With the constant rise and use of technology, Artificial Intelligence (AI) has become a great companion to compliance. Compliance is one of the biggest playing fields and plays a pivotal role in banking institutions. It aims to identify, diminish, and manage risks such as insider trading, spoofing attacks, exploitation of the market, front-running, and more by ensuring that banks operate with integrity and adhere to policies, laws, and regulations during the decision making process. In this post, I will dive into the concept of Artificial Intelligence and compliance, and share some thoughts about why it matters and how to achieve better compliance with AI. In relation to financial services such as Banks, the compliance department is the body responsible for ensuring that the institution as a whole remains in accordance with set rules or standards.

Money laundering is the crucial mechanism utilized by criminals to inject proceeds of crime to the financial system. The primary responsibility of the detection of suspicious activity related to money laundering is with the financial institutions. Most of the current systems in these institutions are rule-based and ineffective. The available data science-based anti-money laundering (AML) models in order to replace the existing rule-based systems work on customer relationship management (CRM) features and time characteristics of transaction behaviour. However, there is still a challenge on accuracy and problems around feature engineering due to thousands of possible features. Aiming to improve the detection performance of suspicious transaction monitoring systems for AML systems, in this article, we introduce a novel feature set based on time-frequency analysis, that makes use of 2-D representations of financial transactions. Random forest is utilized as a machine learning method, and simulated annealing is adopted for hyperparameter tuning. The designed algorithm is tested on real banking data, proving the efficacy of the results in practically relevant environments. It is shown that the time-frequency characteristics of suspicious and non-suspicious entities differentiate significantly, which would substantially improve the precision of data science-based transaction monitoring systems looking at only time-series transaction and CRM features.

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.

R&C: To what extent are compliance departments turning to new technologies to help manage financial crime risks? Compared to legacy compliance systems, what kind of opportunities does the latest financial technology offer financial institutions (FIs) in identifying and responding to suspicious activities? LaScala: We have seen an increase in compliance departments leveraging new technologies to manage financial crime risks. Tools, such as machine learning (ML), artificial intelligence (AI) and robotic process automation can increase effectiveness and efficiency of anti-money laundering (AML) programmes. Specifically, they can help automate repetitive tasks and produce more valuable alerts so that compliance departments can better identify risk and spend more time investigating potentially suspicious activity. R&C: How are regulatory authorities responding to FIs' use of technology tools?

New compliance technology such as AI and intelligent tagging has the power to change compliance. Our webinar which brought together in-house experts and external subject matter specialists has shed light on the latest cutting-edge technologies and how they can help solve the many day-to-day challenges faced by compliance professionals across the globe. In today's rapidly changing regulatory landscape, it is critically important for banks and financial institutions to respond to new regulations with agility, while ensuring that the customer experience does not suffer. These dual demands put pressure on compliance departments. Compliance technology in the form of end-to-end controls capable of mitigating a multitude of financial crime risks can help to alleviate this pressure.

For the last two decades, virtual reality (VR) has been the technology that, as the old joke goes, "will always have a bright future." VR developers have sought new applications beyond gaming and entertainment, but progress has been limited, by and large, to training and simulation situations. Then, along came big data and artificial intelligence, and everything changed for virtual reality. We no longer speak in terms of virtual reality or even augmented reality, but in terms of extended reality (ER), with technology bringing many different types of data and realities into an environment that is both insightful and shareable. One of the more interesting forms of extended reality could be the pairing of VR with artificial intelligence (AI). Partnering VR and specific forms of AI, such as robotics, natural language processing, and machine learning, among others, creates an extended reality where participants become part of a virtual ecosystem and can interact with and dissect data within their real-world field of view.

Using AI engines, firms can check employee interactions conducted via media including emails and recorded phone calls to check if their language and conduct complies with legal regulations. By leveraging AI, legal groups can implement a proactive approach to communications compliance monitoring, constantly and thoroughly reviewing material in real time with a level of efficiency that would be impossible using traditional manual techniques. Intelligence for Compliance Huge volumes of data including conversations from phone recordings, chats and emails can now be analyzed using cognitive engines built specifically to understand noncompliant language. Cognitive engines can augment compliance-tasks, streamline processes and analyze data quickly and efficiently so employees can spend their time on real issues that impact a business's bottom line.

For financial firms, the back office faces a similar challenge digesting, analyzing, and discovering the answers that it wants from the ever-growing amount of data as the front office. Artificial intelligence-based platforms that use data analytics give compliance departments an alternative to throwing a conference room full of lawyers to review data manually. It is not a panacea, according to Wayne Matus, managing director, group investigations at UBS and who spoke on an AI panel hosted by Exiger. "There is a surge of the quantity and quality of data, which let us get to the truth much more accurately for the firm time," he said. "There is no substitute for the person who can see the pattern that the machine cannot."

Financial Services compliance departments are constantly turning to technology to find efficiencies and satisfy increasingly tough regulatory examinations. It started with simple robotics, which can provide great operational efficiencies and help standardize processes. Never ones to rest on their laurels, compliance departments have begun looking to Artificial Intelligence (AI) as the next technological step to enhance and improve their programs. PayPal has cut its fraud false alerts in half by using an AI monitoring system that can identify benign reasons for seemingly bad behavior. HSBC recently announced a partnership to use AI in its Anti-Money Laundering (AML) program.