Money laundering is a global problem that has far-reaching consequences for both the financial industry and society as a whole. To combat this, financial institutions (FIs) have implemented Anti-Money Laundering (AML) solutions, one of which is to maintain AML operations teams to detect and prevent financial crime. However, these operations come at a cost that can be measured not only in euros but also in hours, customer satisfaction, and reputation.
The most prominent and obvious cost is the cost of labor. The cost per year of a traditional AML operations team can vary depending on factors such as the size of the team, the location, the level of expertise required, and the tools and technology used. A traditional AML operations team typically consists of various roles such as AML analysts, investigators, quality assurance specialists, data analysts, and managers. The salaries for these roles can range from $50,000 to $150,000 per year, depending on the level of experience and expertise required. In addition to salaries, other costs associated with an AML operations team include employee benefits, training and development programs, office space and equipment, and technology and software expenses. These costs can add up to approximately 50% to 100% of the total employee salary costs. Summing it all up, a traditional AML Operations team with 10-15 members can cost the FI anywhere from $1 million to $3 million per year.
Other less obvious, yet very much visible costs that come with implementing traditional AML practices can be measured in hours and annoyed customers. Manually checking each trade document, customer profile, transaction details, transaction history, is an excruciatingly protracted load of work. In many cases, customers even have to wait up to several months to solve a simple problem, as the AML team, although hardworking and as efficient as they could be, simply do not have the capacity to look into every case with the speed and thoroughness. This would also risk the FI of missing important details and discrepancies. On average, a traditional AML team can only detect 10% of the red flags, meaning that almost all bad cases go unnoticed. When good customers are getting upset, and bad customers go unnoticed, it is a sign that something must be done.
Unfortunately, that is not all. FIs that fail to comply with AML regulations can face other significant consequences. They may be subject to substantial fines and penalties, ranging from thousands to billions of dollars, as well as legal expenses related to the proceedings. Moreover, non-compliance can limit a FI's ability to engage in certain business activities, as regulators may impose restrictions, such as license suspensions or revocations, hindering expansion and entry into new markets. In severe cases, individuals responsible for AML non-compliance may face criminal charges, resulting in fines, imprisonment, or both. Furthermore, affected investors may seek legal remedies through civil litigation, potentially leading to financial settlements or compensation payouts.
AML violations can also cause serious reputational damage, which may lead to a decline in business and potential withdrawal of investments. Rebuilding a tarnished reputation can be a lengthy and costly process. A loss of confidence in the FI could also take its toll on the bank’s share price. Take the case of Danske Bank, a Danish financial institution for example. The Russian Laundromat scandal, which came to light in 2017, revealed that Danske Bank's Estonian branch had been involved in a large-scale money laundering operation, facilitating the flow of billions of dollars of illicit funds, many of which were suspected to be of Russian origin, through its accounts. The revelations led to a loss of investor confidence in Danske Bank, resulting in a sharp decline in the bank's share prices. From the peak in 2017 before the scandal broke, the stock price dropped by approximately 50% within a year. Even though the bank has gotten back on its feet, it still has to bear higher compliance expenses and funding costs.
The solution is simple: Let the machine help. Using AI and Machine-Learning solutions, such as document classification, data extraction, name checking, sanction screening and vessel screening, currently all provided by Complidata, helps to reduce the time needed to perform checks, as dramatically as going from several weeks to only a few minutes. It would also help to detect more discrepancies and decrease the number of false positives by 2 to 3 times. The system could also be operated by less senior compliance officers, thus the FI could reduce the number of staffs to hire, and the seniority level required, easily saving a couple of millions of dollars per year. Opting for a digitised and automated AML Operations system means happier customers, and safer and more cost-effective transactions.