Data analysis in the insurance industry involves the collection, interpretation, and effective use of information. Data collection can help agents, underwriters, claims processors, and others streamline and optimize operations, enhance policyholder relationships, and make more intelligent and more accurate business decisions — ultimately maximizing profits.
The best news is that the implementation of insurance data analytics can be easier than it sounds. That’s the result of living and doing business in the digital world — and becoming an integral part of Confie, a national leader in personal lines insurance focused on improving insurance outcomes through more intelligent, faster data-driven decisions.
Why Insurance Data Analytics Matters
Ever try driving blindfolded? Don’t accept the challenge. And don’t try to assess risk, set rates, handle claims, or otherwise conduct the business of insurance without having as much relevant information as you can accumulate. After all, you’re in the company of predicting the future. You can’t do that without knowing what happened in the past. You need to be able to extrapolate future events based on historical data you’ve collected, properly studied, and interpreted.
That’s what insurance data analytics is all about: delivering business intelligence for competitive advantage in several ways. Do it right and you’ll have powerful tools for data-driven decision-making.
Here are the gains you can make with such insurance business intelligence at your fingertips.
Understanding Customer Behavior
Understanding customer behavior is essential for accurately assessing risk and tailoring policies to meet their actual needs. Analyzing specific factors provides deeper insights into each individual profile. Here are some thoughtful questions to ask:
- How often and how well do particular auto insurance policyholders drive?
- What about their driving-age children?
- How regularly have they paid their premiums, and have there been any payment delays?
- How many claims have been filed on renters’ insurance for theft or vandalism in a specific neighborhood?
- What is the applicant’s occupation?
- What is their age?
- Do they hold a high-risk job?
- Have they experienced any significant work-related or other injuries?
Answering these questions will enable you to understand risk when considering writing a policy or determine why your policyholder has so many claims.
You want to know more than just negative factors. If a policyholder has a low claims history, you might want to reduce their rates and keep them loyal to your brand. They’re valuable customers to have and maintain.
The more you know about customers and prospects and their patterns of behavior, the better you can understand their motives and gauge their preferences. This type of customer analytics insurance approach helps agencies predict policyholder needs, personalize experiences, and retain valuable clients over the long term.
For instance, if you know they have kids entering high school, they’ll likely soon have more family members behind the wheel. What can you do for this family in terms of rate discounts or bundling strategies to reduce the hit on their finances with multiple drivers?
Data analytics lets you bring up opportunities before your policyholders have even had a chance to start thinking about car insurance. Do that successfully, and you might be able to capture their business for generations.
Reducing Risk and Fraud
The insurance industry in America loses more than $300 billion a year to fraudulent activity of various kinds. Individual insurance companies take the hit, and so do their customers in the form of higher rates for coverage. This has a profound impact on all parties, but what can be done about it?
One leading response is to identify suspicious activity or patterns. Digital data analytics does this by the use of algorithms that flag patterns of behavior or events that could be indicative of fraud.
Today’s data insights for agencies can be derived from a bundle of insurance technology tools that include artificial intelligence (AI), machine learning, and predictive analytics software specifically designed for the industry. The goal is to reduce the risk of fraud, ideally before a policy is sold, or at least in real time during claims processing.
Improving Insurance Operational Efficiency
The idea here is to use digital automation to streamline workflow and improve customer service. That’s a worthwhile goal for all involved parties. This is achieved by making better products available, executing pricing strategies more quickly, processing claims with greater speed and accuracy, and reducing or eliminating the mundane administrative tasks that consume time. Don’t forget, time is money. Data tools for insurance are available to help you maintain optimal customer relationships and streamline the way you do business.
Types of Data Analytics Used in Insurance
There’s a fundamental digital transformation in the insurance industry going on today. Data analytics plays a big part in that ongoing revolution. Capabilities for agencies of all sizes come from various forms, including the following.
Descriptive Analytics: Understanding the Past
This more basic insurance technology is used to sort out and interpret past events for clarity and to identify patterns and trends. Descriptive analytics can be used to examine company revenue figures, to assess risk, and to review claims.
The results are often used as a foundation for more advanced data technologies, such as predictive analytics for the insurance business.
Predictive Analytics: Forecasting Trends
This sophisticated technology is a collection of data tools and processes that use past performance or behavior to predict future results. Predictive analytics can be used to gain valuable data insights for agencies in product underwriting and price-setting, claims processing, fraud detection, and customer service management, among other pursuits.
Use it to mitigate risk in product sales and to operate your business to maximum efficiency.
Prescriptive Analytics: Guiding Decisions
Unlike descriptive and predictive analytics, which examine what happened in the past and what could happen based on developing trends or patterns, prescriptive analytics takes the next step. It uses data to determine what should be accomplished.
Insurance companies can use the information generated to recommend new products to meet the specific needs of policyholders, make decisions during claims processing, manage risk and fraud, and recommend other actions for a positive outcome.
Learn How Confie Can Support Your Analytics-Driven Growth
At Confie, insurance data analytics has been a vital part of our success strategy for many years. We share this approach with our merger and acquisition partners all across the country, aiding in their growth as well.
Consider joining Confie to accelerate your insurance business growth. We have more than 160 fully integrated insurance agencies as part of our nationwide network. All have full access to the latest technologies and data tools for insurance. We’ll help you build your line of business with products, strategies, and tech advancements that will help a market leader.
Contact us today at (714) 252-2500 or drop us a line to learn more about our insurance services, franchise and acquisition opportunities, careers, leadership team, and more.
FAQs
How Can Analytics Improve Customer Retention?
Insurers who are responsive keep customer loyalty. The use of automation makes your operation more efficient, and data analytics help you get ahead of product demand and claims processing, to the satisfaction of your customers.
Are Small Agencies Using Data Analytics Too?
Yes. Smaller agencies are swiftly catching up with the data analytics activities of their larger counterparts, and it’s no wonder why. Tools of this relatively new insurance technology enable resource-challenged insurers to match the efforts of competitors with larger budgets, with smaller workforces.
What Tools Are Available for Insurance Analytics?
Digital solutions from artificial intelligence and machine learning to business intelligence (BI) platforms are available and commonly used by leading-edge insurance companies. Technologies such as the Internet of Things (IoT) and blockchain technology can be used to gain valuable insights into customer behavior and to streamline business operations. In short, there are data tools for every insurance analytics activity in the industry.
