Using machine learning tools, this process can be further improved. The automated process allows claims to be automatically moved from the initial report to analysis and customer contact.

Some insurance companies are already automating parts of their claim-processing, resulting in significant savings in time and improved customer service.

The use of machine learning tools enables insurance companies to detect fraud much more quickly when compared with relying solely on human analysis.

The Healthcare area is the most effective area where machine learning can be beneficial during underwriting. Healthcare insurance aims to cover medical expenses incurred due to sickness, accident, disability, or death.

Since the healthcare analytics market is growing, data-driven approaches stand to benefit this area of insurance quite a bit.

Using machine learning-based tools, they can collect insights from large volumes of highly diverse data such as insurance claims, members, providers, benefits, and medical records. Healthcare insurance businesses can use these solutions to structure and process data, resulting in cost reductions, higher quality of care, and fraud detection.

There is a wide range of business workflows that require recurring documents, such as receipts, bills, insurance quotes, and many others.

At present, processing these documents is essentially a manual endeavour, and the available automated systems are brittle and error-prone.

If a system can automatically extract all of this information, it will significantly improve the efficiency of many business workflows by eliminating error-prone manual labour.

A virtual assistant or bot can be programmed to respond to a user's question in the most appropriate way. By integrating machine learning into a chat-bot system, we will be able to achieve the desired result of customer acquisition through the proper guidance.