Despite decades of significant investments, achieving and above all maintaining real-time anti-money laundering (AML) compliance continues to challenge financial institutions (FIs). Cost-income ratios are under pressure as compliance costs grow. For example, the AML market size is projected to reach USD 6 billion by 2028 globally at a CAGR of 15.7%. Cumbersome AML processes that are a part of onboarding and the FI’s ongoing due diligence are leading to poor customer experience, diminishing the competitive advantage. Regulatory compliance is challenged by peak volumes, the complexity of data, unreliable data due to manual or semi-automated processes, and a lack of skilled candidates in the market.
While there is no silver bullet, combining new technologies with a holistic approach toward implementation and the right service providers can help FIs secure their operations, AML included, like never before. Some key success factors include: -
- Onboarding with a purpose- Orchestrating the onboarding flow with end-to-end delivery in mind. Capturing, using and re-using data efficiently, creating an enhanced digital customer (KYC) profile.
- Data at heart- Having a well-defined data strategy with a digital customer KYC profile at the heart of it that enables not only onboarding but also continuous delivery, monitoring, and due diligence.
- Talent attraction- With increasing complexity and demand for a risk-based approach and automation taking over simpler tasks, the attraction of talent is as critical as ever.
- Tech and vendor collaboration- The market is flooded with innovative solutions. This necessitates a collaborative approach to effectively utilize the latest technologies.
Examples of technologies for AML compliance
Data collected by legacy systems are prone to being inaccurate and/or inadequate when it comes to supporting risk assessments. In this regard, data analytics have the potential to add value and reduce the costs of real-time AML measures. These systems leverage innovative technology solutions to create sustainable, automated processes that smartly gather data insights that can enhance AML operations.
Key technologies that can enhance AML compliance initiatives include: -
- Advanced entity resolution
Entity resolution revolves around merging every instance of entity or customer evidence on systems. In short, it is a form of record linkage. This input is used by the AI/ML engine to generate a holistic overview. It streamlines customer due diligence (CDD) processes and assists in financial investigations. The technology uncovers hidden connections, reduces the time taken to collect and commute critical data, and generates alerts for investigators. Entity resolution is one of the most critical functions of data analytics. When advanced entity resolution is added to the AML workflow, it can identify high-risk players. Therefore, this process converts real-time customer data into a single source of truth.
- Machine learning
A subset of AI, machine learning (ML) is a computer system that uses algorithms to draw data inferences. ML techniques such as natural language processing (NLP) can identify human languages by allowing computers to understand the text and spoken words. Machine learning detects patterns and monitors suspicious financial activities. Traditional monitoring tools can generate fake alerts if a transaction amount associated with an account is larger than previously recorded figures. This is a major area of concern, as studies have shown that up to 90% of such alerts are false positives. ML reduces the rate of false positives through semantic and statistical analysis. The FATF in its recent publication has highlighted that leveraging machine learning in AML can unlock unprecedented benefits.
- Network analytics
A network-centric approach helps to identify suspicious groups. Unusual money flows can be tracked through ML and AI techniques. Statistics from one network are treated as input in transaction monitoring systems, with computational functions being carried out by AI through data-mining techniques to deliver swift findings. Link prediction algorithms conduct party-related checks to determine if an account is connected to any other fraudulent account. This in turn assists financial forensics to arrive at dynamic, data-driven decisions.
- Facial recognition
Facial recognition is a component of biometric software. KYC process in AML has a key role to play. Recognition systems, which are an integral part of the KYC onboarding routine, assist in identifying financial bad actors. It captures images through a surveillance camera or video and compares them with a stored database. ML identifies patterns in the data such as skin color and facial dimensions to identify the customer in the given image.
- Robotic process automation
Software robots or digital workers are outputs of business process automation technology. Robotic process automation (RPA) is easy-to-deploy and reduces the burden of repetitive manual tasks. These processes are used to set up databases and verify existing customer information. RPA can capture information from KYC forms, use optical character recognition techniques and populate the database with verified details. This saves time, streamlines the onboarding process, segregates the foul players, and furthers AML software operational transformation. RPA also carries out risk assessments and monitors account closure procedures of non-compliant, high-risk customers.
Securing your enterprise holdings — An HCLTech initiative
There is no one simple way of neutralizing all AML challenges. Therefore, financial institutions must deploy an all-encompassing strategy that combines technology solutions, process optimization, and resource management to build a complete defense framework for their organizations.
In this regard, HCLTech has been a partner of choice for multiple Fortune 500 organizations in their quest for fool-proof BFSI security, offering AML compliance solutions. With an assortment of highly skilled professionals and a formidable infrastructure footprint, HCLTech techno-functional services and solutions are a valuable asset for modernizing your automation capabilities and, ultimately, safeguarding your organization.
