Loading (StaticQuery)

How to Boost Claims Inspection with Machine Learning

Using image classification to create more accurate scoping reports.

The hype around ML

Businesses and industries across the world are turning to machine learning and implementing it for its practical advantages. It’s true, ML offers unparalleled opportunities for business owners and in recent years, the advancements in technology have generated a lot of buzz around machine learning and deep learning.

The benefits of enabling technology to automatically assess and learn from large sets of information are insurmountable. Not only does ML help process massive amounts of data and provide actionable insights, but it also helps boost productivity for human beings. Imagine being able to read through thousands of pages of documents for relevant information, or examining data from a variety of sources simultaneously and make sound business choices more efficiently.

What’s in it for claims adjusters?

A lot of speculation is going around in the insurance industry that machines are going to replace claims adjusters for good and that remote technology will ultimately trump field adjusting. With the onset of virtual assistants and claims being handled by drones, this fear is not entirely unrealistic. However, what the industry is reminded of again and again is that claims adjusting is inherently a people’s job and requires the element of the human touch. It’s true that with machines replacing humans, claims cycle time would reach an unscalable level but the reassurance that a fellow human being provides is something technology cannot replace.

The uberization of claims adjusting is not a recent process and several insurtech startups and companies are heavily invested in implementing some form of automation in the claims management process. Whether it’s giving more control to policyholders in the claims process via applications or enhancing the customer experience with automated interactions that don’t require a person to connect with an insured, there is certainly a technological shift that can be observed through the industry.

A similar shift can be seen in the claims adjusting space where the days of claims adjusters carrying just a laptop, a digital camera, and an audio recorder are gone. This is replaced with modern-age tools that enable adjusters to map out 3-D models of properties for highly precise estimations.

Leveraging image classification technology

In simple terms, image classification is the process with which deep neural networks play the role of analyzing hundreds of thousands of pictures with the primary goal of identifying and detecting certain objects present in it. This technology has primarily been used in a variety of industries ranging from healthcare to e-commerce to automobiles.

When it comes to claims inspections, adjusters have to deal with thousands of pictures that showcase damages in a property. These images are then segregated according to the claim they belong to and also require proper annotations to help create accurate reports. With such a large number of pictures, the possibility of making a mistake increases.

However, with the help of image classification, claims adjusters can simplify this process and boost their claim cycle time as the entire operation of identifying different pictures and associating them with individual claims is automated. This not only helps claims adjusters to save considerable time creating reports but also clock in more claims throughout the day.

A great way to approach this method is to use applications like JustEZ which has features like smart image grouping that helps group all the images based on the objects present in them and AI-based image captioning technology that can caption 80+ photos in 3 minutes. By providing sentence suggestions based on the loss description, you can create highly accurate reports that depict the damage clearly so you don’t have to invest time in writing every little detail on your own.

The takeaway

Machine learning can greatly optimize the claims inspection process for adjusters and help them save time and close claims faster and in a much more accurate manner. It takes years of experience for claims adjusters to accumulate the knowledge and expertise required to handle a variety of claims and it wouldn’t be fruitful if suddenly this process is automated and humans are rendered obsolete.

What true innovation would look like is a combination of new-age technologies and longstanding practices that form a synergy towards the future of the claims workforce.