AI Anomaly Detection in Insurance: What You Need in 2025

AI anomaly detection means using computers to find unusual patterns or outliers in data that don't fit what's normal.

In insurance, these "anomalies" might be unusual claims, payment issues, or suspicious activities. Detecting them early helps prevent fraud, reduce errors, and speed up how claims are handled. Instead of checking every claim manually, AI anomaly detection scans large amounts of data quickly to spot anything that looks off.

Here's why AI anomaly detection matters in insurance:

• Find unusual claims faster

• Protect against fraudulent activities

• Improve customer service by resolving issues quicker

On a deeper level, AI anomaly detection works by learning what "normal" looks like from historical data. It uses algorithms that can handle lots of variables at once, like claim amounts, customer details, timing, and more. When new data comes in, the system calculates how likely it is to be normal. If it's unlikely, the AI flags it as an anomaly.

Technically, this involves methods such as machine learning models that can adapt over time and detect subtle changes. In insurance calls, AI anomaly detection can analyze voice data, tone, or inconsistencies in conversation to uncover potential risks. For example, it can identify suspicious behavior during customer interactions that might suggest fraud or errors.

Using Strada's AI-powered workflows, insurers can instantly detect anomalies from insurance calls. This means unusual patterns in conversations get flagged right away, and the system can automate follow-up actions. This automation leads to faster resolution of claims or investigations, saving time and resources.

Examples of AI anomaly detection in insurance include:

1. Spotting a claim with an unusual amount compared to similar cases

2. Detecting repeated calls from one customer suggesting possible identity theft

3. Flagging inconsistent information in claim-related calls

In practice, AI anomaly detection is becoming essential for insurers wanting to stay efficient and reduce risks in 2025 and beyond.