Deltec Bank, Bahamas, ‘If technology is able to recognize patterns and behaviors across more channels in a faster time, the potential benefits are huge.’
Artificial Intelligence (AI) and machine learning have been around for some time and already automate many aspects of our lives from Google searches to shopping on Amazon and streaming on Netflix. Recently, it has come into more prominence across long-standing sectors such as financial services where it can solve real-world problems.
The regulatory pressure applied to the bank means they need to be stringent when it comes to their processes. As well as that, incidents involving finances will naturally be highlighted more in the press as they can deeply affect people.
AI has the potential to transform how banks perform anti-money laundering (AML) and know your customer (KYC) compliance. These are two processes that typically take up a lot of time and require significant data analysis to meet the regulation. Through automating those data processes, AI can revolutionize how the banking sector handles regulation.
What are AML and KYC
AML refers to a set of procedures, laws, and regulations designed to stop the practice of generating income through illegal actions. KYC is the process that helps banks identify their customers and evaluate any potential risks. KYC is a crucial part of an AML procedure. If the KYC process is done thoroughly, it should prevent financial service providers from being used either intentionally or unintentionally in money laundering crimes.
The problem with both AML and KYC is often very time consuming and tedious processes. Banks and financial service institutions tend to rely on manual processes for analyzing data and monitoring transactions. Emerging AI technology is offering a more automated approach that could be more accurate and efficient.
Automating KYC/AML with AI
There are many ways that AI can be used to automate KYC and AML tasks for financial institutions.
Identifying High-Risk Customers
One of the major benefits of AI comes in working out who the high-risk customers are. Machine learning algorithms can use pattern recognition techniques as well as text analysis (natural language processing) to put forward relevant customers for enhanced due diligence (EDD) processes. The models will pull together multiple sources of evidence and present the insight to human investigators. It takes away the resource-heavy data analysis process and allows humans to focus on the parts that need their full attention. Given machine learning algorithms can analyze huge volumes of data, it will likely cover sources that humans would never have discovered to add accuracy to decision making.
A Dow Jones and ACAMS survey revealed that as many as half of the alerts generated during KYC screening were false positives. By this, we mean that KYC processes are wrongly flagging companies or individuals. This means a significant amount of time is spent by banks, analyzing data that they do not even need to investigate.
AI-based systems better understand customer behavior over more data points and attributes, reducing the volume of reported false positives. The frameworks are more intelligent than a traditional (and often static) rules-based system e.g. condition X always means non-compliance and condition Y always means compliance.
The AI platform will adapt as it discovers false positives and not to raise them as suspicious transactions in the future.
The ability to detect patterns in the text means that AI can understand changes in a fast-paced regulatory environment. Natural language processing (NLP) is an application of AI capable of analyzing documents and text to extract information. As well as regulatory documents, it could do this to extract information such as client details where applicable.
AI can be used for everyday tasks like generating documents, reports, audit trails and notifications. For example, agents could be provided with insight about a specific company or individual in a matter of minutes, rather than having to wait for an analyst to give the information.
Through using AI, banks will have less reliance on human resources and reduce their costs when it comes to deploying people in AML and KYC operations. As well as this, those people can be redeployed elsewhere in roles that involve problem-solving or strategic work that AI is not yet capable of augmenting. Banks can use staff to help customers and not just carrying out manual tasks that machines can do both faster and better.
It is also worth saying that AI does not fatigue like a human might. Whilst we like to think it doesn’t happen, there is every chance that transactions can slip through the net during heavy manual processes due to tired eyes.
Case Study Datamatics
A case study from Datamatics on a leading bank explores how AI was used to solve the data analysis and processing challenges of KYC. The bank wanted a way to speed up the processing of KYC forms whilst maintaining accuracy during times of volume spikes where staff simply could not handle enough.
Datamatics provided a solution that redesigned the forms to speed up data capture and implement their TruBot tool . TruBot automatically processes the data and manages the entire KYC lifecycle including the receipt of documents via different channels. It then manages, archives, retrieves and generates reports.
The impact of implementing automation of KYC processes in the bank was a 50% reduction in man-hours, 60% increase in productivity, 40-50% increase in the cost efficiencies and 100% error-free.
AI if offering automation in AML and KYC processes that can drastically change the way financial institutions analyze their data. According to Deltec Bank, Bahamas, ‘If technology is able to recognize patterns and behaviors across more channels in a faster time than humans ever could, the potential benefits are huge. As a cybersecurity and due diligence continue to be hot subjects, KYC and AML will become increasingly important. Financial institutions should get ahead of the curve and find out what AI can do for them.
Disclaimer: The author of this text, Robin Trehan, has an Undergraduate degree in economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com . The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.