Getting buy-in from senior management is one of the biggest hurdles to adopting AI in the fight against financial crime, indicating a need for a shift in mentality, says Fenergo’s Karl Seagrave.
As the war against financial crime heats up, regulators around the world are homing in on banks in breach of Anti-Money Laundering (AML) and Know-Your-Customer (KYC) regulations; and Asia is no exception. Recent research shows that in the past three years alone, Asian regulators have dished out more fines for non-compliance than ever before, demonstrating that no region is untouchable when it comes to regulatory sanctions.
Prior to 2016, fines across APAC were minimal, but the tide has well and truly turned. In 2018, one bank received the largest fine ever levied in APAC; a whopping USD 534 million for repeated contraventions of AML legislation. The MAS (Monetary Authority of Singapore) has levied nine separate fines totalling USD 21 million since 2016, yet prior to this they had not issued a single fine. Hong Kong has also ramped up activity in the past two years, imposing seven fines totalling USD 5.4 million, while Philippines regulators dished out their first fine in history (USD 23.1 million) in 2016.
But beyond the financial repercussions – which on their own are enough to make banks sit up and take note – is the burdensome task of keeping up with the growing number of compliance requirements. Aside from investing in additional staff and training, there is really only one way to effectively tackle the issue – automation.
The robots are coming
While there is still some fear associated with Artificial Intelligence (AI), the fact is, AI is particularly valuable when performing a set of repetitive tasks, saving valuable time, effort and numerous resources that can be refocused on higher client-value tasks. Automation is simply the best way to streamline the customer onboarding process, while reducing the risks associated with fraud.
For the purpose of AML, AI can mine huge volumes of data, automatically flagging risk-relevant facts faster than humanly possible. When used during the onboarding phase, AI can identify illicit client relationships, beneficiaries and links to criminal or terrorist activity, mitigating the risk of AML regulation breaches. The technology can automatically track changes in regulation around the world and identify gaps in customer information stored by the financial institution. It can then distribute KYC alerts to the bank prompting them to perform regulatory outreach to customers to collect the outstanding information.
AI technologies, including natural language processing (NLP) and machine learning (ML), can together create leapfrog automation opportunities across large parts of client life cycle management (CLM) in areas that are currently very labour-intensive, time-consuming and error-prone.
A shift in mentality
The successful adoption of AI requires a shift in mentality. According to recent research by Fenergo, the majority of financial institutions (68%) found that meeting regulation requirements, data management and improving data capture were the biggest challenges they faced. In APAC, meeting regulation requirements was the number one concern.
And yet, most (87%) of those surveyed said it was difficult to get buy-in from senior management when it came to investing in new CLM technologies. Despite wanting change, a mere 15% of financial institutions surveyed have fully automated the collection of client data. We still have a long way to go, however attitudes are changing. The same survey highlighted that 45% banks are planning to adopt AI within the next five years.
One of the key challenges for banks is not just how to go about implementing change, but the lack of understanding of the tangible benefits of AI. To break it down, here are five key ways in which AI can help improve AML/KYC and client onboarding processes:
1. Risk assessment and due diligence: AI can simplify the process of identifying high-risk clients by creating an association framework that improves the whole process of documenting, analysing and storing client information. It can automatically update the client risk profile and match this against the classification process (i.e. high, medium and low-risk) to ensure continued compliance.
2. AI-driven UBO (Ultimate Beneficial Ownership): With the enhanced global focus on the identification of Ultimate Beneficial Owners in the wake of the Panama and Paradise Paper scandals, a number of countries in APAC have taken steps to implement national registers of beneficial ownership. AI’s ability to “read” vast amounts of data and derive meaning can help expediate the identification of UBOs, gathering and linking all the relevant data to create a simple view of complex relationships in a matter of minutes.
3. Automated AML: A recent Dow Jones-sponsored ACAMS survey on AML revealed that false positives are one of the most challenging issues for bank compliance teams. Underpinning the alert generation process with AI can result in fewer false positives, due to its ability to produce an accurate, graphical representation of the legal entity structure.
4. Faster and simpler onboarding: AI has the power to eliminate hours of tedious manual labour by reading and analysing massive amounts of information; and when applied to workflow automation, can transform the generation of documents, reports, audit trails and alerts/notifications.
5. Future-proof against changing regulation: Mounting regulation requirements are putting compliance departments under enormous pressure; an issue which has created a new financial ecosystem with new challenges. AI’s ability to detect patterns in vast amounts of text enables it to form an understanding of the ever-changing regulatory environment, providing a clear advantage to institutions who adopt AI to keep the banks up-to-date with regulatory changes.
A powerful tool
With financial regulators around the world cracking down on banks and putting AML and KYC procedures under the microscope, the time has come for banks to make a change. And while keeping on top of regulation requirements can pose a significant challenge, AI – when implemented correctly – can be the best weapon in a bank’s arsenal in the fight against financial crime.
All it takes is a small shift in mentality and a willingness to try something new, to completely transform the way a financial institution operates.
Karl Seagrave is Head of Innovation at Fenergo.