By- Indranil Roy
Starting with the initial guidelines, when the US passed the Banking Secrecy Act (BSA) 1970, KYC regulations has been key to prevent money laundering by ensuring that financial institutions accurately identify and verify the identities of their customers. Notably, the initial push for KYC came from the global money laundering and terrorist financing watchdog, the Financial Action Task Force (FATF) and national governments, which aimed to create a standardised approach to customer verification.
Historically, KYC processes were largely manual, involving extensive paperwork, face-to-face meetings, and prolonged verification timelines. As financial crimes grew more sophisticated, so did the complexity and scope of KYC requirements. Over the years, technological advancements began to play a crucial role in streamlining these processes.
For instance, In India, the Reserve Bank of India (RBI) has recognised the government-backed DigiLocker platform as a source of e-documents for KYC purposes. DigiLocker is a secure digital document wallet service boasting over 281 million users as of May 2024. This allows financial institutions to leverage pre- verified documents stored by users on the platform, streamlining the process and enhancing customer convenience for a significant portion of the population.
This digital transformation is further accelerated by advancements in artificial intelligence (AI) and technology, playing a crucial role in revolutionising how financial institutions manage KYC, and resulting in significant cost savings, revenue enhancements, faster cycle times, and operational efficiencies. This transformation is not just a trend but a necessity especially as the bad actors are also using latest technologies in their schemes and scams.
The Need for KYC Transformation
KYC transformation will continue to accelerate, even in the absence of new regulatory mandates. Technological advancements, particularly in AI, are enabling this acceleration. Several factors make this transformation imperative:
1. Standardisation of Processes and Platforms:
KYC processes and supporting technology platforms are highly diverse, often further complicated by mergers and acquisitions (M&A) and the launch of new lines of business and products. This presents a significant opportunity for financial institutions to standardise KYC platforms and processes. Standardisation will lead to economies of scale, long-term reduction in operating costs (both technology and people), and quicker customer onboarding, reducing revenue leakage and enhancing time-to-revenue considerations.
2. Reduction of False Positives:
False positives constitute a large portion of alerts raised by name screening, adverse media, and Enhanced Due Diligence (EDD) processes. AI advancements make it easier for machine learning and rule-based systems to identify and adjudicate false positives. These systems can automate adjudication with human in the loop for final decisions, leading to increased compliance rigour, fewer penalties, and scalability in alert quantity and disposition workloads. Consequently, operating costs decrease as AI-based digital workers take on these tasks.
3. Optimisation of Sub-processes:
AI’s application extends beyond false positive detection to optimising sub-processes and other time-consuming activities. For instance, AI can significantly streamline the Enhanced Due Diligence process, such as the source of wealth analysis and validation, which traditionally required extensive manual effort. AI-based digital workers can now perform these tasks efficiently.
4. Anomaly Detection in Transaction Monitoring:
Anomaly detection is another critical area benefiting from automation. This process involves identifying transaction patterns that deviate from declared norms, such as currency usage, transaction purpose, and frequency limits. Automating anomaly detection enhances accuracy and efficiency in identifying suspicious activities.
Extending Beyond KYC: The AML and Fraud Management Spectrum
The opportunities for optimisation extend beyond KYC to the broader anti-money laundering (AML) framework, including transaction monitoring alert investigation. This encompasses tracking the flow of funds, monitoring high-risk jurisdictions, politically exposed persons (PEPs), and various structuring, layering, and integration scenarios. AI-driven automation in these areas enhances the ability to manage fraud alerts and investigations, contributing to a more robust and responsive financial crime compliance framework.
The Promise of Perpetual KYC
A particularly promising area for AI application is perpetual KYC (pKYC). By continuously monitoring a small set of high-impact attributes for changes, financial institutions can conduct immediate, automated mini-KYC checks. This approach is not only cost-effective but also ensures that risk and AML profiles are updated in a more dynamic and behavior-driven manner rather than remaining static over extended periods.
The transformation of KYC processes through AI and technology is not just a forward-looking initiative but a current imperative for financial institutions. By standardising processes, reducing false positives, optimising sub-processes, and enhancing anomaly detection, financial institutions can achieve significant cost savings, improved compliance, and operational efficiencies. Furthermore, extending these technological advancements to broader AML and fraud management processes fortifies the BFSI sector’s ability to combat financial crimes effectively.
As technology continues to evolve, the banking and financial sector must remain agile, leveraging AI and other technological advancements to stay ahead in the KYC transformation journey. The future of KYC lies in its ability to adapt and innovate, ensuring robust compliance and enhanced customer experiences.
However, the implementation of AI in KYC is not without challenges. One significant hurdle is integrating AI systems with existing legacy infrastructure, which can be complex and costly. Additionally, the reliance on AI raises concerns about data privacy and the potential for algorithmic biases, which could lead to unintentional discrimination or false negatives. Ensuring the ethical use of AI and maintaining compliance with evolving regulatory standards also demands continuous oversight and adaptation. Financial institutions must navigate these challenges carefully to fully realise the benefits of AI while mitigating associated risks.
(Indranil Roy is the Managing Partner, Global Head, Industry Solutions Group, Mphasis)
(Disclaimer: Views expressed are personal and do not reflect the official position or policy of Financial Express Online. Reproducing this content without permission is prohibited.)