Insurance Applications and Data Analytics

I’ll be blunt: Most insurance applications stink. I don’t know who designs them, but whoever it is should be fired – unless you are a conspiracy theorist. If so, then you know insurance applications are written poorly on purpose so insurance companies can later deny claims when insureds provide incorrect answers.

This is an important point. For example, a large portion of cyber applications are warranty applications, and yet the questions asked are somewhere between nonsensical, obvious trickery, and most commonly, asked in a manner by which no honest applicant could ever state they are in complete compliance. These questions are often asked in an absolute manner. By this, I mean that to answer them positively, the applicant would need to be 100% sure that at all times and in all places they are in complete compliance with every law, every regulation, and even every standard operating procedure. Most applicants do not understand the implications of answering incorrectly, nor do they likely even understand the full question.

Another issue is when questions are asked so obliquely that it is impossible to decipher their meaning. I saw an app recently that included a question about carrying fuel specific to construction companies. Construction companies are always carrying fuel for their generators and heavy machinery. The question did not specify whether it was referring to the insured’s main purpose as fuel transportation or to fuel transportation for their local machinery.

Agents know the issues better than carrier people. I think one factor here is that these applications are old and were designed for knowledgeable human underwriters to review. By and large, knowledgeable human underwriters who actually review these applications are fairly rare these days, but the applications have not been updated. Another factor is the people writing the applications are simply clueless.

These poorly designed applications create unnecessary friction and costs.

First, if no one understands the question, litigation will ensue. The only ones benefiting are attorneys, and in this case, I hope the trial attorneys win. There is no good reason application questions cannot be written more intelligently. This is not rocket science.

Second, if no one understands the question, the answers will be wrong. How can a carrier legitimately claim underwriting prowess based on bad data? That’s just luck if the results are good. If results are poor, I have never seen a carrier review applications to determine how their results would have changed had they had better data with which to underwrite and price the risks. It seems like a no-brainer place to start.

Third, if the data is poor, exactly how is AI going to solve the problem? If a construction company has 50% of its vehicles transporting fuel at some point, but the application says no one transports fuel because the person completing the application sees the question as a business focus, rather than ancillary, including hauling five-gallon gasoline containers which just happened to explode due to a rear-end accident. When asked, the underwriter also didn’t know what the question meant. But the adjuster will inform the insured that their answer was not a legitimate response after a claim.

How is AI going to solve the problem when everyone interprets the answers to poorly written questions differently? The standard deviation is too high, which means there is too much variance in answers to make any reasonable conclusion whatsoever, other than the application question is horribly written.

The list of poor questions is nearly infinite. Even simple questions like, “Are you in a flood zone?” is poorly written. First, define flood zone. Which flood zone? By whose measure of a flood zone? And why should an applicant know all these nuances?

These questions are jokes, and carriers need to clean up their acts. They are only injuring their own attempts to harness AI and data analytics by collecting inaccurate data. Sure, it takes energy to write questions correctly, and budgets rarely exist for writing questions well versus spending big dollars on AI systems that will analyze poor data.

Insurance success is rarely determined by strategy. This industry is too sloppy for strategy to matter much, and much of what is called strategy is more appropriately titled a wish list.

Success is based on execution. Success comes to those who execute methodically, and because data collection begins with applications, methodical execution begins with well-written application questions. Given the dearth of quality applications, I assume carriers do not have anyone employed who knows how to write these questions. Let me know if you need advice on writing questions well. I am very happy to help.

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None of the materials in this article should be construed as offering legal advice, and the specific advice of legal counsel is recommended before acting on any matter discussed in this article. Regulated individuals/entities should also ensure that they comply with all applicable laws, rules, and regulations.