The Insurance Industry Embraces Artificial Intelligence

Nerd For Tech
Published in
4 min readNov 17, 2021


AI in the insurance industry

“It turns out that being insurance claims adjuster is to have a pair of sharp eyes, dealing with dozens of bills every day, finding the core information at a glance and recording it in the computer. But now, artificial intelligence technology can automatically extract the information from the bills.” an insurance claims adjuster said.

Currently, AI technology has been used in identity verification and insurance claims. For example, the sudden epidemic Covid-19 in 2020 has caused a surge in people’s demand for health insurance. Due to the intelligentization of insurance claims, many companies successfully responded to the rise in business while maintaining the same workforce.

AI technology has been applied in multiple business scenarios. For example, in terms of identity verification, insurance companies use face recognition for the entire process of online insurance, including online purchase, save from damage, and return visits, to help the system complete verification more quickly and reduce labor costs. In terms of insurance claims, OCR (Optical Character Recognition) technology has also been applied. In addition, R&D companies and insurance companies continue to carry out a technical enhancement to ensure the privacy of customer information.

OCR technology changes insurance claims

Claim settlement is the largest and most important scene in the insurance industry. Anyone who has been through the claims process knows that insurance claims are rigorous and complicated. When an insurance company accepts a health insurance claim application, the insured needs to submit a total of more than 40 types of documents, including medical bills, settlement lists, expense lists, expense receipts, and CT/MRI reports. Then, the insurance claims personnel enters the information into the system and form electronic records.

OCR technology is facing considerable challenges in the implementation of the insurance claims business. The materials submitted by customers often have vague and incomplete information, which prolongs the claims process. Especially in medical claims, there are more than 30,000 hospitals across the country, and there are 34 categories of various bills. The format, as well as the name of each field, are quite different. The information verification is also complicated.

OCR advantages

First of all, the automatic insurance claims settlement supported by OCR technology can reduce the rate of problematic items. The online claims image classification and quality inspection capabilities remind customers of problems such as missing data and unclear low-quality images to assist customers in completing the upload of claims data.

Secondly, it can accurately and quickly identify the contents of the bill. Through OCR technology, insurance companies have realized the optimization of bill blur, tilt, flip, content overlap, and information serialization.

More importantly, OCR technology can improve input efficiency. Through the structured analysis of the claims data, the manual input content is reduced. The claims staff can be shifted from the input to the review part. Take the expense list input as an example. Previously, it took 40 minutes to enter a single receipt manually. After adopting the intelligent solution, it can be completed within 10 minutes, and the efficiency is increased by four times.

How to train an OCR model

The model is required to provide the correct data for use. For example, if you are training a model to transcribe receipts automatically, your training data should include all the values you want to transcribe: name, amount, time, etc. For the model’s receipt automatic transcription function, the data should consist of receipts containing the value you are looking for. In addition, the data should also be comprehensive, including images from different angles, different types of image quality, and so on.

High-quality training data is required

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.


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