AI Articles and Analysis in Finance

To some, facial recognition may feel like an AI technology with one chief use case and numerous niche ones that are not helpful outside of the client company that requested it. While security and anti-fraud solutions tend to dominate the conversation, there are many actionable possibilities for facial recognition software.

The insurance industry is dominated by large global firms that deal with thousands of customers filing insurance claims every day. Claims processing is a huge part of the insurance business process and improving turnaround time for each claim is critical to reducing operational costs at insurance firms.

AI applications for the insurance industry have certainly garnered a lot of press lately. We’ve previously covered such applications in the American and European insurance spaces. Countries in Asia such as China and Japan have large insurance industries and seem to have established national AI strategies.

Even during a time when the buzz around AI and digital data storage is prominent, there are still some companies with large amounts of backlogged data that are taking up space in their databases, but not providing enough value. Large stores of enterprise and customer data can be valuable to insurance companies for optimizing their business operations and gaining analytical insights on how their business decisions affect company growth.

Along with the rise in popularity of chatbots and simple conversational interfaces, there is growing interest around other natural language processing (NLP) capabilities in the banking, finance, and insurance industries.

When it comes to the possibilities for AI in the insurance space, it may seem like machine vision is an inevitable choice for any insurer looking to have as much information on the insured property, car, or people as possible. However, this may still yet to be the case for many insurers. The insurance space has a relative lack of case studies for this type of AI software. This may indicate a lack of demand from the insurance industry or a lack of traction on the part of the technology's use in insurance.

The applications of natural language processing (NLP) have been increasing as more companies find uses for their text data. This includes insurance companies with large stores of data from claims and customer support tickets.

Health insurance fraud may prove especially pressing due to the opioid epidemic, and thus routing out fraud could be of greater importance in the coming years.