The nature and extent of money laundering (ML) risk among the financial technology (FinTech) sector remains poorly understood. Attempts to describe the entire FinTech sector as posing uniformly “high” or “low” ML risks are often oversimplified and unhelpful. Rather, the FinTech sector is a diverse and complex one, where a true understanding of how ML risk manifests itself requires nuanced study.
This white paper aims to improve the understanding of factors that may influence ML risk exposure across the FinTech sector. It provides an initial, high-level view of some commonalities and divergences among the wide array of FinTechs that participate in the FinTech Financial Crime Exchange (FFE). Among its findings are:
• The self-identification of detailed ML typologies among FinTechs remains a significant challenge, owing to the often-limited view of financial activity FinTechs possess. FinTechs would benefit greatly from guidance and typologies studies from regulators and financial intelligence units (FIUs) that address risks related to specific product types and delivery methods in further detail.
• The nature of ML risk FinTechs encounter varies significantly based on the size of their customer base, geographical indicators, product features and operational factors, among others. In some cases, the ML risks will be genuinely low, in others higher. The level and nature of risk present, and the way typologies emerge, will differ greatly from company to company, and across market segments. Indeed, attempting to identify a single “FinTech typology” can prove unproductive.
• It is therefore important that FinTechs are not all stigmatised as “high risk” when there is no clear evidence that they pose higher ML risks than banks or other sectors. FinTechs and the public sector can play a role in clarifying this picture so that the FinTech sector is not broadly painted as “high risk” in instances where it is unwarranted. This is critical to avoid widespread “de-risking” of the sector.
• The non face-to-face nature of FinTech business means that fraud-related crimes predominate as the most commonly identified predicate offence to ML. Varieties of fraud encountered include stolen card fraud and identity theft, as well as more complex social engineering frauds.
• A limited range of other predicate offences and ML typologies were self-identified among FinTechs surveyed and warrant further study to determine their prevalence across certain product types and customer segments. These include elderly/vulnerable victim abuse, human trafficking/migrant smuggling, and drug-related crimes. However, this information is still anecdotal, and the FFE intends to undertake further study of these trends to understand their significance.
• Depending on their structure and product features, even UK-focused FinTechs can be vulnerable to “smurfing” or “money mule” activity that may be of low values in any single instance but can be frequent and widespread, and sometimes difficult to detect.
• Limits on product usage and the heavily UK/European Economic Area (EEA) customer base of most FinTechs surveyed in this report will tend to limit their utility for large-scale international ML; however, where FinTechs’ products and services become more complex and extend in geographical reach, this may change.
• None of the FinTechs participating in this study has observed ML activity involving PEPs. Exposure to PEPs is low and the risk of laundering the proceeds of bribery and corruption is likely relatively small. The risks of laundering the proceeds of tax evasion are also perceived as relatively low.
Based on these observations, this white paper outlines recommendations for FinTechs, law enforcement, regulators and relevant international bodies. For example:
• FinTechs should undertake ongoing and robust assessments of their ML risk, and be willing to challenge commonly-held assumptions about the nature of that risk. They should look to harness their skill in leveraging data to build a more refined picture of ML typologies. By ensuring risks are identified, understood and controlled, FinTechs can help to change perceptions that the entire sector is uniformly “high risk.”
• FIUs and law enforcement agencies should engage FinTechs in broader efforts to identify and develop more detailed ML typologies, and in advancing operational strategies for detecting and deterring ML.
• Regulatory bodies should aim to provide more detailed guidance on ML risks related to products and delivery methods with relevance to sub-components of the FinTech sector.
• International bodies such as the Financial Action Task Force (FATF) can play a role in fostering a detailed understanding of risks across the global FinTech sector, ensuring that FinTechs are included in global discussions about ML, and in developing consensus about appropriate responses.
By David Carlisle & Florence Keen.
Original Source: FinTech Financial Crime Exchange