Over recent years the financial services industry has been under considerable pressure due to a number of regulatory factors including:
- changing regulations in multiple jurisdictions
- increased regulatory scrutiny
- cost pressure due to implementation of new regulations with impact to BAU (Business As Usual) capacity
- legacy systems (which are a particular issue with larger Financial Institutions (FIs))
- ensuring staff are sufficiently trained
- longer client onboarding time frames.
These factors are severely impacting Know Your Customer (KYC) operations of FIs.
The objective of KYC is to truly know the identity of a customer and their associated risk profile in order to place them into relevant customer buckets to perform due diligence. It is also about keeping up with growing regulations and to avoid large financial penalties, as some institutions have learnt the hard way. AI techniques can help to build more accurate risk scores for customers to better understand their risks and then to mitigate appropriately.
Globally, regulators have been unsatisfied with processes that are in place at several FIs, which has led to many of the institutions incurring costly fines running into tens of millions, legal fees and reputational damage. These shortfalls have led to regulators across the globe to increase their focus on robust and effective controls regarding Customer Due Diligence (CDD).
Traditionally, the KYC data analysis and Anti Money Laundering (AML) functions are very labour-intensive and are, therefore, inherently costly and prone to errors. The staff are often under pressure to cope with increased workloads, and productivity and quality often suffers as a result.
Many FIs are looking to centralise client onboarding and KYC operations across multiple business units and jurisdictions. Subsequently, they are actively looking for ways technology can help to mitigate the current challenges outlined above by using Artificial Intelligence (AI) and Robotic Process Automation (RPA). These technologies offer opportunities to further digitalise various client lifecycle management processes within KYC and AML, thereby improving operational efficiencies and resource utilisation to reduce costs.
This article explores ways in which AI could be implemented to help to solve or mitigate some of these problems.
AI Technologies Supporting KYC/AML
There are several solutions available, which seek to address the problems associated with predominantly manual CDD tasks and activities. These solutions range from basic screen scraping-based, repetitive task automation to enhanced AI-based solutions, such as identifying trends in historical data and providing forward looking intelligence to strengthen human decisions.
The value of AI lies in relieving staff of repetitive, manual tasks to ensure they are focusing on more meaningful activities, such as analysis and investigations. Utilising AI also brings substantial cost benefit, overturning the costly need of hiring large volumes of staff, employed for time-consuming tasks, such as data gathering and inputting.
AI is an enabler for competitiveness for the future and is capable of several analytic processes and tasks.
Unstructured Data Analysis
AI is capable of learning to “read” using Natural Language Processing (NLP) and “understand” segments of a document based on those insights. Using these rules, AI can assess written content and respond accordingly. This gives AI the ability to “read” vast amounts of information in any language, through intelligent document scanning and sifting through the array of external data sources banks should be consulting. As well as massively reducing risk, it can improve sales effectiveness and enhance the overall client experience.
It is also becoming widespread in helping to analyse unstructured content such as adverse media. Together with machine learning, NLP-based AI can “read” such articles and perform a range of tasks including extracting metadata, identifying entities that are referred to and “understand” the intent or purpose of specific parts of the document.
A subset of NLP, Natural Language Understanding (NLU) can select specific rules in lengthy regulatory documents and send them to people and departments responsible for ensuring compliance. NLP systems can analyse documents to identify people, products and processes affected by legal and regulatory changes.
Robotic Process Automation (RPA)
RPA enables financial institutions to automate repetitive, non-value adding and data intensive tasks. Implementing RPA can streamline KYC decision-making through more effective client data capture and regulatory client classification and evidencing. With the addition of bots, RPA automates the client data entry process and conducts initial verification, passing relevant client records to Client Services, if data gaps prevail. In addition, RPA solutions can automatically consume information (data and documents) from multiple vendors and utilities. This improves overall data quality and speeds up the time it takes for account set-up and client/product onboarding.
For AML and risk rating, RPA can also make decisions related to AML alert types, Politically Exposed Persons (PEPs) and sanctions, leaving higher risk alerts and clients for AML and KYC professionals to check. It will also attempt to categorise and index information, leaving users to complete only a final review.
AI-based link analysis is a set of techniques for exploring associations among large numbers of objects of different types. These methods are crucial in assisting human investigators in comprehending complex webs of evidence and drawing conclusions, which may not be apparent from any single piece of information. These methods are equally useful for creating variables, which can be combined with structured data sources to improve automated decision-making processes. Typically, linkage data is modelled as a graph, with nodes representing entities of interest and links representing relationships or transactions along with dubious jurisdictions, companies and Ultimate Beneficial Owners (UBOs).
One of AI’s biggest advantages revolves around delivering workflow automation. AI can be used to generate documents, reports, audit trails and notifications. For instance, AI-based workflow automation reports generate risk profiles on both companies and individuals in just minutes, providing comprehensive and in-depth global due diligence information. Also, the reports provide links to the data sources, enabling them to be fully auditable. These are vital details for internal audit teams and regulatory examiners who typically want to know the accuracy, veracity and origin of any information used in AML decision-making.
In most cases, money launderers hide their actions through a series of steps that disguise transactions to look like the money, which came from illegal or unethical sources, is earned legitimately. Most of the major banks across the globe are shifting from rule-based software systems to artificial intelligence-based systems which are more robust and intelligent to the anti-money laundering patterns.
Realised Benefits in KYC/AML from AI
The real power of AI technology doesn’t solely lie in its ability to intelligently extract risk-relevant facts from a huge volume of data, but also to then synthesise and deduplicate that information so that it is both meaningful and concise. This allows unstructured data from different sources and formats to be classified automatically for the KYC profile. Eliminating false positives and irrelevant results makes analysing true risk a much easier task.
Additionally, AI is assisting FIs to automate significant portions of the investigative process, allowing AML investigators to focus their attention on otherwise unintentionally overlooked red flags or suspicious account activity. The result is a finely tuned cross-enterprise compliance program that operates more cost efficiently, and more importantly, functions more effectively than traditional systems.
Machine learning can further identify candidates for automation by observing trends in user behaviour. Once a client portrait emerges, it becomes possible to predict or model future customer actions. Also based on pattern recognition techniques coupled with unstructured text analysis, it is made more efficient to identify relevant customers for Enhanced Due Diligence (EDD).
Shifting from rule-based software systems to AI-based systems, which are more robust and intelligent, can help to uncover a broader array of potentially illicit AML transaction monitoring patterns, which would not have previously been possible.
It can also be used to identify gaps in the collection of customer information and generate alerts for KYC process completion. Cognitive engines now available can understand and analyse high volumes of regulatory changes and verify, so that a business is alerted to the most up-to-date policies.
The first wave of AI technology deployment is already happening in global banks and rule-based AIs (typically based on ‘if-then’ rules) are enhancing productivity in internal processes. With the advent of AI applications for KYC and AML purposes, the adoption of AI technology by financial institutions in these labour-intensive and high-risk areas seems certain to rapidly accelerate in a bid to keep a competitive edge.
Many complex activities cannot be simply automated, as the process is subjective and uses a variety of inputs. However, as overall AML and KYC processes continue to digitise, the ability to use effective AI solutions will increase. AI-based solutions are capable of simulating human intelligence, including complex decision-making through the evolution of Machine Intelligence. This intelligence can be used in cases that are today done manually in the CDD process, such as exception handling and management.
Regardless of the emergence of new AI technologies, KYC/AML professionals should not worry about losing their jobs anytime soon. We are living in a time of unprecedented change and human oversight in the use of AI is required, which is likely to generate new roles and specialisms within the industry. Furthermore, with regulators only too eager to penalise banks for any failures in KYC/AML compliance, institutions are staffing up. Al doesn’t replace human intelligence, but enhances it or makes it more efficient by cutting down manual tasks and freeing valuable time for more meaningful activities. Banks will continue to use analysts to make ultimate decisions on whether a transaction is suspicious and must be reported, but by utilising AI, they can be more productive and have more confidence that their decisions are accurate.
Ultimately AI takes KYC and AML compliance to the next level. AI isn’t just a technology, it is a collection of related technologies which has the potential to automate workflows and quickly analyse large volumes and different types of data to help analyse customer data, customer feedback and nuances with improved accuracy and timely completion not achievable through humans. AI is enabling FIs to explore and implement improved ways on how to control KYC costs and complexity, offering several opportunities to improve and grow.
About the Author
Merlyn Stracey is a Senior Consultant at MHC, with over 13 years’ Project, Operations and Implementation Management experience from leading Financial Institutions as well as the Public Sector. She possesses deep domain knowledge of global KYC roll-out for leading Tier 1 FIs, where she has gained hands on experience in KYC implementation involving large scale changes and training.
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