Revolutionizing the Fight Against Financial Crime
Artificial intelligence (AI) has the ability to completely transform how banks perform anti-money laundering (AML) and know-your-customer (KYC) compliance. Additionally, for the purposes of anti-money laundering, artificial intelligence systems can mine huge volumes of data for risk-relevant facts, simplifying the process of identifying high-risk clients. Fenergo’s CTO, Niall Twomey, discusses.
AI is particularly valuable when performing repetitive tasks, saving valuable time, effort and 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 labor-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. This can significantly improve the overall client onboarding experience.
From an anti-money laundering AML perspective, AI can intelligently extract risk-relevant facts from a huge volume of data, making the process of identifying high-risk clients even easier in the fight against financial crime. It has the ability to track the changes in regulations around the world, identify gaps in customer information stored by the financial institution and provide know your customer (KYC) alerts to perform regulatory outreach to customers to collect the outstanding information. Here are five key ways in which AI can help improve AML/KYC and client onboarding processes:
1. Accurate Client Risk Profile and Enhanced 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.
2. 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 and will 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 Papers scandal and the establishment of national registers to improve transparency in this area.
3. AML Screening and Investigation
A recent Dow Jones-sponsored ACAMS survey 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 fewer 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 help to leverage previously performed steps in the alert investigation process to formulate a recommended next steps approach.
4. Improved Client Onboarding and Document Management Automation
When applied to workflow automation, AI has the ability to transform the generation of documents, reports, audit trails and alerts/notifications.
5. Managing Regulatory Change and Compliance
AI’s ability to detect patterns in a vast amount of text enables it to form an understanding of the ever-changing regulatory environment. Furthermore, NLP can analyze 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. However, as financial institutions are still in the experimentation phase, it will be a few years before industry-wide deployment takes hold.
Niall Twomey is Chief Technology Officer at Fenergo, where he has responsibility for technical strategy, design and architecture. Prior to working with Fenergo, Niall spent over 10 years working with leading IT and consulting houses in financial services product development and system integration roles at Barclays Capital, Fidelity Investments and Accenture. Niall holds an honors MBA from UCD, Smurfit Business School of Business Dublin and an honors degree in Business Information Systems from University College Cork. 
