AI Articles and Analysis about Business intelligence and analytics

We've noticed a lot of interest from our readers for our pieces dealing with AI applications in the finance and banking sectors. New applications for artificial intelligence often seem to develop by transferring an existing use-case in a related field, and this might be the case with AI applications for ATMs as well.

The ability of companies to collect external data is likely to change the insurance industry as it is today. Traditionally, insurance companies would collect internal data from customers: data such as their weight, gender, and any family history of health issues. However, with the advent of technologies making possible the collection of data specific more to individuals than large groups of people, insurance companies could be able to offer unique, tailor-made policies to their customers.

Last year, Boston Consulting Group reported that banks were tracking three times as many individual global regulatory changes as compared to 2011. The report added that these institutions were averaging 200 regulatory revisions per day in 2017.

Asset and wealth management firms are exploring potential AI solutions for improving their investment decisions, and making use of their troves of historical data. In fact, according to our AI Opportunity Landscape research into the banking industry specifically, approximately 13.5% of the AI vendors in banking offer solutions for wealth and asset management.

Robo-advisors are digital platforms that provide automated, algorithm-based financial planning services with little to no human supervision.

Episode summary: In this episode of AI in Industry, we speak with Dr. Sanmay Das from the Washington University in St. Louis about risk prediction and management in industries like banking, insurance and finance.

Over the past decade, the industrial sector has seen major advancements in automation and robotics applications. Automation in both continuous process and discrete manufacturing, as well as the use of robots for repetitive tasks are both relatively standard in most large manufacturing operations (this is especially true in industries like automotive and electronics).

In this episode of AI in Industry, we speak with Brendan Kohler, the CTO of Co-founder of Boston-based startup Sentenai. Brendan speaks about how businesses today can effectively leverage the data being collected by their existing IoT and automation systems. Though example applications and use-cases, Brendan elaborates on what kinds businesses might use this technology today and what they can expect in the near future.