Increasing regulation, the growth in suspected cases of money laundering, and COVID-19 all mean that bank compliance departments are being pushed to the limit. And now an investigation by the International Consortium of Investigative Journalists has revealed the deficiencies that still exist in the fight against money laundering. Financial institutions also have some catching up to do when it comes to harnessing new technology.
Compliance pressures on banks increase due to record number of suspicious activity reports related to money laundering
The FinCEN files reveal how banks and regulators are struggling to combat financial crime
In Germany alone, the number of suspicious activity reports (SAR) related to money laundering increased almost twelvefold between 2009 and 2019. Switzerland has also seen huge growth in the number of suspicious activity reports related to money laundering, and in 2019 these hit new record levels for the third year in a row.
The pandemic has led to a rise in cyber crime
Bank compliance departments are also under pressure as a result of COVID-19. The pandemic has led to heightened security risks that cyber criminals are increasingly seeking to exploit. The result is a significant increase in fraud, cybercrime, money laundering, terrorist financing, and abuses of government stimulus packages and financial aid.
Flood of regulation poses challenges for banks
The last few years have also brought a flood of new laws and regulations for banks, including the EU’s action plan to combat money laundering. This year, Switzerland introduced CDB 20, a revised code of conduct for banks relating to due diligence.
There is no doubt that these developments make the work of compliance functions even more complicated and time-consuming. It is impossible for banks to cope with this situation using their existing procedures and staffing levels, but non-compliance with these regulations can result in heavy fines.
Digitalization is the key
Banks are required to show they have put in place effective procedures, such as compliance management systems. As they start to realize the seriousness of this situation, more and more financial institutions are turning to digital solutions. But they still have a lot of catching up to do when it comes to automated compliance and risk management systems.
Automated compliance and risk management systems
The answer lies in an intelligent combination of digitalization, analysis technology, automation, machine learning and artificial intelligence. New technologies help banks to analyze enormous amounts of data more efficiently, uncover suspicious patterns more easily, and recognize potential risks at an earlier stage. This slashes the time required to carry out complex analyses and checks, thus increasing efficiency and cutting costs.
Artificial intelligence and machine learning help financial institutions to analyze data based on automated, standardized, self-learning processes. They also flag up security risks or breaches of the law. A key issue here is the combination of prescriptive approaches – compliance criteria that are defined as business rules by bank experts – and predictive approaches (machine learning).
The benefits: more efficiency, lower costs
Along with analyzing huge volumes of data from different sources at high speed, these IT systems can also draw their own conclusions. This ensures compliance with legal requirements while increasing efficiency and cutting costs. More than ten of Actico’s projects have resulted in client savings of 30% to 50%.
The compliance departments of banks and insurance companies mainly use machine learning in sanction screening processes in order to detect money laundering and terrorist financing.
Whitepaper: Why successful banks and insurance companies now rely on machine learning in compliance.
The whitepaper highlights the increasing challenges faced by bank compliance functions. It explains how digitalization and machine learning work and the many benefits they offer. The whitepaper also contains 10 lessons learned that should be considered when implementing machine learning in compliance.
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