Insurance companies utilize data mining to quickly identify suspicious patterns in customer behavior, particularly in relation to insurance claims. Examples include recognizing sudden increases in claims from a policyholder or a group of people filing similar claims, which could indicate potential fraud. Data mining is also employed to create predictive models assessing fraud risk based on policyholders' demographics, claim history, and other relevant criteria. However, the text emphasizes that while data mining aids in fraud detection, it is not infallible. The insurance industry faces the challenge of balancing effective fraud detection with avoiding false positives that can lead to unproductive investigations and frustration for policyholders. Continuous improvement of data mining models and algorithms is crucial to keep up with the evolving nature of insurance fraud.