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Analytics
Risk Modeling

Modeling is the process of mathematically combining data about past performance to make predictions about future events. A simple example of such a model is a baseball player's batting average.

Data about past performance (times at bat and number of hits) is combined into a mathematical formula (hits divided by times at bat) to estimate the probability that the next time at bat will be a hit.

This batting average tells you what you can expect from a player "on average" for their next time at bat. It also allows you to determine which of two players has a better chance of hitting the ball the next time they are up.

An insurance score works the same way. Data about past performance such as number of previous MVR violations, length of time since most recent claim and deductible amount is combined in a formula to determine the expected loss ratio of a policy. Such a loss ratio score allows you to determine which of two risks is the better.

Underwriters have been building models since the start of insurance. They have taken information about past performance and made assessments as to what is likely to happen during the period of the policy. This process becomes Analytics when statistical theory, mathematical formulas and computers are used to process massive amounts of data to come up with predictions or scores.

Models can be built to predict
· Loss Ratio
· Claim Frequency
· Severity
· Professional Liability
· Propensity to Renew
· Propensity to Churn
· Profitability
· Probability of Responding to a Marketing Campaign
· Probability of Fraud
· Probability of MVR violation

Modeling Case Study

Consider the costs and benefits of ordering motor vehicle reports (MVRs) on drivers as a part of the underwriting process. Ordering an MVR on every driver will ensure that every sur-chargeable offense will be found, but the ordering cost will be incurred on every driver.

If an MVR score is used to predict who is likely to have a sur-chargeable offense, all policies or applications could be scored and those least likely to have such offenses would not have MVRs ordered. A typical MVR model could result in not ordering on about 7.5% percent of all policies. The savings from not ordering on this group is greater than that missed on sur-chargeable offenses. This savings drops immediately to the bottom line.

This same score could be used by a carrier that only orders on some of their policies. In this situation, every policy would be scored in the MVR model and only those most likely to have violations would be ordered. In this way the "hit rate" for those with violations would be sharply increased while ordering costs could be kept constant.

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