Automation for Cost-effective AML Compliance
Over the last few years, all industries have as witnessed a series of seismic changes, all driven by technology. And the banking industry is not immune to these changes. However, what hasn’t changed for banks is the strict regulatory framework within which they need to navigate and manage these dynamic market changes; all the while being compliant, and cost focused.
No longer a new concept in the banking industry (which has always been an early technology adopter), Robotics Process Automation (RPA) is being leveraged by banks to meet the regulatory requirements set out by the Bank Secrecy Act and Anti-Money Laundering (BSA/AML) in a cost and time efficient manner.
So, what is RPA? Essentially, it is a software application that replicates human actions and at the same time interacts with various applications on the computer. It does this round the clock, through the year with or without the support of human interaction. It is most often deployed for repetitive tasks.
RPA for AML
Being highly regulated, the banking and financial services industry can ill afford errors. With a growing customer base, stringent global and local regulations, and digital operations posing a high-risk of security and fraud; an error can mean penalties in cases of non-compliance. Which is why more and more banks are becoming increasingly stringent with their ‘Know Your Customer’ or KYC norms. It is in this space that RPA offers a huge opportunity to enhance regulatory compliance, including AML and customer due diligence.
Some of the areas where banks can use RPA are:
- Customer onboarding: This is a time-intensive process and RPA can reduce the time taken significantly—from months to weeks, or even days. For example, if a bank is onboarding a corporate customer then leveraging RPA the bank can log in to various public registers of companies and retrieving the registration information (date, number) and getting the list of company directors; thus, hastening and completing the identification and verification of the customer.
- Customer due diligence: The due diligence processes can be improved substantially, with time reducing for an average check by as much as 50%.[i] Banks can use RPA for data entry of customer information, while the bots can validate the existing information by accessing internal databases, social media, and websites.
- Customer outreach: As part of AML compliance, banks need to update customer information at regular intervals (dependent on customer risk levels), apart from collecting this information during initial onboarding. Doing this manually would be cost inefficient as well as increase the probability of human errors. By deploying RPA, banks can structure the process and at regular intervals, the additional data can be collected from external sources.
Innovation in Automation…
While at present most banks are deploying RPA for the basic repetitive processes, once the shift has happened there is a significant scope to leverage innovations as well.
For example, Artificial Intelligence (AI) and machine-learning have a huge potential to impact the KYC compliance process by assisting in identifying high-risk customers who need to be screened with an Enhanced Due Diligence (EDD) process. This can be done based on pattern recognition techniques coupled with unstructured text analysis.
HSBC has been automating some of its compliance processes to become more efficient. The bank is leveraging AI technology to automate its anti-money laundering investigations, which were traditionally conducted by individuals. In a 12-week period, HSBC was able to achieve a 20% reduction in false positives while maintaining the same number of reports of suspicious activity for human review. Taking this initiative forward has the potential to save HSBC millions of dollars per year.[ii]
Another area of innovation is using chatbots for customer communication during the onboarding process and using the Natural Language Processing (NLP) to analyze their responses to identify any high-risk behavioral the while saving cost as well as reducing human error probability.
Similarly, using Machine Learning banks can automate SAR filings and report generation; and leverage visualization technologies to make sense of large volume of unstructured data.
Facing several challenges, the banking sector can leverage automation and specifically RPA to manage and mitigate a number of them. Considering the significant potential of RPA, especially in managing the regulatory compliance needs, banks across markets are deploying this technology on a wide-scale.
By using this in an optimum and intelligent manner, compliance teams in banks can not only save time and costs, but also reduce the risk of human errors significantly; in this stringently regulated space. For tomorrow’s banking leaders, it is imperative to build organizational maturity and ability to adopt these technologies.