The insurance industry is booming, with U.S. insurance net premiums totaling $1.22 trillion in 2018. Despite this success, there is still an opportunity for insurance providers to capitalize on insights discovered via data mining to restructure strategies and reach customers.

Here are four reliable ways insurance companies can employ location data to expand their customer reach, expedite claims, reduce risk, and detect fraud:

An effective customer targeting program is a critical component to every insurance company’s comprehensive marketing strategy. Insurers must collect and analyze customer data, interview customers, and envision the buyer journey from the customer’s perspective. Targeting helps insurers concentrate messaging on a subset of the general audience, improving the performance of marketing campaigns and the ROI of marketing spend by focusing on high-value prospects.

With a cutting-edge targeting strategy, insurers hold the ability to segment potential customers down to exact neighborhoods and blocks. They can utilize data analytics to determine an individual’s propensity to own a home and buy various insurance policies. In terms of current homeowners, insurers can determine their overall home value and housing type to pitch the homeowner an insurance plan that fits their unique needs. An exploration into potential buyers’ net worth allows the insurer to get an idea of how much the individual is willing to spend on an insurance plan — saving them time and energy throughout the prospecting process.

Big data supports insurers by offering them access to complex information from a variety of sources, including public records and proprietary databases. This data-driven approach makes claims and investigative processes more efficient by giving insurers the big picture on individuals and businesses quickly.

A newfound partnership with location analytics provides adjusters with digital customer location data and traffic maps. These insights help to ensure the safety of adjusters when visiting customers to document potential damage — easily identifying congested or blocked roadways and avoiding construction.

According to the Life Insurance and Market Research Association (LIMRA), nearly nine out of ten financial services companies have or are exploring the use of big data analytics to compliment the underwriting processes. Risk management is an integral part of the insurance industry. The level of risk involved determines prospective policyholders’ insurance premiums.

One of the biggest areas that could benefit from predictive analytics lies in determining the likelihood of certain weather events. Struck with close to seven hurricanes every four years, Florida’s experience with these natural disasters are an example of a major catastrophic risk. Insurers can combine claims information and historical weather patterns to predict the highest exposure areas. Insurance carriers could then incentivize policyholders who reside in hurricane-prone areas with discounts on premiums, or offer financial assistance methods.

Insurers can utilize location analytics to pinpoint fraudulent insurance claims. Insurance companies lose roughly $40 billion per year through fraudulent claims, often because they fail to determine which claims are legitimate and which may be fraudulent. To combat this issue, data mining is utilized to help adjusters target claims which have the greatest likelihood of adjustment. 

Handling fraud cases manually has always been costly for insurance companies. If the data is not analyzed thoroughly, fraud often goes undetected. For example, a person may claim that his home was burglarized. Upon further inspection, however, it is determined that he took the most valuable items out of the house prior to the incident in an effort to collect the insurance payout. By applying data mining techniques to past claims data, insurers are able to discover patterns that indicate which claims have a high probability of fraud.

With advancements in technology, the future of data-fueled insurance analysis is bright. Advanced data platforms will allow for customer, policy, and claims information to be conveniently stored all in one place. With this approach, new policy underwriting can take into account previous data claims when making policy edits. 

Gravy can help insurance companies determine the best data solution to help strengthen their strategies.

https://www.iii.org/publications/a-firm-foundation-how-insurance-supports-the-economy/introduction/insurance-industry-at-a-glance
https://www.aoml.noaa.gov/hrd/tcfaq/E19.html
https://www.fbi.gov/stats-services/publications/insurance-fraud