Why Fraudsters Target Your Company (And How Insurance Claims Analytics Helps)

Insurance fraud costs the industry billions annually while inefficient claims processes drive up operational expenses. This guide shows claims executives how advanced insurance claims analytics can identify fraudulent patterns, optimize workflows and reduce payout costs.

In brief:

• Insurance claims analytics help prevent fraud by making your company a less attractive target for fraudsters.
• Advanced analytics spot fraud patterns, assign risk scores, and optimize workflows to protect your business.
• Breaking down data silos and updating legacy systems are key to effective fraud detection.
• Start with small pilot programs and cross-functional teams to overcome adoption hurdles.
• Track KPIs like fraud detection accuracy and cost savings to measure success and ROI.

Insurance fraud drains $308 billion from the U.S. insurance industry each year. According to a study for the Coalition Against Insurance Fraud, these losses come directly from your customers’ wallets, costing each consumer around $900 annually through increased premiums.

Take a look at the financial breakdown of fraud costs in different insurance sectors:

• Healthcare Insurance Fraud: $105 billion per year
• Life Insurance Fraud: $74.7 billion per year
• Property and Casualty Fraud: $45 billion per year

Insurance claims analytics platforms can identify fraudulent patterns, optimize workflows, and reduce payout costs, making it much harder for fraudsters to succeed.

An old saying goes, “You don’t have to run faster than the bear to get away. You just have to run faster than the guy next to you.” Similarly, when it comes to defending against insurance fraud, you don’t need to be perfect — you just can’t afford to be the slowest person, especially when organized fraud rings operate like sophisticated businesses. There’s no room to be behind the pack.

Organized insurance fraud rings research your defenses, detect weaknesses, and swap secrets about the easiest targets on the dark web. If your fraud detection strategy still relies on static rules and manual reviews, your business is an easy target.

A poor fraud detection system creates a vicious cycle, attracting more fraud and leading to a surge in financial losses, reputational damage, and loss of consumer trust. This guide shows claims executives how advanced insurance claims analytics can help stop your company from being targeted.

How Insurance Fraudsters Pick Their Targets

Fraud rings operate with businesslike efficiency. They maintain databases of insurers’ weaknesses and share intelligence about which companies have mediocre controls. When they identify a carrier with weak defenses, word spreads quickly. You might go from minimal fraud exposure to a preferred target status almost overnight.

Centric Consulting National Insurance Practice Lead Sean Neben explains: “It’s kind of an arms race to protect yourself from fraud. If you turn out to be the insurance company that doesn’t have adequate controls in place to prevent fraud, you’re going to get a disproportionately large amount of it, and it could come on you really quickly.”

Here’s what might make you attractive for insurance fraud:

• Lots of high-value personal data
• A complex vendor ecosystem
• Being seen as outdated

To deter fraudsters from targeting your business, invest in an advanced insurance claims analytics platform. Advanced claims analytics transforms your defense strategy with smart fraud pattern detection, predictive scoring, process optimization, and thorough analysis of your provider network.

How Insurance Claims Analytics Can Fortify Your Cybersecurity Defenses

Advanced insurance claims analytics create defensive capabilities that adapt as quickly as the schemes they’re designed to catch. These include:

• Smart fraud pattern detection
• Predictive scoring for risk assessment
• Process optimization to reduce operational costs
• Network analysis for proactive detection

Let’s explore each of these defenses further.

Smart Fraud Pattern Detection

Traditional systems operate like a simple checklist. They can only catch things that break a specific rule, like a claim over a certain dollar amount. However, modern insurance analytics tools, especially artificial intelligence (AI)-powered systems, analyze everything and learn what “normal” looks like. They can quickly spot a claim that feels even a little suspicious by identifying subtle signs of fraud that might normally go undetected.

For example, the system might flag a policyholder filing small claims over a short time. On the surface, these claims look normal, but together they signal fraud. In workers’ compensation, advanced analytics can now automatically analyze medical records to quickly identify suspicious billing or treatment patterns from a provider.

Predictive Scoring for Strategic Risk Assessment

Automated claim risk scoring revolutionizes how you allocate investigative resources. Instead of relying on adjusters to manually find suspicious patterns, predictive analytics assigns risk scores based on hundreds of data points, identifying and routing high-risk claims for review. So rather than simply reacting to fraud, they become proactive and can target the highest-probability cases.

Process Optimization That Reduces Operational Costs

Advanced claims analytics can easily reveal issues and patterns in your claims processing. By identifying where delays occur and which steps provide little value, you can streamline operations without giving up accuracy.

For example, say you have certain types of claims that always need to be reviewed multiple times. Insurance claims analytics could help you assign the review work to adjusters from other groups or automate complex workflows. Insurance claim analytics helps when you’re reviewing the same information multiple ways or adding in support adjusters to verify your work.

Network Analysis to Uncover Organized Schemes

Relationship mapping creates a bird’s-eye view of your provider network to spot organized fraud schemes that individual claim reviews would miss.

For example, it can help identify fraud rings and collusion patterns that span multiple claims and providers, such as unrelated medical providers with suspicious billing patterns or networks of providers that consistently work with the same set of attorneys or claimants.

How to Overcome Hurdles in Adopting Insurance Claims Analytics

The biggest obstacles to successfully implementing claims analytics in the insurance industry aren’t technical, but organizational. Understanding the challenges upfront will help you map out a plan for a more straightforward implementation that also addresses potential hurdles.

Implementation Hurdle #1: Breaking Down Data Silos

Many insurers struggle with fragmented data across different systems. Your claims data might sit in one system, policy information in another, and provider networks in a third. This fragmentation limits the effectiveness of analytics because comprehensive fraud detection requires a complete view of relationships and patterns.

In my own experience, I had a long-term relationship with a credit card company, but when I tried to open a traditional bank account with the company, I was tagged for fraud.

I learned that the credit card business and personal banking business were two totally separate business units that don’t share data. Their fraud engine was running on a tiny amount of data, so they didn’t have the full picture of my history with the bank. Such siloed approaches can lead to false positives, missed opportunities for fraud detection, and lower customer satisfaction.

The solution involves creating data integration strategies that connect disparate systems. You don’t necessarily need to replace existing systems, but you do need mechanisms to bring data together for analysis.

Implementation Hurdle #2: Tackling Outdated Legacy Systems

Older claims systems often don’t have the features needed for modern data analysis, and they’re unfortunately a major roadblock to implementation. However, solutions such as batch data extraction, implementing middleware solutions, or using AI-powered document analysis can extract insights from unstructured claim notes.

Generative AI has revolutionized this area by turning previously unusable claim notes into valuable fraud detection signals.

“Generative AI eats that stuff up… You might look at claim notes and say, ‘This is garbage,’ but to a large language model, it’s gold,” Neben says.

Implementation Hurdle #3: Managing Team Resistance

Many workers are concerned that automation and AI will displace them. Focus on claims analytics as a way to help employees focus on complex cases requiring human judgment while automating routine pattern recognition.

To work around these hurdles, start with small, focused pilot programs. Identify specific claim types, regions, or high-volume areas for predictive analytics test programs.

Now, let’s discuss a specific road map for getting started.

How to Implement Claims Analytics at Your Insurance Company

Here’s a quick checklist to start implementing insurance claims analytics in your business.

1. Build a Cross-Functional Team

Build a cross-functional team across information technology (IT), claims operations, and legal teams to balance different perspectives and insights. Plus, include executive sponsors who can remove organizational barriers and allocate necessary resources.

• Claims Operations: Identify current workflows and pain points
• IT: Tackle technical implementation and data integration
• Legal: Ensure you follow regulations and meet privacy requirements

2. Target Quick Wins with FNOL Triage

First notice of loss (FNOL) triage offers an excellent starting point because it affects every claim, and improvements are easily measurable. Focus on areas with existing manual processes that immediately benefit from automated pattern recognition. Start with use cases that have immediate value and impact, such as:

• Automating medical record review for workers’ compensation claims
• Detecting provider billing patterns
• Duplicating claim detection
• Staging accident identification

3. Integrate Your Technology Stack

Selecting analytics platforms that already work well with your existing systems will prevent an entire IT overhaul, saving time and money. Modern solutions provide flexible options for integrating your systems through application programming interfaces (APIs), file exchanges and cloud-based processing.

Additionally, prebuilt analytics solutions, such as Centric’s Insurance Analytics Platform, offer fraud detection models specifically designed for the insurance industry. This reduces onboarding and implementation time, often accompanied by training and enablement support.

4. Establish and Track KPIs for Success

Establish clear key performance indicators (KPIs) that match your business goals to measure success and demonstrate return on investment (ROI):

• Fraud Detection Accuracy: Percentage of flagged cases that were actually fraudulent
• Investigation Efficiency: Time reduction in claims processing
• Cost Savings: Quantified reduction in fraud losses
• Coverage Metrics: Percentage of claims processed through analytics
• Resolution Time: Average days to close investigations

With a clear step-by-step process, your business can adopt new technology and avoid being an easy target for fraudsters.

The Reality of the “Arms Race” Mentality

Fraudsters don’t take a break while you work on your defenses. They’re working 24/7 to improve their own processes. For example, cybercriminals are now using generative AI to create more convincing documentation and fabricated identities that easily circumvent traditional detection methods.

Fraud detection is like an arms race. You must keep up with existing detection capabilities while proactively protecting your business for the future, racing against the proverbial clock to gain that competitive advantage.

Ready to transform your fraud detection capabilities? Discover how advanced analytics can protect your bottom line while streamlining operations. Contact the expert claims analytics specialists at Centric Consulting to build defenses that make fraudsters look elsewhere. Let’s talk