AI Articles and Analysis about Business intelligence and analytics

Mitsubishi UFJ Financial (MUFG) is a Japanese holdings bank and financial services company ranked 5th on S&P Global’s list of the top 100 banks, and the largest Japanese bank on the list.

UBS is a Swiss multinational investment banking and financial services company ranked 30th on S&P Global’s list of the top 100 banks. In addition to investment banking and wealth management, the company is looking to improve its tech stack through several AI projects.

Morgan Stanley is a US financial institution known mostly for its financial advisory services. According to our AI Opportunity Landscape research in financial services, approximately 10% of AI vendor products in the industry are wealth management solutions, and 4% are asset management solutions.

Progressive is one of the largest auto insurers in the US. The company has been experimenting with AI since the middle of the 2010s, with customer-facing applications that update insurance premiums based on driving habits and answer questions in a chat window. In this article, we discuss both of these AI use-cases. More specifically:

The top 100 global banks, including Goldman Sachs, are beginning to take AI strategies very seriously.

The retail industry collects massive amounts of data every day, and this makes its key processes ripe for automation with machine learning. Along with the manufacturing sector, the retail industry likely stands to benefit the most from one particular AI technique in the next few years: machine vision, also known as computer vision.

This article is based on a presentation given by Emerj CEO Daniel Faggella in Geneva, at the 2019 New Shape Forum: Weapons Governance for the Geneva Disarmament Platform. To learn more about Emerj's AI presentations and speaking, visit our presentations page.

Many large insurers are finding ways to digitize parts of their business process in preparation for future projects involving machine learning. This is especially true in claims processing, which could become faster and less error-prone if claims adjusters did not have to search through large amounts of data or paper documents manually.