Is RegTech the answer to the ineffective terrorist financing controls of South African financial institutions?

François Combrinck, capacity architect, Ovations.

South African financial institutions are ineffective in preventing the financing of terrorism and the financing of the proliferation of terrorism. It depends a report published by the Financial Action Task Force (FATF), a supra-governmental body that sets the policy frameworks for combating money laundering and the financing of terrorism around the world. As a result, the terms terrorism financing and proliferation financing (or FT/PF in financial crime compliance jargon) have been heard with increased frequency in conference calls and the hallways of financial institutions in South Africa. .

But what is the financing of terrorism and what is the financing of proliferation? And what is the difference between the two?

Simply put, terrorism financing is the provision of funds for terrorist acts, while proliferation financing is the provision of funds for the manufacture or acquisition of weapons used in terrorist acts. For example, a financial institution that opens an account for a non-profit organization that receives donations and then commits terrorist acts facilitates the financing of terrorism. A financial institution that provides financial services to a chemical manufacturer that sells its products to a terrorist organization that uses these chemicals in the manufacture of bombs, facilitates the financing of proliferation.

Detecting the financing of terrorism or the financing of the proliferation of terrorism seems quite easy at first glance: many organizations around the world (the United States Office of Foreign Assets Control, or OFAC; the United Nations; the European Union; the Her Majesty’s Treasury in the United States United Kingdom, among others) publish lists of known terrorists and terrorist organizations, and the South African FIC Act has incorporated regulations specific to one of these lists (the list of United Nations Security Council). However, a simple one-dimensional search of these lists to see if new or existing customers appear on the list is not enough, and the FATF agrees.

Fully aware of being listed in these databases, these individuals and organizations as well as their supporters and suppliers hide behind opaque organizational structures to avoid detection. To determine whether clients are linked to terrorists or terrorist groups, it is important to keep track of the ownership structure and management of client companies and verify this information against third-party sources. This verification should not only be performed when onboarding the client, but also at regular intervals to ensure that the financial institution’s view of its client’s ownership structure and management is up to date and to confirm that None of the owners, parent companies, subsidiaries or directors appear on any of the lists of terrorists and terrorist organizations published by governments around the world.

However, screening customer names against these government-issued lists is not enough. To detect hidden links with terrorists and terrorist organizations, it is crucial that a regular search for adverse information is carried out for all customers (or at least for high-risk customers) in order to find any potential links between the customers and known terrorists and terrorist organizations.

While this type of daily customer and related party screening seems complex and expensive, there are many tools available that use the latest news scraping, text analytics and machine learning technologies to make this type of filtering not only possible, but indefensible to overlook. by the AML/CFT compliance frameworks of financial institutions.

Sifting through news articles around the world and selecting lists of known terrorists and terrorist organizations is only the beginning of identifying terrorism and proliferation financing networks. The next, equally crucial step is to identify the networks through customer-provided information and monitor their transactional activity. Shared contact details, shared addresses, and shared IP addresses will expose hidden networks between an institution’s existing customers. Monitoring transactional activity, on the other hand, is crucial for identifying the hidden networks involved by the recipients of payments made and the originators of payments received.

This type of transaction monitoring to reveal hidden networks poses its own set of challenges. For starters, nominative beneficiary and originator data are not always reliable and even counter-account details for transfers are not readily available to most financial institutions‘ AML/CFT monitoring systems. Additionally, detecting hidden networks by monitoring payment flows requires organizations to pay attention to transaction values ​​well below what their risk appetite and existing operational capacity allow.

However, these problems have already been addressed by the latest financial crime monitoring tools using advanced analytics and machine learning. By using big data technology to process unstructured data, the latest financial crime surveillance systems can analyze hundreds or even thousands of data points to detect payment networks. The native machine learning capabilities of these solutions ensure that triggered alerts are not a false alarm. Additionally, using machine learning, these solutions can detect new transactional patterns that highlight potential terrorist or proliferation financing without human intervention.

Addressing the shortcomings identified in the FATF report on measures to combat terrorism financing and the financing of the proliferation of South African financial institutions may at first glance seem like an additional cost to add to the ever-increasing regulatory risk bill. and compliance. However, organizations that are brave enough to embrace the latest financial crime surveillance technologies may soon find that the added benefits gained from introducing RegTech into its stable of surveillance tools far outweigh the cost.

Marianne R. Winn