AI Sector Overviews Articles and Reports

The military is always looking for ways to innovate its technology for weapons and vehicles, and it follows that AI and ML would become part of that work in the current decade. Currently, the Army is testing autonomous vehicles and aircraft for battlefield use. However, most AI applications for these vehicles do not have clearance to operate the weapons attached to them. 

Banks and other financial institutions can be tight-lipped about how they implement AI technologies within their businesses. Citi, however, has been relatively open about their current AI initiatives. Since 2017, they have published press releases and other announcements of AI initiatives that are both internal and customer-facing.

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.

Many of the top Fortune 500 retailers have begun using AI and ML to solve business problems for various departments. Walmart and Costco share one in grocery stocking, which includes the freshness and condition of the products along with timing the restocks for peak hours.

Banks are starting to deploy natural language processing (NLP) to make use of enterprise and customer data in text mining applications ranging from gauging customer sentiments to enterprise search.

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).

Identity fraud was the number one method of fraud that affected businesses globally according to The Communications Fraud Control Association's (CFCA) Fraud Loss Survey.