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The general premise of this article is different from most of my previous AI Power articles.

While most of the articles in this series have related to the near-term struggles for power between organizations and governments with regards to regulation, data, and international policy, this article will focus on the long-term trajectory that AI and technology are headed towards and what that means for the most powerful nations and organizations.

We spoke to Moe Tanabian, General Manager of Intelligent Devices at Microsoft, who is speaking at the AI Edge Summit in Mountain View, California on September 17 and 18. Tanabian discusses how to think about and reframe business problems to make them more accessible for AI, as well as AI at the edge, which involves doing AI processing on individual devices rather than in the cloud. The edge could open up new potential for business problems to be solved with AI. Tanabian also provides representative use cases of intelligent devices.

We set out to create a report that would be particularly useful for executives and business leaders who are looking to get started with an AI initiative, as well as IT and management consultants who want to competently and effectively guide their clients through AI adoption.

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.

Business process management (BPM) in banking involves the automation of operations management by identifying, modeling, analyzing, and improving business processes. Many banks already have some form of BPM for various process. For example, compliance processes at most banks tend to have some form of software automation in their workflows.

Retailers and financial institutions are adopting artificial intelligence and machine learning in their business to solve various business problems such as cybersecurity and document digitization. However, many of these companies are also using AI to improve their payment processes for their clients and customers. These types of applications are usually layered into an existing payments technology stack, which could include straight-through processing (STP) or robotic process automation (RPA).

This article was written and contributed by Thomas A. Campbell, Ph.D. and Jon Fetzer of FutureGrasp, and was edited and published by the Emerj team.

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.  

Since the advent of online banking services, customers have had several different ways of communicating with their banks. Banks need to monitor all of these incoming customer requests and respond to them in the most efficient way possible. Further, each of the various channels of communication represents a valuable way to segment customers to not only improve how they perceive a bank’s brand but also to market banking products to them better.

Although there are established use-cases for AI applications in the business world, the claims that AI vendors make about returns from their software are often exaggerated. What is also not apparent amongst the AI hype is that adopting AI and machine learning is far more challenging than it might seem.