How is Computer Vision changing the Insurance Sector for Good- Top 5 Use Cases that take Center-Stage? — TechyWorld+ | by Shaip | May, 2022
Artificial Intelligence makes machines smarter, period! Yet, the way they do it is as different and intriguing as the concerned vertical. For instance, the likes of Natural Language Processing come in handy if you were to design and develop witty chatbots and digital assistants. Similarly, if you want to make the insurance sector more transparent and accommodative toward the users, Computer Vision is the AI subdomain that you must focus on.
Today, we are going to discuss the latter: the Insurance Sector, to be exact, and how Computer Vision is increasingly streamlining it with just the right set of innovations. For the unversed, Computer Vision is one of the few AI and Machine Learning applications that allow computing devices to understand and preempt scenarios based on visual inputs.
And if you want a heads-up as to which field has already been making the use of this technology, take a cue from autonomous driving and intelligent vehicles that can now better identify pedestrians, signals, and even driver emotions with well-trained Computer Vision models.
Top Computer Vision Use Cases that are Relevant to the Insurance Sector
The insurance sector, till 2019, was plagued with procedural challenges. Piling paperwork was an issue, and so was the element of bias that kept surfacing every time a claim was processed. Implementation of Artificial Intelligence eventually made life easier for the insured and even the insured.
However, the enhancements weren’t limited to automation and transparent claim processing. Here are some of the top use cases that have taken the insurance sector by a storm and might help the entire industry turn up the heat in the years to come:
The insurers are progressively relying on potent computer vision models to identify the growing number of frauds in the concerned sector. Computer Vision coupled with NLP empowers machines to scan and weed out fake images and invalid documents, thereby minimizing the instances of unscrupulous claims.
At present, fraud detection using Computer Vision is still an assistive use case as the alerts are sent over to humans for final evaluation. In time, we can see the models taking up the reins and making decisions independently.
‘How much claim to process’ happens to be one of the most important questions for the insurers to answer. Managing claims is a step-pronged approach involving damage estimation, claim registration, back-and-forth reporting, payments, etc. As there is a lot of paperwork involved, human errors are common.
Computer vision, paired with natural language processing, can streamline the entire process by identifying the exact nature and extent of damages and processing claims accordingly.
Here is a sub-domain of claims management that requires explicitly trained computer vision models to work. The process involves the insured uploading the images of the damaged vehicle online and the extensively trained model evaluating the same to cross-check the extent and veracity of the claim.
This tool is expected to save time for the insured and insurer by automated assessments and releasing the cleared amount instantly. However, high-quality training data needs to be fed into the models for systems like these to work.
Do you know anything about ‘Builders’ Insurance’? It is a risk insurance plan considered by builders that cover the under-construction buildings, construction material on-site, and even the employees.
However, the insurance companies, besides providing the insurance, also have Computer Vision-trained surveillance setups installed at specific locations to analyze the threat quotient of the concerned area whilst minimizing accidents.
These surveillance setups can visually identify if the builder is adhering to all the safety standards like necessary equipment for employees, safe-proofing the entire site, executing processes in the desired and safe way, and chances of mishaps if any.
When insurance-relevant customer support is concerned, AI models trained with quality data and annotation datasets can be a handful. Models trained in computer vision can interact with customers better, either as chatbots or as assistants, to understand even the most specific concerns. Therefore, visually trained models are absolutely necessary if insurance companies want to improve their customer experience.
AI technology is adequately disruptive and is arguably one of the best technologies that insurance companies can rely on to maximize output by minimizing frauds and misplaced claims. Even the insured can experience quicker claim settlements with intelligent machines working alongside humans to positively impact the insurance sector.
And while we build on these use-cases in 2022, it is a matter of time before AI, Machine Learning, Computer Vision, and NLP join hands to make this space more proactive by targeting some of the other complex bottlenecks in insurance, including premium assessment and still high settlement times.
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