AI Articles and Analysis about Business intelligence and analytics

According to Fortune, JPMorgan Chase is the largest bank in the U.S. and controls over $2 trillion in total assets. In this article, we detail the types of AI research JPMorgan is doing as well as how they are likely to be using their applied AI applications.

Predictive analytics is perhaps one of the most common AI applications used by financial institutions, banks, insurance companies, and healthcare companies. This type of software allows business leaders across these industries to plan for the most probable outcomes in business areas such as credit, loans, and patient health. Predictive analytics software could make predictions about future business events based on typical company experience using historical enterprise data.

The advent of machine learning in finance ushered in a keen interest in using AI to automate processes from fraud detection to customer service. While some use-cases aren’t nearly as established as others, our research leads us to believe that in the coming five years, banks will continue to invest in machine learning for risk-related processes, including underwriting.

Automated loan processing and underwriting is not a new concept in the banking and financial services industry. Lenders have consistently faced pressure to reduce the costs and time associated with internal loans processing and turnaround.  

Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT).

McKesson is a large pharmaceutical distributor that delivers health information technology, medical tools and supplies, and care management tools and software to healthcare providers in the United States. The company has been working to incorporate artificial intelligence solutions internally since 2012, but in more recent years they have adopted a few important automation tools. 

Ten years into the longest economic expansion on record, auto lenders are looking for ways to leverage new opportunities for growth and risk reduction.

Our research indicates that AI applications for risk-related banking functions are more numerous than applications for other business areas. Fraud and Cybersecurity, Compliance, Loans and Lending, and Risk Management collectively made up 56% of the AI vendor products in the banking industry, as shown in the graph below: