Expert Insights

Confronted with increasingly complex security risks and a resultant increase in regulatory scrutiny, financial institutions juggle operational costs with numerous compliance challenges, at the forefront of which are Know Your Customer (KYC) requirements. KYC involves verifying client identity through an on-boarding process and performing periodic risk assessments to determine the likelihood of fraudulent activities that could implicate clients’ business relationships with the institution. While KYC processes vary by institution, human involvement proves itself a costly universality that often leaves room for error and consequently risks fines for noncompliance.  

Typical KYC processes include periodic data collection to verify customer identity, ongoing monitoring of customer activity, and enhanced due diligence to comply with sanctions and other international regulations. While complex, these tenants involve consistent workflows that follow the same series of steps. Prior to the advent of an alphabet soup of buzz terms such as “AI” or “RPA,” financial institutions had to devote significant labor and monetary resources to collect and screen data, culminating into a self-perpetuating cycle of tedious tasks that merely require human oversight rather than human sweat. 

Given the repetitive nature of these tasks, KYC has widespread applications for RPA, a technology oriented around increasing efficiency. Using RPA not only reduces the cost and labor burden on financial institutions but also ensures greater uniformity and accuracy of results by reducing potential for human error.  

Dubotz explored such applications by creating a bot using RPA to gather data and fill out KYC templates; the bot gathered and organized data, assigned case lists, and summarized results while still leaving a check point for cognitive input to confirm data accuracy and exercise in-depth analysis when necessary. Though a certain degree of human input is still necessary for high-level analysis to ensure reliability of conclusions, implementing RPA still ultimately maximizes both resource allocation and operational efficiency; after implementation, Dubotz’s bots yielded a 67% increase in efficiency. View the full KYC Automation Case Study here. 

While data collection and organization only represent a portion of overarching KYC requirements, RPA has broader applications in security and compliance for financial institutions across the board. A large part of the KYC process incorporates constantly and consistently identifying and verifying supporting KYC documents. In advanced use cases beyond simple RPA, using RPA along with Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning Technologies, information can be classified and extracted from such documents. RPA bots can then continue to handle the processing of information with or without humans in the loop depending the type and sensitivity of the process. Ultimately, though the burden of KYC requirements have proven universally inescapable, implementation of RPA and AI can greatly facilitate compliance responsibilities, allowing financial institutions to dedicate resources to where it matters.