As a bridge between humans and computers, the use of natural language processing in compliance demonstrates how this branch of AI is adding value across the financial services industry. Natural language processing (NLP) falls under the wider umbrella of artificial intelligence (AI) and essentially uses algorithms to help computers understand the everyday language of humans -- both spoken and written. As such, NLP is a fundamental bridge enabling interaction between computers and humans, and allowing machines to understand commands and input from humans in a seamless and streamlined manner. Once a machine can understand a human's everyday means of communication, the potential to add value becomes almost limitless. The Thomson Reuters Center for Cognitive Computing is constantly researching ways to perfect and advance different areas of AI, including machine perception, reasoning, knowledge management, and human-computer interfaces.
Machine learning and artificial intelligence have become buzzwords in the financial services industry. Daniel Fernandez helps to break down the difference between the two terms and explains how these technologies are being used by compliance departments today. By delving into how these technologies work, Daniel sheds light on the issues they can help to solve, the challenges facing their increased adoption and what financial institutions should be doing right now to take advantage. Like many industry buzzwords, artificial intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is this: AI has become an overloaded and misused term, often mistaken for machine learning (ML).
Given the increasing compliance demands on organisations from the public and regulators alike, companies cannot afford to neglect their compliance obligations. The role of compliance is to prevent, detect, respond to and remediate risk and financial institutions (FIs) must use all of the tools at their disposal to achieve it. Implementing an effective compliance framework requires the whole firm to be on board, from the C-suite down. The compliance function is in a state of flux, however – virtually unrecognisable from a decade ago. The catalyst for much of the change in the financial services industry was the global financial crisis.
Up until recently, compliance has mainly relied on people. And as a result of the significant increase in regulatory reporting requirements for financial institutions over the last decade, demand for compliance professionals has surged. Companies have had no choice but to hire more and more compliance staff, in an effort to tackle the growing regulatory burden. However, over the last few years, technology has begun to play a much larger role within compliance. Financial institutions and regulators have realised that by harnessing the power of technology, and more specifically, the power of artificial intelligence (AI) and machine learning (ML), a considerable proportion of the compliance function can actually be automated, reducing the burden on institutions and compliance professionals.
Like many industry buzzwords, Artificial Intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is – AI has become an overloaded and misused term, often mistaken for Machine Learning (ML). This blog aims to clarify the difference between the two, explain some of the complexities of implementing these solutions today, and highlight how ML can immediately add value in financial compliance applications. In simple terms, Artificial Intelligence enables computer systems to perform tasks that require human intelligence. Intelligence is the key word.