Physicists may say otherwise, but it is trade that makes the world go round -- at least financially. From supply chain issues to volatility in prices across asset classes, from stocks to crude oil, trade defines much of the movement in the international economy. With trillions of dollars moving daily across the financial system, the temptation to indulge in surreptitious behaviour is great. Regulators, compliance officers and banking leaders have long sought effective tools to combat the increasing sophistication of bad actors, whose wrongdoing frequently leads to billions of dollars in financial losses. Compliance officers and regulators are looking to identify criminal actions such as insider trading, market manipulation, money laundering, violations of sanctions/export controls and trading in others' accounts more accurately and quickly.

Increased use of AI can drive efficiencies and reduce costs in compliance management. Here's what that means for CIOs in highly regulated industries. Complying professionals can utilize automation tools rather than investing in additional solutions to decrease capital expenditures, expedite compliance, and increase flexibility. These solutions enable businesses across various industries to automate repetitive procedures, speed up business processes to increase efficiency and production, lower costs, and eliminate errors. Enterprises can expand the possibilities of automation with cognitive capabilities by combining RPA and AI, thereby increasing business value and competitiveness.

While regulations are created to protect consumers and markets, they're often complex, making them costly and challenging to adhere to. Highly regulated industries like Financial Services and Life Sciences have to absorb the most significant compliance costs. Deloitte estimates that compliance costs for banks have increased by 60% since the financial crisis of 2008, and the Risk Management Association found that 50% of financial institutions spend 6 to 10% of their revenues on compliance. Artificial intelligence (AI) and intelligent automation processes, such as RPA (robotic process automation) and NLP (natural language processing) can help drive efficiencies up and costs down in meeting regulatory compliance. In a single year, a financial institution may have to process up to 300 million pages of new regulations, disseminated from multiple state, federal, or municipal authorities across a variety of channels.

For years now, the SEC has quietly advanced its data analytics so that the staff is better equipped to examine registrants and identifier outliers. Information from this analysis is used to direct risk-based examinations of registrants, including investment advisers. The Office of Compliance Inspections and Examinations also uses data-rich tools such as the National Exam Analytics Tool (NEAT) to improve its review of areas such as trading. For policy-making efforts, the various SEC divisions rely on the Office of Data Science and the Division of Economic Research and Analysis, which collectively maintain the Quantitative Research Analytical Data Support (QRADS) program. The rule-making divisions can use the data to direct rulemaking or enhance analysis to make rules more effective (and litigation proof).

Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version. In reality, money laundering is an activity that traps 40.3 million people in modern slavery, fuels political unrest, and finances terrorism across the globe. Considering the consequences, it's no wonder governments enact AML regulations. And just as money laundering crime grows more sophisticated, so too do the regulations. These regulations have honorable and important intentions, but there's no denying the ever-evolving compliance headaches they create for financial institutions.

The rise of financial technology has drawn the attention of regulators to FinTech firms and how they function. FinTech companies like any other regulated institutions are required to comply with a growing set of regulatory rules. One of the latest examples is the FCA's roll-out of Secure Customer Authentication (SCA) for e-commerce transactions. This will mean that card issuers, payments firms, and online retailers will have to follow more stringent authentication steps for European online payments over €30. However, the real regulatory pain for FinTech's is felt when scaling fast whilst entering new markets. Catering to new obligations from different jurisdictions places further regulatory scrutiny on their operations, ensuring that they are compliant with the various nuances of different regulators' rules.

--Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two main tasks: fetching the regulatory announcements that actually contain changes of interest, and incorporating those changes in the business process. In this paper we focus on the first task, and present a Compliance Change Tracking System, that gathers regulatory announcements from government sites, news sites, email subscriptions; classifies their importance i.e Actionability through a hierarchical classifier, and business process applicability through a multi-class classifier . Na ıve Bayes, logistic regression etc.), hierarchical classification method, rule based approach, hybrid approach with various preprocessing and feature selection methods; and show that despite the richness of other models, a simple hierarchical classification with bag-of-words features works the best for Actionability classifier and multi-class logistic regression works the best for Applicability classifier . The system has been deployed in global delivery centers, and has received positive feedback from payroll compliance officers. Organizations are faced with rapidly changing regulatory policies, and ever-growing number of regulations.

Within the financial services sector, Anti-Money Laundering (AML) is a significant challenge for many institutions, often consuming large numbers of people and effort to manage the process and comply with the regulations. As a result, these same institutions are looking for new solutions to help them reduce the burden and increase the controls in this complex space. The combination of artificial intelligence (AI) and, more specifically, machine learning (ML), are increasingly being considered as enablers of a better solution. Despite its potential, however, adoption of AI and ML within Anti-Money Laundering has been relatively slow. This is due, in part, to the limited understanding of how AI and ML could be applied within compliance programs, and to the fact that regulators and compliance officers are often concerned that AI and ML are "black boxes" whose inner workings are not clearly understood.

With the July 2017 publication of its MiFID II Policy Statement, the Financial Conduct Authority (FCA) has signaled the final countdown to the most sweeping changes of governance, protection and transparency for a generation. While MiFID II will fundamentally change the business models of many firms, especially those in the advisory sector, there are also significant new technology requirements that apply to all firms and which are causing some to have sleepless nights. One of these new technology decrees is a mandate to record telephone calls for anyone either directly involved in trading or giving advice that may lead up to a trade. The intent behind this is noble, which is to provide transparency against firms or individuals giving bad advice. However, the new regulations don't end simply with the need to record these calls, but go further to require that firms actively monitor their calls to identify mis-selling or other bad practices.

"I've seen the future of Compliance and it's not looking good for my profession." I had meant to write this article for a while now and started drafting yesterday, so it was a bit of a surprise when I found an article in the Financial Times this morning, which in large parts at least, discusses exactly the same subject. Well, I must be up to something, but to be fair I'm by far not the only one. The bottom line of the FT article is basically that thousand of Compliance jobs are at risk because of automated compliance systems using artificial intelligence. Following the financial crisis, banks had increased the number of Compliance staff significantly and according to an estimate from Citigroup the cost for the banking industry is about $270 billion each year.