Machine vision offers a great opportunity for insurers to automate visual tasks and mitigate fraud.
Machine vision refers to the AI-based analysis (machine learning) of images from sources such as smartphones, satellites, or drones. In simple terms, machine vision is the eyes of applications and machines. It uses software algorithms to assess visual images based on existing data sets already assessed by humans.
According to Insurance CIO Outlook, machine vision can help property and casualty insurers simplify property assessment for claims processing. Traditionally, a claim adjuster would go on-site and assess the situation. By using drones programmed with machine vision, this process becomes more simple and safer, as the drone can use machine vision to obtain images and create 2D and 3D models for claims assessments.
In employee benefits, machine vision can greatly streamline the quoting process. Many requests for proposal still come in as images and PDF documents that cannot be interpreted as text by a typical computer. Moreover, client information cannot be copied and pasted from this format into the quoting tool, requiring manual rekeying of information by a human underwriter or salesperson whose time is better spent elsewhere.
This is where a machine vision technique called optical character recognition (OCR) comes in. OCR is the conversion of images to text (e.g. a photo of an RFP) into a machine-readable format. This enables insurers and distribution partners to generate a shell quote with information pulled from the RFP and begin working on a quote immediately.
Machine vision can also be used to improve the speed and accuracy of damage assessment and claims evaluation. For example, when a customer damages their vehicle, they can simply send a picture of the damaged area to their auto insurer, and the AI’s machine vision will analyze the images to determine the damage and claim amounts.
It can also help reduce fraud in claims assessment. Fraudsters usually think low-value damages go under the radar and are not assessed as thoroughly as higher-value claims. The neural networks can identify and filter out patterns of fraud cases or suspicious damage reports.
Additionally, machine vision also improves underwriting. It does this by intaking data from satellite images to find attributes insurers might find value in. Based on its findings, risk can be assessed leading to cost reduction for policyholders, higher quality of care, and improved fraud detection.