How Insurance uses Pattern of Life Analytics to its advantage

• More accurate underwriting: PoLA can be used to analyze a wider range of data about potential policyholders, such as their driving habits, health data, and social media activity. This can help insurers to get a more complete picture of a policyholder's risk and set premiums accordingly.

• Reduced fraud: PoLA can be used to detect fraudulent insurance claims. For example, PoLA can be used to identify patterns of behavior that may indicate that a policyholder is exaggerating a claim or committing fraud.

• Improved customer experience: PoLA can be used to personalize the insurance experience for customers. For example, PoLA can be used to identify customers who are at risk of lapsing on their policies and offer them incentives to stay insured.

Here are some specific examples of how PoLA is being used to improve Insurance today:

• Progressive Insurance is using PoLA to set car insurance premiums. For example, Progressive uses PoLA to analyze driving data from its customers, such as braking speed and acceleration, to set premiums more accurately.

• State Farm Insurance is using PoLA to detect fraudulent health insurance claims. For example, State Farm uses PoLA to identify patterns of behavior that may indicate that a policyholder is exaggerating a claim or committing fraud.

• Nationwide Insurance is using PoLA to personalize the insurance experience for its customers. For example, Nationwide uses PoLA to identify customers who are at risk of lapsing on their policies and offers them incentives to stay insured.