Fraud Analytics in Insurance: What You Need to Know 2025

Fraud analytics means using data and technology to spot dishonest actions, especially to catch fraud early.

In insurance, fraud analytics helps companies identify when claims might be fake or exaggerated. Instead of having to check every claim manually, fraud analytics uses patterns and clues from past fraud cases to highlight suspicious claims. This saves time and money, and it keeps prices fair for honest customers.

It fits into the bigger picture by improving trust in the insurance system and making fraud harder to get away with.

Here are some basic things fraud analytics looks for:

• Unusual claim amounts compared to typical claims

• Multiple claims from the same person or address in a short time

• Inconsistent information between documents or statements

On a deeper level, fraud analytics uses advanced techniques like machine learning to detect subtle patterns humans might miss. For example, AI models can analyze thousands of calls or reports to find tiny clues that might indicate fraud, such as tone changes or unusual phrasing.

This is where tools like Strada come in. Strada uses voice AI and fraud analytics together by listening to calls and flagging suspicious behavior instantly, making it easier for insurers to act fast.

Some practical examples of fraud analytics in action include:

1. Detecting fake injury claims by comparing medical reports across many cases

2. Flagging suspicious call recordings where the caller's story doesn't match past claims

3. Using data from past fraud cases to improve ongoing claim reviews