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).
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.
Artificial Intelligence (AI), long the subject of science fiction, is now becoming more and more widespread and is seen as an increasingly important computer science across multiple industries.In Financial Services in particular, Machine Learning and Natural Language Processing is increasingly used today to make sense of big, complex data in a wide range of areas. One such area is regulatory compliance.The use of AI -- particularly Natural Language Understanding (NLU), a subset of Natural Language Processing –can help firms to realise a number of benefits, including improving the speed and efficiency with which they achieve compliance, and making that compliance much more robust. As we've seen over just the last couple of years with the introduction of MiFIDI & II, UCITS, AIFMD and the like, there is a constant stream of documents being issued by regulators, which can each run to hundreds, or even thousands, of pages. Wading through those documents and trying to pick out the pieces that are important so that appropriate rules can be built, code can be written and reporting systems can be automated (for example), is an onerous task for human beings. In order to achieve compliance, many regulated firms take the approach of partnering up with third party consulting firms, paying large sums to them to help interpret the regulations employing people to write up what everything means from a rules perspective, then attempting to code those rules into their systems.