AI Articles and Analysis about Process automation

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).

Robotic Process Automation (RPA) is a rule-based software solution that automates repetitive tasks without any self-learning capabilities. It is not inherently artificial intelligence. RPA vendors now offer AI-tools as add-ons to their automation platforms. This includes RPA applications in banking where some form of AI, such as computer vision or natural language processing, is a part of the automation workflow.

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:

In the banking sector, supervisory organizations create and oversee the compliance rules that banks and other financial organizations need to follow. These compliance regulations are important for companies to carefully abide by, since non-compliance can potentially result in large fines or in extreme cases, even loss of banking licenses. 

Oil and gas companies face many of the same challenges as large banks and established insurance firms when it comes to searching through their backlogs of documents. They want to use the data stored within these documents to make decisions on where to drill and determine whether or not they’re in compliance with laws and regulations. 

Much of the artificial intelligence and automation discussion going on in the enterprise recently involves something that, in most cases, actually isn’t artificial intelligence at all: robotic process automation, or RPA.

Our sector-wide research suggests that natural language processing (NLP) is one of the more common AI approaches in banking AI use-cases today. Sentiment analysis is a capability of NLP which involves the determining whether a segment of open-ended natural language text (which can be transcribed from audio) is positive, negative, or neutral towards the topic being discussed.

Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions.