Machine learning is a subset of artificial intelligence (AI) that utilizes algorithms and computer power to sharpen the judgments organizations make about voluminous and disparate data. Simply put, it allows machines to learn how to perform certain tasks without being explicitly programmed to do so. Machine learning already permeates virtually every facet of our lives--it is used every day for image and speech recognition, digital assistants, cyber protection, consumer marketing, medical diagnoses, ferreting out proscribed content on social media platforms, ride-sharing apps, law enforcement, and in countless other applications. Machines could learn how to detect fraud simply by being provided with examples of previously seen fraud cases, and without the need for manually coded business rules. Machine learning helps optimize the mix between humans and machines in an intelligent, accretive process that "learns" as it goes along.

The findings have been published in the report titled, "Achieving Integrated GRC in an Interconnected Digital Age." According to the report, key factors impacting risk and compliance executives include the need and expectation of real benefits from digital technologies, such as big data, artificial intelligence (AI) and machine learning, as well as distributed ledger technology (blockchain) to bring measurable increases in efficiency to risk management operations. At the same time, many firms are facing challenges in moving toward the future. At a fundamental level, the report indicates that risk operations are having difficulty developing agile capabilities and continue to be hampered by inflexible technology. Thomson Reuters commissioned the independent survey to better understand the impact of these challenges and opportunities, as risk and compliance executives increasingly face intense, enterprise-wide regulatory scrutiny around strong governance and sound internal controls.