The problem of premium leakage is widespread in the P&C insurance industry. Verisk Analytics estimates that personal lines automobile insurers incur $29 billion in annual losses due to premium leakage, and that 8% commercial P&C policies have some sort of ownership misrepresentation resulting in a loss ratio that is double that of other policies studied.
Premium leakage is premium and/or revenue lost due to misclassification, missed exposures, exposure changes, fraud or the failure to recognize and address other material facts related to premium. Basically, premium leakage results from misapplying or missing any premium- affecting factor.
Premium leakage may occur as a result of a confluence of different factors: customers provide incomplete information, agents are not incentivized to thoroughly vet and exclude risks, underwriters do not have effective capabilities to gather and process information. At the heart of premium leakage is the issue of information asymmetry in insurance contracts: the fact that the customer almost always has material information that is not disclosed, either intentionally or unknowingly, to the carrier.
This information asymmetry is aggravated by underwriting fraud, particularly when an agent perpetrates the fraud on behalf of the customer to earn business. Carrier instituted incentives such as policy discounts and commissions elevate conflicts of interest and chances of fraud in the system. A case in point is discounts that carriers provide based on customers meeting certain criteria e.g., being a good student, or being a member of an affinity group. Agents may fraudulently employ policy discounts to get customers into signing an insurance policy.
The business case for addressing underwriting fraud is often made on the basis of preventing premium loss for the carrier and taking actions against bad actors. Often neglected in the analysis are the more insidious longer term effects and the impact on insureds and agents, and the softer aspects of fraud management.
Information asymmetry in insurance manifests itself most prominently as moral hazard and adverse selection. Moral hazard in insurance is characterized by insured’s reckless behavior which leads to carrier losses, and adverse selection incentivizes riskier and higher cost individuals to acquire insurance, leaving out individuals with lower risks and lower costs. Underwriting fraud does not just rob carrier’s premium, but it worsens moral hazard and adverse selection by blunting the carrier’s ability to separate good risks from the bad ones.
That has strategic implications not just for the carrier but also for the agents and insureds. Higher loss ratios ultimately cascade into smaller incentives such as contingent commissions for agents and dividends for policy or stock holders. Providing a small innocuous discount to a policy where it is not justified may be overlooked. After all, the customer gets a lower rate, the agent gets his commission, and the carrier wins the business. But that oversight is strategically destructive since it discounts the overall longer term impacts of such behavior in the aggregate.
Carriers’ reluctance in taking action against underwriting fraud is understandable. Proving fraud may be difficult, fraud investigations team may get challenged by the sales and distribution leadership especially if their top producers are affected, and agent punitive actions may backfire. The business case for managing underwriting fraud does not have to rely just on hard premium savings and hard action. Knowledge of underwriting fraud behaviors can enable carriers to fine tune their business processes and management system to appropriately shape agent and insured behavior. Carriers are well advised to focus on solving the root of the problem, the information asymmetry that plagues insurance contracts, and knowledge of fraud behaviors can illuminate pathways to do that.
Max Kanaskar is CognitiveScale’s Financial Services AI Advisor. In this role, Max works with financial services organizations (including insurance companies, banks, asset managers) on their AI journey—from strategic insight into how to develop AI competencies and centers of excellence to more tactical development of AI roadmaps and delivery of AI solutions.