Data In The Insurance Industry
The Benefits of Big Data to the Insurance Industry and Insurance Programs
The insurance industry has always been driven by data, and today increasingly so with the availability of more data. Using aptly named “big data,” the industry is in a position to make more informed decisions regarding insurance program development, risk assessment, underwriting, pricing, claims investigations and settlements, client acquisitions, policy tailoring, and more.
What Type of Data Is the Industry Collecting?
Big data comes in two forms – structured and unstructured. Structured data, which is commonly referred to as quantitative data, is well organized and objective and enables us to easily count, measure, and express the data with numbers. Unstructured data is qualitative data that does not have a predefined structure and is subjective and interpretative in nature.
Examples of structured data include names, addresses, the vehicle makes and models, dates, claims history, and so on. This type of information is easily digestible and analyzed to make determinations about underwriting and pricing.
Unstructured data is information derived from social networks, emails, reports, recorded interviews, multimedia, photos such as satellite imaging, and devices (Internet of Things/IoT). Because of advancements in artificial intelligence and machine learning, this data can now be stored and analyzed computationally, making it even more valuable for insurers seeking to understand consumer trends and patterns.
How Is Data Being Utilized?
Risk Assessment: Big data technology can help improve the overall efficiency of the risk assessment process. Before making a final decision, an insurer, MGA, or program administrator can use big data and predictive modeling to identify potential issues based on client data and assign them to a suitable risk class.
Underwriting: Leveraging data from both structured and unstructured sources, underwriters can identify an insured’s operations and prefill hundreds of underwriting questions, including getting industry classifications right from the outset and obtaining a complete picture of a business’s risk profile.
Pricing: Telematics, for example, enables insurers to collect real-time data on driver behavior and usage in order to provide premium discounts and usage-based insurance.
Streamlined Internal Processes: Insurers can improve operational efficiencies by streamlining the application process. An example of this is a prefilled homeowners application. Big data can also be used by insurers to assess loss or damage in order to segment or, in some cases, automate claims. This makes it much easier for carriers to make important claims decisions, such as whether or not to pay a claim.
Fraud Prevention & Detection: Insurers can use predictive modeling to compare a person’s data to previous fraudulent profiles and identify cases that require further investigation. Furthermore, text analytics can detect potential “red flag” trends in adjusters’ reports.
Customer Acquisition and Retention: Capturing and analyzing a customer’s online behavior data allows insurers and agencies to create targeted marketing campaigns to acquire new clients. Tracking customer online behavior provides far more precise information than any survey or questionnaire.
Algorithms can also help predict the early signs of customer dissatisfaction based on customer activity. Based on the information provided, businesses can quickly respond to improve their services and find a solution to the customer’s complaints.
Tailored Insurance and Policy Development: Obtaining more detailed information combined with the ability for increased data segmentation enables insurers to develop policies and strategies for specific situations and exposures and cater to a wider range of clients.
The opportunities for improved risk analysis and pricing along with customized insurance programs and products to meet emerging trends and changing customer demographics are endless with access to more data and advanced analytics and insight capabilities.