AI Technology is Changing the Insurance Industry for the Better
The insurance industry is usually not a vertical that we tend to view as cutting-edge or innovative. We tend to reserve such labels for tech companies creating AI technology. In a previous article we told you about how AI helps insurance companies fight fraud, however, as we will see in the examples below, AI is also helping to transform the insurance industry by creating AI technology to meet their business needs. Specialized computer programs have been created that are able to recognize patterns and learn from them. These programs are able to do things like answer customer questions, appraise the damage and match customers with the right insurance policy and company. These tools are positively revolutionizing the auto insurance industry, which will benefit both drivers and insurance companies.
Helping Insurance Companies Process Claims Faster
Thanks to AI, a lot of the manual work required for processing claims can be automated. For example, Liberty Mutual is using AI in one of its initiatives, Solaria Labs, which experiments in areas like computer vision and NLP. The Auto Damage Estimator is one of the biggest results of these efforts. The system conducts a comparative analysis of anonymous claims in photos and is able to quickly assess the damage caused to the vehicle and provide repair estimates.
Similar solutions are used by CCC Intelligent Solutions, which digitizes and automates the entire claims process with AI. The photos that are submitted from accident sites are analyzed by an AI system in accordance with the rules provided by the insurance company. Based on this information, CCC is able to assess the damage and provide time estimates for insurers to approve and send to their customers for confirmation.
Mindy Support recently worked with an IT service provider to help them detect damage caused to cars but also provided the clients with additional services such as submitting a claim, appraising the damage, predicting the type of repair required, and a lot of other useful information. To help them train the machine learning algorithms, we annotated a dataset of 36,000 images with a quality score of 98%. You can learn more about this project in our case study.
Connecting Customers With the Right Insurance Company
When customers are shopping for insurance, they usually get only a few quotes and end up missing a great opportunity simply because they didn’t do enough research. In many cases, people don’t know about the offers that exist or can’t find a policy that matches their requirements. The traditional approach used by many insurance companies involves sending out a generic, random message to all possible customers, which can get annoying and costly over time. The other way is that companies manually analyze a customer profile and figure out the best offer for the customer; the problem with this is that it would take a lot of time to go through huge volumes of data and understand the eligibility of the customer and then come up with an offer.
One company is trying to change all of this. Insurify quickly matches customers with car and home insurance companies that fit their specific needs. The company relies on RateRank algorithms to determine policies that may be a good fit for each customer, depending on factors such as a person’s location and desired discount amount.
What Types of Data Annotation are Required to Create These AI Technologies?
Technology like the Auto Damage Estimator, which relies on computer vision to identify the damage, is trained with data annotation methods such as 2D bounding boxes and polygon annotation since the dents will not always fit inside a rectangular box. Also, since some of the damage might be hard to detect, such as scratches, semantic segmentation may need to be used since this is one of the most detailed types of data annotation.
If we look at the type of technology developed by Insurify, which needs to analyze lots of information, semantic annotation would need to be used, which is the process of tagging documents with relevant concepts. The documents that are used as training data are enriched with metadata: references that link the content to concepts described in a knowledge graph. This makes unstructured content easier to find, interpret and reuse. This means that human data annotators would need to perform tasks like sentence splitting, part of speech tagging, named entity recognition, and many other things.In addition to this, companies will need to hire QA specialists to make sure that all of the data annotation work meets the needed quality levels.
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September 20th, 2022Mindy News Blog