Consider this fact – from handling just 10 regulatory changes a day in 2004, banks were handling 185 such changes in 2017. Today, there are an average of 220 regulatory alerts per day. And thanks to a dynamically changing compliance and regulatory landscape, this number seems to be rapidly increasing – challenging the seamless operations of banks like never before.
Regulatory Technology or RegTech, which was a relatively unknown concept a decade ago, has now become the next FinTech. By 2020, it is estimated that it will comprise 34 percent of all regulatory spending – this translates into an amount of USD 76 Billion. Ironically though, cost of compliance has continued to rise even as RegTech has gained importance. Clearly, the next steps require strategic planning and effective deployment of technology. Artificial Intelligence (AI), automation and advanced analytics techniques hold high promise for innovations in compliance.
The Smart Algorithm-based Model to Compliance
Traditional Anti-money Laundering (AML) systems deploy a rules-based approach to detecting suspicious activity – and this is triggered on reaching a certain threshold level. AI and Machine Learning (ML) systems, on the other hand, look for subtle patterns in the system to reveal criminal activity and intent. Their highly refined self-learning models facilitate advanced and adaptive real-time transaction monitoring – as well as unearth different forms of hidden money laundering clues within the transactional data. This enables efficient data aggregation, accurate risk scoring and alert generation, and automation of processes.
Intelligent data platforms use a ‘fuzzy logic’ approach that looks at degrees of truth rather than the binary true / false logic. This helps banks create a wider canvas-view of suspected fraud even from unstructured data. It also enables speedy identification of connections in transactions early on in the process to prevent fraudulent activities. And when deep learning techniques are deployed, thought processes of fraudsters can be effectively mimicked – to prevent financial crimes.
Additionally, when advanced analytics is applied in a non-linear manner, customer risk scoring becomes significantly more accurate. The number of false positives is slashed and obscure combinations of variables are efficiently identified to predict and prevent fraud. Further, learning algorithms efficiently detect innocuous-looking patterns that may escape the attention of even data scientists. This allows investigators to exercise their critical thinking skills on high-risk cases. And when this is applied across a network of banks and financial institutions, an efficient system of real-time fraud prevention can be co-created.
Add automation to the mix of AI and analytics – and the circle of thorough efficiency and accuracy is completed.
Reaching the Smart State of Compliance
The key to achieving a telling impact in intelligent compliance lies in being able to deploy technology and analytics in the right manner. This calls for:
Designing a seamless and end-to-end Know Your Customer (KYC) and AML process
Creating an efficient strategy for qualitative data aggregation
Integrating micro-segmentation with systems and processes – for more accurate validation
Establishing the right metrics to determine effectiveness and assess impact
The use of AI and ML for regulatory compliance is fast catching up. The Financial Crimes Enforcement Network (FinCEN) has an AI system to monitor the Currency Transaction Reports (CTRs) for identifying potential money laundering. Many large financial institutions have already implemented AI- / ML-based solutions in their business functions.
Today’s RegTech implementations are setting the foundation for automated and adaptable compliance. Banks and financial institutions see significant merit in leveraging AI and ML for data-driven intelligence to tackle the rampant menace of financial crimes. This transformation will undoubtedly enable financial services companies to effectively predict, prevent, and act on threats.