Operationalizing Risk Data for Insurance
The use of location data has long been part of insurance technology trends. It touches on virtually every aspect of the business, starting with risk assessment and underwriting and extending to claims management, fraud detection, and new customer acquisition. Today, businesses have access to more location-based data than ever, including satellite imagery, mobility data from GPS-enabled phones and telematics devices, and detailed information on customers, structures, and vehicles.
The potential is enormous, and insurers are well aware of that fact. This realization has led to an environment in which virtually every player in the industry is vying for data supremacy. One observer has referred to this as the “data wars”, a contest aimed at leveraging advanced analytics and AI to gain an edge over the competition. Clearly, the data wars are already well under way, but as the volume and variety of available information increases, and as insurers find new and innovative ways to leverage that data, the battle for data supremacy continues.
Extracting meaningful business value from all of this available data often remains a challenge, though. Companies are already using data in a far more granular way than they did in the past. Yet a key barrier remains: How can companies operationalize data to give them a reliable view of risk, for a single property, for an entire building or neighborhood, or as an aggregated view of risk across the enterprise?
Unfortunately, most companies today are dealing with datasets that lack precision and accuracy. Very often they’re based on an estimate of actual location. To make matters worse, data is often trapped in siloed lines of business and disparate legacy systems, making a single version of truth about a property or a building extremely difficult to acquire. For most, incorporating third-party data sources is critical, yet many struggle to implement data enrichment strategies to use externally sourced information effectively.
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Accuracy and Precision Matter
When Harvard Business Review Analytic Services surveyed industry experts and analysts about the state of location intelligence data in the insurance industry, shortcomings in accuracy and precision emerged as key challenges. Top executives fully understand the opportunities available to them for reducing risk and increasing profits with location data, but the challenges associated with those efforts are significant.
Translating an address to a latitude and longitude, or geocoding is already a fairly standard practice in the insurance industry, but if the resulting coordinates are not hyper-accurate, then the resulting risk assessment will also be lacking. In many cases, an error of just a few feet can make a substantial difference with respect to the accuracy of a property’s risk profile.
Accurate geocoding is only part of the picture, however. At Precisely, we also attach a single, unique, persistent identifier to each property. Called the PreciselyID, it serves as a common link across multiple data sets, giving insurers a key advantage in managing and linking data across multiple systems and sources. It allows for straight-forward linkages across the organization with minimal processing time, as well as the ability to easily layer additional contextual data onto that unique identifier.
For any given address, the PreciselyID always remains constant. This provides a level of consistency over time, and it guarantees that the attributes linked to a specific location can always be accessed quickly and easily, without consuming vast amounts of processing power and excessive time. There are security benefits to this approach as well, as it can shield personally identifiable information (PII) from discovery simply by joining data to external sources using the PreciselyID.
Perhaps most importantly, the PreciselyID opens the door to thousands of additional data points about a given property. For North America, for example, the PreciselyID unlocks over 9,000 data points, including prevailing windspeed and direction, proximity of nearby structures, and proximity of combustible vegetation, crime statistics and more.
Turning Data into Business Value
One of Precisely’s insurance clients was aiming to insure the lower risk properties located in what were otherwise considered high risk wildfire zones in California. Precisely helped them to develop highly refined risk profiles for each property based on detailed location data. This enabled underwriters to identify the specific homes that could be insured vs. those for which coverage should be be priced appropriately. With accurate location data, including detailed information on wildfires, this insurer was able to focus on profitable policies in a high-risk geographic area, without incurring significant total losses. Of the properties it did insure, zero had total losses and claims paid were significantly below total exposure.
Analyzing data across multiple silos can also be a challenge. Consider a case in which an insurer wants to assess cumulative risk in a multiuse urban high-rise. This would require the consolidation of data across multiple business lines within the company. The building may be occupied by small businesses such as retailers and restaurants on the ground floor, and by a larger commercial enterprise or major chain hotel on the next ten floors, with personal lines for residential condominiums in the remaining floors. Assessing that kind of property requires effective master data management (MDM) that incorporates location as a key element linking those various properties.
The value of location intelligence to the insurance industry cannot be overstated. Location has always played a key role in risk assessment, but today it serves as the key that unlocks a world of new information, enabling insurers to understand risk at a far more granular level.
To learn more about how precision accuracy and location MDM strategies are becoming insurance technology trends, helping insurers understand exposures, reduce risk, and increase profitability, check out the free on-demand webinar, Harness External Data in Underwriting: Increase Profitability & Drive Value. Discover how insurers are combining internal and external data to assess risk more accurately and enhance profitability, delivering greater value to their customers.