Why is policy check lag 65+ days?
The insurance industry is a $120 billion business that has often been slowed down by delays and inefficient processes. In this blog, we discuss the reasons why policy check lag is as high as 65+ days.
The burden of manual policy checking
Insurance companies rely on manual processes to complete & track policy checks. Manual policy checking processes lack flexibility and are difficult to scale. Each stage has its challenges and nuances that make it difficult to speed things up. The only way insurance brokers can review more policies with these time limitations is to hire, train and assign more workers to the task. Let’s face it, task-oriented, high volume work like policy checking is boring and mundane resulting in a very high rate of errors.
Forming teams of skilled insurance professionals armed with yellow highlighters and red pens to manually check policies that run into hundreds of pages isn’t efficient. Hours of combing through pages and pages of policy language, coverages and endorsements are extremely tedious work and this is why critical information is missed or overlooked. In reality, employees merely spot-check 10 to 50% or less of the policies due to lack of manpower.
Bringing in centralized processes and standardization won’t help either. It is a 90’s solution to a 21st-century problem. This just allows the problem to be shifted to a different group of people who still need to spend time to review, highlight and notify Errors and Omissions (E&O) with perceived quality as against definitive quality. This often results in a lot of errors and omissions.
The most time-consuming processes in policy checking
Document comparison, often referred to as redlining, is primarily used to identify changes between two versions of the same document for editing and review.
Insurance professionals have to compare two dissimilar documents, like comparing a binder to a new policy or an existing policy or a new policy. As a policy and a binder are two differently structured documents of different lengths, comparing them manually is a navigationally difficult and attention heavy process.
There is a lot of information trapped in insurance documents. The challenge is to read, extract, classify, and analyze policy-level data trapped in documents hundreds of times. If the documents need to be digitized and more often than not, they will need to be; the data needs re-keying which takes additional time.
Red-lining is just the start. The insurance worker needs to pull the relevant pieces of information, assign value to the words, and intelligently analyze the policy and convert the unstructured information into a usable format. Most often the documents need to be compared against multiple documents such as the Current Policy/Proposal, Current Policy/Prior Term Policy, Current Policy/Quote and so on.
Recognize insurance-specific data points
A key step in being able to recognize and compare the insurance-specific data points such as:
|Policy number||Producer||Insured name||Street address||City||State||Zip Code|
Visually using the policy checklist to identify potential errors and omissions such as missing endorsements, incorrect limits, address errors, or premium shortfalls are laborious and need skilled workers to spend countless hours manually reviewing lengthy and complex documents.
The challenge of manual, repetitive, nonstandard workflows
Another cause of slow down that adds to policy check lag is the manual, repetitive, nonstandard workflows. There is a lack of intelligent workflows when dealing with high volume, repetitive manual processes. This issue crops up in different parts throughout the policy checking lifecycle.
There is usually an absence of an audit trail to keep track of errors and omissions, their identification and corrections, along with timestamps and sign-offs. Add to this there is usually no easy way to cross-reference and verify all documents at once to validate accuracy. Redundant software, legacy systems and outdated processes conspire to make it difficult to check each policy against relevant source documents and flag variation. All these issues add to the already extended policy checking timeline.
Exdion Policy Check: Automate to win
If you want to get rid of the inexorable wait that is a part of the policy checking process, you need to embrace policy checking technology. Read how Exdion’s clients have benefited from automated workflows that help them shorten the policy check lag from 65+.
We automate policy checking using AI, ML and NLP. Imagine a system that helped you check documents of different sizes, without the need for complicated systems or huge investments. Exdion Policy Check is that system and it helps you scale your operations from 1 to 100 in a short period.