Skip to main content

Tackling financial crime with AI

(Image credit: Image Credit: Michael Dain / Flickr)

Financial regulators around the world are cracking down on banks. With Anti-Money Laundering (AML) and Know-Your-Customer (KYC) procedures being put under the microscope, huge fines are being levied against institutions which are found to be in breach. In fact, recent study discovered that over the past ten years, banks across the globe have been slapped with a total (opens in new tab) of US$26 billion in monetary penalties for AML and sanctions violations. As banks and financial institutions embark on digital transformation initiatives to streamline and simplify the customer onboarding process and reduce risk associated with fraud, many are eyeing the potential of emerging technologies. 

For the purpose of AML, AI can mine huge volumes of data for risk-relevant facts. This enables financial institutions to simplify the process of identifying illicit client relationships, beneficiaries and links to criminal or terrorist activity during the onboarding phase.

Artificial Intelligence 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. AI technologies, including natural language processing (NLP) and machine learning (ML) together, can 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.

AI’s NLP, which allows it to “read” vast amounts of information in any language, can enhance the KYC process for new client onboarding applications through intelligent document scanning and improve its ability to sift through a vast array of external data sources. These functionalities can significantly improve the overall client onboarding experience.

From an AML perspective, banks and all financial institutions can harness AI to intelligently extract risk-relevant facts from a huge volume of data, making the process of identifying high-risk clients even easier in the global fight against financial crime. 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 know your customer (KYC) alerts to the bank prompting them to perform regulatory outreach to customers to collect the outstanding information.

The adoption of AI requires not only new technology but also a shift in mentality. There is still a knowledge gap within the sector on how to change the traditional model of compliance and what tangible effects it will have over an organisation. For a more accurate view on what AI can bring, 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 automate the creation and updating of the client risk profile and match this against the classification process (i.e. high, medium and low-risk) to ensure continued compliance throughout the client life cycle. Furthermore, AI can make the process of identifying high-risk clients even easier for enhanced due diligence processes. It creates an association framework that improves the whole process of documenting, analysing and storing client information.

2. AI-driven UBO (Ultimate Beneficial Ownership)

AI’s ability to “read” vast amounts of data (including unstructured text) and derive meaning can help in producing comprehensive, accurate and auditable risk profiles on companies and individuals in a matter of minutes. This can add huge benefit to compliance teams who are tasked with weaving through complex webs of data on shareholders, beneficial owners, directors and associates. AI will also improve their ability to draw accurate conclusions for a risk-based approach to compliance. This will gain even more significance over the coming years, given the enhanced global focus on the identification and ability to perform customer due diligence on Ultimate Beneficial Owners in the wake of the Panama and Paradise Paper scandals. To improve transparency national registers have been established globally and AI can further expediate the identity of UBOs by being able to gather and link all the relevant data to create a simple view of complex relationships. 

3. Automated anti money laundering

A recent Dow Jones-sponsored ACAMS survey (opens in new tab)  on Anti-Money Laundering reveals that the area of false positives is one of the most challenging for bank compliance teams. Underpinning the alert generation process with AI can result in a fewer number of false positives. While they are a significant part of the AML compliance process, alerts are not enough to support an effective and thorough investigation process. What is required is the linking of high-quality data to the alert (via interpretation and link analysis) to produce an accurate, graphical representation of the legal entity structure. AI can improve the accuracy during the identification of false positives and help to leverage previously performed steps in the alert investigation process to formulate a recommended next steps approach.

4. Faster and simpler onboarding  

When applied to workflow automation, AI can transform the generation of documents, reports, audit trails and alerts/notifications. Artificial Intelligence has the power to eliminate hours of tedious manual effort by reading and analysing incredible amounts of information, which results in an improvement on timelines and also a much simpler process.

5. Future-proof against changing regulation

In the past few years rapidly evolving regulation has increased the pressure on compliance departments and that has created a new financial ecosystem with new challenges. AI’s ability to detect patterns in a vast amount of text enables it to form an understanding of the ever-changing regulatory environment, and so, it gives a clear advantage to those institutions who adopt these practices as a standard solution and invest in innovation. Furthermore, NLP can analyse and classify documents, extracting useful information such as client identities, products and processes that can be impacted by regulatory change, thereby keeping the bank and the client up-to-date with regulatory changes.

AI can be instrumental in helping banks fight financial crime and many of the major global financial institutions are already investing in the technology needed to harness its full potential. Most financial firms are still in the experimentation phase however, with increasing scrutiny from regulatory bodies, the application of AI in AML may become commonplace sooner than we think.  

Karl Seagrave, Head of Innovation, Fenergo (opens in new tab)
Image Credit: Michael Dain / Flickr

Karl Seagrave is Head of Innovation at fintech provider Fenergo, the digital enabler of client and regulatory technology for financial services.