FATF, the non-democratic organisation that is creating rules in regard of anti-money laundering (AML) and countering terrorist financing (CFT) has officially declared that a surveillance society is necessary to combat crime.
HOW CAN NEW TECHNOLOGIES IMPROVE ANTI-MONEY LAUNDERING AND COUNTER-TERRORIST FINANCING ACTION?
Technology has the potential to make efforts to combat money laundering and terrorist financing faster, cheaper, and more efficient. It can process large volumes of information that go beyond human capability and provide data processing results in record time, releasing human resources for more critical work such as the analysis of complex ML/TF cases.
Multinational financial institutions, retail and commercial banks and internet-based firms such as Fintech take the lead when it comes to implementing new technologies. The advantages for the private sector include:
• Better identification, understanding and management of ML/TF risks
• Process and analyse larger sets of data faster and more accurately
• Efficient digital on-boarding
• Greater auditability, accountability and overall good
• Reduce costs and maximise human resources to more complex areas of AML/CFT
• Improve the quality of suspicious activity report submissions
Technology can also improve supervision through better live monitoring, exchange of information with counterparts, and a more informed oversight of regulated entities. The advantages for the supervisors include:
• Supervise a larger number of entities
• Better identify and understand the risks associated to the different sectors individual entities
• Live monitor compliance with AML/CFT standards and act in cases of non-compliance
• Communicate more efficiently with regulated entities and carry out additional information requests
• Store, process and report on larger sets of supervisory data
• Exchange information with other competent authorities
WHICH TECHNOLOGIES OFFER THE MOST POTENTIAL TO AML/CFT?
MACHINE LEARNING offers the greatest advantage through its ability to learn from existing systems, reducing the need for manual input into monitoring, reducing false positives and identifying complex cases, as well as facilitating risk management. The most useful applications of Machine Learning to AML/CFT include:
• Identification and Verification of customers through authentication AI, including biometrics, and liveness detection techniques (micro expression analysis, anti-spoofing checks, fake image detection, and human face attributes analysis)
• Monitoring of the business relationship and behavioural and transactional analysis by using Machine Learning algorithms to place customers with similar behaviour into cohesive groupings to monitor and alert scorings to focus on patterns of activity
• Identification and implementation of regulatory updates: Machine Learning techniques can scan and interpret big volumes of unstructured regulatory data sources on an ongoing basis to automatically identify, analyse and then shortlist it based on the institutions’ requirements.
• Automated data reporting (ADR) can make granular data available in bulk to supervisors.
DISTRIBUTED LEDGER TECHNOLOGY (DLT) may improve traceability of transactions on a cross-border basis, and even global scale, making identity verification easier. It may speed up the customer due diligence process, as consumers can authenticate themselves. They can even be automatically approved or denied through smart contracts that verify the data.
DLT continue to pose challenges from an AML/CFT perspective as they are decentralized in nature and enable un-intermediated peer-to-peer transactions without any scrutiny.
NATURAL LANGUAGE PROCESSING AND SOFT COMPUTING TECHNIQUES simulates human decision-making, as a result, it can take incomplete, ambiguous, distorted, or inaccurate (fuzzy) input and produce useful outputs. Like with all technological solutions, when they are integrated with broader monitoring systems, they should include an element of human analysis for specific alerts or areas of higher risk.
Application Programming Interfaces (APIs) connect customer identification software with monitoring tools, or risk and threats identification tools with customer risk profiles. An API will generate alerts or alter risk classifications as relevant. This can:
• Improve the interoperability between traditional banking data, moving away from siloed systems with fragmented frameworks.
• Increase automation and optimise resources and output accuracy.
• Supply an aggregated and normalized data feed, helping to build a more complete risk profile for new customers.
WHAT ARE THE CHALLENGES IN IMPLEMENTING NEW TECHNOLOGIES FOR AML/CFT? (…)
The unintended consequences of new technologies – such as ethical and legal issues, can arise from a misguided implementation of these tools.
New technologies must be adopted in a responsible, proportionate and risk-based approach manner, which maximises effectiveness gains whilst ensuring financial inclusion and the protection of underserved populations, data protection and privacy.
Operational risks and risk mitigants of new technologies, including unintended exclusion and privacy risks, are discussed in FATF’s Guidance on Digital Identity.
More information you will find in the full report Opportunities and challenges of new technologies for AML/CFT (pdf).
This means that the customer transactions (including all transactions by consumers) will be monitored in detail by advanced software systems, without any democratic or public scrutiny. FATF speaks friendly words in regard of care and inclusion, be aware as reality is different. We already know the major risks by the profiling systems of adtech companies. The risks of the systems of payment providers are higher and we already see the negative consequences of FATF-legislation in the de-risking practices of banks.
FATF lays the foundation for a surveillance society where there is no room for people.