About $80 billion in fraudulent insurance claims are made every year in the United States, and this is just a conservative estimate. Many more cases of fraud go undetected because insurance companies don’t have reliable tools to identify them.

However, there’s a solution to this widespread problem. Using comprehensive data analysis for insurance fraud prevention, insurance providers can stop fraudsters in their tracks. This guide will show you how to use the latest data analytics tools to protect your staff, customers, and bottom line.

Insurance Fraud Prevention is Vital

The importance of insurance fraud prevention can’t be overstated. Even if just a few fraudulent claims go undetected every year, it can have a ripple effect on the entire company.

Below is an example of the impact that fraudulent claims have on your company, your customers, and the insurance industry as a whole:

  • You lose money on the initial claim. Even if the payout is just a few hundred dollars, this adds up over time when other fraudsters use the same scheme in the future.
  • Your staff wastes time processing fraudulent claims. That time would be better spent helping honest customers with legitimate claims.
  • Your customers pay more for their premiums. To offset the added costs of paying for fraudulent claims, you’ll have to charge all of your customers higher premiums. The FBI estimates that US families pay from $400-$700 more per year due to insurance fraud.
  • Your customers may have fewer options. If fraudsters take advantage of certain services, you might be forced to drop these services entirely in order to mitigate future risk. Your honest customers suffer due to the actions of a few dishonest policyholders or agents.
  • Fraudsters will continue to take advantage of the loophole or vulnerability. If you don’t implement an effective insurance fraud prevention system, your problems will only compound over time. You’ll be seen as an easy target for people looking to game the system.

The only way to stop this ripple effect is to create a more effective insurance fraud prevention system based on the latest data analytics technology. Data analysis allows you to identify more fraudulent claims than ever before. In some cases, it may even enable you to prevent fraud before it happens. Here’s how:

Protect Your Company and Customers with Data Analysis

Which data analytics tools are most effective for insurance fraud prevention? It depends on the line of insurance that you offer and which business intelligence strategies you already use. Every insurance company has slightly different needs. However, most insurance companies in all lines of insurance should follow the steps below to improve their insurance fraud prevention strategies:

Step 1: Govern your data. Collect quality data from a variety of reliable sources and organize it effectively. You should obtain as much data as possible. This may include demographic databases, past insurance claims, user data (social media accounts, cell phone records, and ATM use), the policyholder’s financial records, and data from nonconventional sources. Data analytics experts can help you identify which are most important for your industry, store it effectively, and normalize it.

Step 2: Set up a descriptive analytics model. A descriptive analytics model shows you basic trends and patterns in your data, including cause and effect. This is the first layer of fraud prevention—it helps you identify when and why past incidents of fraud occurred.

Step 3: Set up a predictive analytics model. A predictive analytics model takes what you’ve learned from your past data and makes accurate projections about the future. The best predictive models use machine learning algorithms to improve the accuracy of these predictions. These algorithms constantly evolve and learn based on each new data set or pattern. You’ll get a specific rating for every new customer you sign or agent you hire, which determines how likely they are to commit fraud. This helps you identify potential vulnerabilities in the system before any fraudulent claims are filed.

Step 4: Set up a prescriptive analytics model. A prescriptive analytics model takes everything you learned from the other two models and helps you plot a course of action. This model focuses on the business side of insurance fraud prevention. Now that you know why past incidents of fraud occurred and who is likely to commit fraud in the future, you can structure your business to make it much harder to file a fraudulent claim in the first place.

Step 5: Revisit your insurance fraud prevention strategy frequently. Fraud prevention is a moving target. When a loophole closes, fraudsters look for others. Using the data analytics models above, you can identify some of these early cases of fraud before they become widespread.

However, one of the challenges of using data analysis for insurance fraud prevention is that your organization may not have access to all of the most advanced analytics tools. Your staff may also lack the training necessary to use those tools. A data analytics firm can take care of these details for you, so you can start using all latest insurance fraud prevention innovations without delay.

Which Data Analytics System Offers the Best Protection?

There are many different insurance fraud prevention analytics tools available on the market. Choosing the best software and hardware for your needs is time-consuming, and requires an advanced level of data analytics expertise. This is why many insurance companies choose to work with an experienced data analytics firm.

Knowledgeable firms handle everything from software licensing to data visualizations and user-friendly data portals. You won’t have to create a machine learning algorithm from scratch or train your IT staff on how to use a new, complicated analytics software. You’ll just see results.

This is especially important if you want to maximize the resources you have. A few years ago, a top 20 life insurance & annuity carrier wanted to improve its insurance fraud prevention strategy. The company was using an inefficient analytics tool for the job. They used spreadsheets to visualize patterns in the data, which was time-consuming and sometimes inaccurate. Moreover, their data sources weren’t normalized, so they couldn’t make very accurate predictions.

To solve this problem, the insurance carrier hired Tek Leaders to revamp its fraud prevention strategy. We provided the company with a platform that reports, shares, and analyzes data all from one place. We also normalized the data, which made the reports and predictions more accurate. Our changes led to a 30 percent reduction in cost and a 70 percent reduction in annual maintenance for the company. Most importantly, with our help, the company’s fraud detection capabilities were three times more efficient. They were able to identify many more potential cases of fraud.

By opting for an all-inclusive insurance fraud prevention strategy provided by an experienced analytics firm, you’ll offer your agents, customers, and shareholders the best possible protection.

Insurance fraud prevention isn’t a one-size-fits-all solution—every insurance company has different needs. To find the perfect fraud prevention system for your company, contact Tek Leaders today. We offer advanced fraud preventative analytics tailored specifically for our clients. Or, if you have more questions about our fraud prevention technology, you can reach us by email directly.

Author: Devender (Dev) Aerrabolu

Devender (Dev) Reddy Aerrabolu is the CEO of Tek Leaders. His goal is to help SMBs bring value from their data. Dev helped Tek Leaders grow from scratch into a $25 million enterprise by focusing on clients’ data needs.