As both businesses and society today continue to embrace digitisation at an uncontrollable rate; fighting financial crime, money laundering and the funding of criminal activity are all growing in importance. The global phenomenon of digitisation has led to an increase in wire banking, thus raising pressure on banks and financial institutions to monitor and detect suspicious activity to prevent it coming to fruition. It is therefore crucial that the banking system today adopts the latest tools for a mature digital service, which include self-learning capabilities that are able to quickly adapt to the ever-changing banking environment. The recent narrative has been primarily focused on regulators ramping up demands for greater scrutiny of transaction monitoring, as well as threatening to impose hefty fines on non-compliant banks. In Australia, for example,The Sydney Morning Herald recently reported that the Commonwealth Bank has come under extensive scrutiny following an internal review of the bank's breach of global anti-money laundering and counter-terrorism laws, with legal proceedings now being levelled against it.
The practical application of Artificial Intelligence (AI) by banks and financial service providers will be a hot topic in 2018. While seemingly a recent development, banks have in fact deployed AI to improve efficiency and lower costs for well over two decades. Starting with the use of Natural Language Processing (NLP) disciplines across several banking processes in the 1990s, the emergence of big-data and cloud computing drove the adoption of additional Machine Learning capabilities by banks. Robo advisors and fraud detection are two recent examples of AI applications in banking. Inflection Point Despite the long history of AI deployment, we are now at an inflection point in the transformation of banking.
The menace of trade-based money laundering (TBML) is an increasing, yet often under-reported, financial and reputational risk to banks and a growing concern to governments and regulators. Transnational crime is worth up to $2.2trn each year and much of it is facilitated by various forms of trade-based money laundering. A PWC report stated that 80 percent of illicit financial flows from developing countries are accomplished through trade-based money laundering. With sums of this magnitude, it is not surprising that banks, who are often the (unwitting) facilitators of this illegal activity, are coming under increasing pressure from regulators to take greater action to limit this growing international crime. For banks, TBML, disguised under the huge volumes of legitimate trade, is also extremely difficult to detect.
Temenos (SIX:TEMN), the banking software company, expanded its financial crime mitigation product to include an AI-based Suspicious Activity Prevention solution protecting banks and their customers from fraud. Demand for financial compliance and the increasing levels of financial crime are putting huge pressure on banks. Their legacy processes have grown so complex with a high level of manual work for screening alerts and other fraud mitigation activities. Each manual step is inefficient and prone to errors. High level of false positive rates exacerbate this problem.
Money laundering is a massive financial drain on the global economy. The amount worldwide is estimated at up to $2 trillion annually, or about 5% of global gross domestic product.1 In addition to its size, the complexity of fighting money laundering and complying with regulations has escalated. The increasing number of banking channels, digital payment networks and alternative avenues (casinos, virtual currencies, transaction laundering) keeps financial services executives up at night. Meanwhile, regulatory expectations are more demanding than ever.