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Insurance Technology Trends

Authors Photo Precisely Editor | January 6, 2022

Without a doubt, the insurance industry is driven by data. Even before the rise of personal computers, the Internet, and widely available analytical tools, insurers relied heavily on insurance technology and statistical analysis to assess risk and price policies accordingly.

Dial labeled insurance.

Today, insurers have access to more data than ever before. This is a double-edged sword; larger volumes of data provide significantly greater input to sound underwriting decisions, yet many companies struggle with the volume and variety of available data, as well as with the speed at which that information comes at them.

Looking to the year ahead, here are some insuretech trends we see for 2022 and beyond. In many cases, these represent the continuation and acceleration of insurance technology trends that have already begun to take hold. As big data and cloud-native analytics evolve into a driving force across all industries, the impact is felt especially strongly within the insurance industry.

Data Management Is More Challenging Than Ever

It should come as no surprise that the first trend revolves around the continued growth of big data and cloud-native analytics as a key driver in the insurance business. There are a lot of challenges associated with the data-intensive processes that are so important to the insurance industry.

Executives complain of having too much information. They often face challenges with the lack of governance and structure, and poor data quality is a widespread concern. Additionally data often exists in silos, lacking effective mechanisms for reliable and consistent integration. The timeliness of information flows is often not up to the standards required by underwriters. Finally, information often lacks context, leaving users unable to tap into its full potential.

Is the data structured? Is it governed? Am I using the right data?  The answers to those questions determine the data’s usefulness in the underwriting process, and by extension, they determine its value in creating and maintaining a profitable portfolio.

Data integrity addresses these problems holistically by:

  • Integrating disparate data sources and making them available where and when they are needed
  • Proactively managing data quality to ensure accuracy, consistency, and completeness
  • Adding context through data enrichment and location intelligence
  • Providing an overarching governance framework

Read the Report

Future of Insurance Data

Read this report to see an exclusive snapshot into the state of insurance location data and how AI will effect the insurance industry in 2022 and beyond.

Investment in AI Is Accelerating

It should also come as no surprise that investment in artificial intelligence (AI) is on the rise among insurers. AI and machine learning are being used for an ever-expanding array of applications, including fraud detection to technical pricing and optimizing claims management processes.

Nevertheless, AI initiatives often face headwinds in the form of poor data integrity. Machine learning models are only as good as the data with which they’re trained. Data quality is paramount, and access to information from a variety of sources across the enterprise (as well as third-party data) is important.

Perhaps just as importantly, AI systems must be designed to accommodate change. Risk profiles are constantly in flux. The COVID pandemic, for example, brought about significant changes in driving behavior, leading to less traffic on the roads and fewer automobile accidents. Timely access to updated information ensures that AI investments are positioned to deliver optimal results.

The Rise of IoT & Mobile Devices

Some insurers have already begun to explore the use of IoT sensors as a tool in better understanding risk. In the auto insurance space, telematics devices and mobile phones are providing detailed information with respect to driving patterns and location, but there are other applications emerging as well, such as the use of IoT devices to determine actual usage time for insured equipment. Machines that sit idle much of the time presumably present lower risk than those that are in use for two shifts a day or longer. Just as auto insurers use actual miles driven to set a driver’s rates for the coming year, commercial P&L insurers can refine risk models based on detailed information about actual usage.

As IoT devices expand, and as new applications for mobile technology are developed, we expect to see significant expansion of machine-generated data used in the insurance industry.

Self driving car.

Location Matters More than Ever

Our mission of mobile technology serves as a kind of bridge to this next topic: location. The insurance industry once relied upon coarse-grained information broken down by a 5-digit Zip Code or census block. That has all changed, as the detailed information available has grown exponentially.

In many cases, location intelligence starts with a relatively simple question: “Where is this building (or person, or car) located?” Even this question, though, can be difficult to answer accurately and reliably. Effective geocoding serves as a very important first step in the process of resolving an entity’s location. With that information in hand, a whole new world of data and attributes add rich context to the location in question becomes available.

For insurers, this information can be highly consequential. Consider, for example, the case of an insured driver whose house is located on a corner lot. On one side, there’s a busy road, on the other a quiet side street. How much accident risk should we attribute to this location? It might depend on the location of the policyholder’s driveway. If it opens onto the major road, the risk may be relatively high. If it faces the side street, the driver’s risk is substantially lower.

Wildfire risk, likewise, is dependent on a property’s surroundings. That may include factors such as prevailing wind speed and direction, elevation, and proximity to combustible vegetation or other flammable material. Location intelligence provides this kind of rich tapestry of information that can shed light on a property’s risk profile.

Good Data Governance Ensures Regulatory Compliance

Finally, we expect continued regulatory pressure with respect to data privacy, data sovereignty, and governance. Europe’s GDPR continues to evolve as cases make their way through the courts. Other jurisdictions around the globe are considering similar legislation. Insurance regulators are keen to understand the risk models being applied by the companies they oversee. As the volume of data being used by insurers increases, regulatory scrutiny will continue to increase as well.

As we look to the year ahead, the case for strong data integrity programs will only get stronger. Precisely, the world’s leader in data integrity, helps companies to build trust in their data, enabling their users to derive insights from their data with confidence.  Read the report Future of Insurance Data to see an exclusive snapshot into the state of insurance location data and how AI will affect the insurance industry in 2022 and beyond.