The Road to Adoption and Challenges Ahead
A growing demand for Know Your Customer (KYC) compliance professionals highlights the increasing importance of adhering to regulatory requirements in the digital assets industry. The lack of trained compliance professionals is evident in that it has become one of the most sought-after professions. It seems that the logical solution to quickly bridge the skills gap, especially for the crypto sector, is to turn to technology to speed up KYC processing. However, compliance people are a very conservative bunch.
The benefits of using technology in a data analytics task where artificial intelligence (AI) algorithms can analyze vast amounts of transaction data to identify patterns and anomalies that may indicate suspicious activities is accepted. AI can uncover hidden connections, trends, and behaviors that are difficult for humans to detect. However, compliance professionals cannot accept that machines could be better than them at their job.
Here are some applications of AI which continue to face skepticism:
- streamlining the CDD and KYC processes by automating data collection where Natural Language Processing (NLP) and Machine Learning (ML) can analyze unstructured data from various public sources like social media and news articles to identify potential risks associated with customers
- performing real-time sanctions screening and cross-referencing customer information against international sanctions lists and Politically Exposed Persons (PEPs) databases
- automating SARs generation by identifying suspicious transactions, analyzing relevant data and providing a comprehensive report
- document translation and summarization
- helping financial institutions stay up-to-date with evolving regulations by continuously monitoring regulatory updates, analyzing the impact of new rules, and adjusting compliance processes accordingly
A lack of awareness and understanding of AI among compliance professionals leads to resistance and skepticism towards its adoption to more use cases, so it will take time before true adoption of AI in this industry begins. However, AI also faces resistance due to the importance and complexity of decision-making as well as the consequences of incorrect decisions, especially if they lead to unintentional participation in money laundering processes resulting in criminal prosecution for a company’s board members. Consequently, trust in people is higher than the current trust in technology.
However, within the next 3-5 years, algorithms might actually perform better than humans in most tasks. This thought is daunting because it implies everything will change, putting not only jobs but entire companies at risk.
Interestingly, Apple’s foray into the banking sector with their Savings Account attracted $1 billion in deposits in the first few days and 240,000 accounts, marking the start of tech companies expanding into financial services. Apple streamlined the onboarding process, allowing clients to open an account in less than a minute. It remains to be seen if this process is fully compliant but given Apple’s brand reputation, we can assume there is too much at stake for them to not be compliant.
AI is not a silver bullet for compliance issues because it largely depends on the availability of data which can be analyzed. Although AI can help reduce errors and increase efficiency, it is not a magical solution that can solve all problems in the compliance space. As the industry moves forward, companies need to invest in education and training programs for compliance professionals to help them better understand the capabilities and limitations of AI. This will enable them to make informed decisions about when and how to leverage AI in their daily operations.
While AI has the potential to revolutionize the compliance space, its adoption will be gradual and requires addressing the concerns and skepticism of compliance professionals. Striking the right balance between AI-driven solutions and human expertise is key to ensuring the effective and responsible use of technology in compliance and this involves being proactive in embracing the potential of AI while respecting the importance of human judgment and expertise.