While researching underwriting leakage for an earlier blog post, I discovered another, more prominent type of leakage in insurance: claims leakage. While these two concepts are similar in nature, there is a fundamental difference between them that is worth exploring.
What is the difference between claims leakage and underwriting leakage?
- Underwriting leakage is the gap between optimal and actual underwriting (selection, pricing, and conditions).
- Claims leakage is the gap between optimal and actual settlement of a claim (payment of the claim according to the policy, correctly offsetting the loss).
Put another way: Underwriting leakage is a result of the inability to predict the future, while claims leakage is a result of the inability to accurately assess the past. Leakage is a fundamental concept for insurers because the insurance business is comprised of certain costs and assets balanced with uncertain liabilities. No other non-speculative business is so dependent upon an ability to know the unknowable.
What causes leakage?
A significant source of both types of leakage is process inefficiency. When an organization takes too long to underwrite a policy or settle a claim, or when too many different people are involved, the carrier is paying non-productive costs to perform the activity. Those non-productive costs are leakage costs. This aspect of leakage is addressed with specialized insurance workflow software to streamline processes and the application of business rules/policy conditions.
The other source of leakage is more interesting: accurately understanding the risk (for underwriting), or the loss (for claims). This is where the fundamental difference lies, and differences can be more revealing than similarities. Underwriting leakage can be reduced by using better models, data, and information to better understand the risk – i.e., to better predict a catastrophic event and the cost of that event. (Underwriters would best be served by a crystal ball, like a fortune teller.) Meanwhile, claims leakage can be reduced by better evaluating something that has already happened with forensic techniques and history and applying the terms of a policy appropriately. (Claims adjusters would best be served by a deerstalker cap and magnifying glass, like Sherlock Holmes.)
Can insurers avoid leakage?
Claims leakage (excluding process inefficiency) could theoretically be reduced to zero with robust logic, complete records, and solid forensics. The event that caused the loss has actually happened, and the adjuster needs to understand the event and apply the policy rules accordingly. However, there are practical limitations, such as the cost of determining an accurate claim must be balanced with the cost of the claim — some leakage might be accepted as a net savings. For example: Assigning an adjuster and sending her to evaluate a kitchen fire might produce more costs than the cost of a payment on the high side to cover uncertainty.
Underwriting leakage (again, excluding process inefficiency) can never be reduced to zero because it is impossible to know what is going to happen. The onus, then, is on the carrier to equip underwriters with the necessary data, information, and software to best be able to understand the likelihood of an unpleasant event happening, the uncertainty associated with that likelihood, and the likely effects (damage, costs) of the event. The savings in leakage costs is very difficult to quantify, though, as they will be realized over years of more profitable business.
What does this mean for the insurance industry?
The ability to pay claims should be relatively constant across markets, with solid adjusting practices, training, and process. However, carriers have a real opportunity to compete with each other on their ability to equip their underwriters with everything needed to reduce underwriting leakage — since there will always be uncertainty associated with underwriting decisions, there is always an opportunity to reduce that uncertainty. And it bears repeating: Uncertainty is expensive, and whatever reduces uncertainty saves money.