Failing to follow KYB requirements and Anti-Money Laundering (AML) regulations can have dire consequences for financial institutions. Santander bank was fined $132M by a UK watchdog for not having properly checked 560,000 business customers. More than $5B in fines were issued for non-compliance in 2021 alone. It illustrates how financial institutions struggle to keep up with increasingly demanding regulations.
KYB is still a mostly manual, lengthy and resource-intensive process that is prone to errors. The average bank will spend north of $60M a year on KYC or KYB procedures, and this figure keeps creeping up. Banks could cut these costs by investing in technology. Automating KYB has become a top-priority project for financial institutions. However, automating such complex processes comes with a lot of challenges. We'll explore them in this blog post.
What is KYB?
KYB is a verification process performed by regulated organizations on companies they want to do business with. It's also called “KYC for companies”, and is used to comply with AML and other regulations.
The term KYB was introduced in 2016 after the Panama Papers case, which revealed a global network of companies used to launder money and hide assets. Soon after the scandal, the US Financial Crimes Enforcement Network (FinCEN) updated its Customer Due Diligence Requirements for Financial Institutions, strengthening their business-related rules. The EU followed suit with the 4th Anti-Money Laundering Directive.
Regulated businesses in the US and EU must perform KYB checks. This includes not only banks but also fintechs and financial services companies. It follows the 5th AML Directive in the EU and the latest updates of the US’s CDD Final Rule. Other countries are also extending their AML regulations. For instance, the Hong Kong Monetary Authority (HKMA) revised its guidelines in September 2020.
Even if KYB requirements vary by country, they often follow the same structure: identifying the legal entity, its representatives, and ultimate beneficial owners (UBO). The next step is to perform identity verification and sanctions & PEP screenings.
Challenges of Automating KYB
Fragmented Data Sources
One of the most common challenges when automating KYB processes is the lack of unified data sources. It makes getting the necessary data to identify the legal entity and its owner difficult. Finding the UBO of a company can be tough when dealing with complex ownership structures.
The most challenging situations occur when there are several layers of corporate ownership. For example, holding companies based in different countries that don't have the same level of transparency about business owners. Tracing the ownership chain may thus require the integration and orchestration of many data sources like private vendors or national business registers.
Regulators are not making things easier. The Beneficial Ownership Registers Interconnection System (BORIS) promised more transparency at the EU level as it went live by the end of 2022. But a few weeks later, the Court of Justice of the European Union (CJEU) invalidated a provision of the 5th AML Directive that guaranteed public access to this information. The Court stated that this provision was in direct conflict with the fundamental rights to respect for private life and to the protection of personal data. This marked a significant step back in ownership transparency. Performing such verifications will remain difficult for the foreseeable future.
Managing Customer Relationship During Onboarding
Collecting information and documents from customers is a key step in the onboarding process. It can also present challenges when it comes to automating KYB.
Institutions are pushing the adoption of modern solutions such as video-based identity checks, open banking, or qualified e-signature. However, less tech-savvy customers can be reluctant to use newer verification methods. Others might be concerned about privacy. It's thus important to support different verification methods. It serves as fallback when one proves ineffective or the company runs the risk of damaging conversion rates and activation.
Back-and-forths with customers about erroneous documents are also a frequent cause of headaches. Some businesses report as many as 70% of KYB processes containing at least one document that cannot be validated on the first attempt or requires more information. Such a process requires extra time and effort from compliance officers. It also makes the process of automating the chain of customer interactions even more complex.
On top of this, companies with an international footprint need to juggle with different sets of requirements. They get to know the preferred verification methods, the most common documents and deliver localized instructions that will guide the user through their onboarding journey.
Manual Document Processing
This is the biggest challenge that companies that are striving to automate KYB need to address.
Optical Character Recognition (OCR) solutions or AI-driven tech have proved effective at approving common documents such as driving licenses with good accuracy levels. Unfortunately, manual review remains unavoidable for some documents that aren't machine-readable. This is the case for Articles of Incorporation, cap tables, proof of funds, and many more that are part of KYB.
Human validation will inevitably lead to errors. It's especially challenging at scale when hundreds of people, sometimes contractors, are involved. They need to receive appropriate training on the intricacies of each document. Their productivity needs to be closely watched to control costs, their decisions audited, and “double-blind reviews” implemented on the most sensitive documents.
The companies that will be the most successful in automating KYB will be on the lookout for alternatives to manual reviews. These businesses will try to use it only as a fallback when all other methods have failed or on edge cases.
After talking with industry leaders in AI and document processing, and Fortune 500 corporations with massive document review needs, we remain convinced that verification will remain hybrid for some time. Therefore, we have worked on a range of techniques to augment the human reviewers. These will allow them to perform their job faster with even more accuracy.
Scenario Orchestration
In a common KYB example, you're using a private business data provider like Kompany to get beneficial ownership information, verify the identity of each beneficial owner, screen each of them for Sanctions, Warnings and PEP, discard any false positives, and match the information with collected documents. Depending on the results, the decision might be escalated to a supervisor or need extra checks. For instance, if the client’s company country of incorporation is flagged for poor financial transparency, it was created recently, or its activity seems risky.
To make things even more tricky, KYB should not be a fixed point in time but rather seen as a continuum. This is why clients need to be monitored continuously for suspicious events like a change of control or a newly reported court action. Each of these flags potentially impacts the score, and the status, or triggers more checks on the customer.
Chaining checks and building different decision scenarios to address various risk situations costs a lot of engineering resources. Very few companies can afford to build a custom decision engine, where rules can be designed and implemented without coding. Yet, it’s the only path toward a more automated and intelligent process.
Conclusion
Automating KYB has been challenging for many reasons. Various providers are required at different stages of the KYB chain, either to get exhaustive data or to run checks. This comes with technical challenges to integrate, maintain, and orchestrate these solutions. It also creates friction in contracting and negotiating with each vendor. Besides, risk profiles and geographical regulatory specificities add another dimension of complexity. On top of that, there still are situations requiring human intervention, like managing back-and-forth with customers and reviewing unstructured documents.
How can Dotfile help in automating KYB
Dotfile can help regulated businesses automate compliance processes to approve more than 90% of customers in less than 1 minute. This results in a more seamless user experience and a reduction in operational complexity and costs.
- Data collection and enrichment - Streamline data collection either via a web portal or an API. Import your existing customers’ data, and orchestrate multiple verification providers, company databases, and fraud prevention APIs. Approve individuals and businesses in over 200 jurisdictions.
- Checks and validation - Run and cascade identity verifications, documents and banking checks with OCR, Open Banking, and AML screening. Combine automated decisions with human fallback whenever necessary.
- Decision - Automate decisions based on collected data and results of checks with a flexible decision engine. Configure custom scenarios based on your customer profile and their risk levels.
- Monitoring - Keep your business customer base compliant with ongoing monitoring. Get notified of noteworthy events across your customer base.
Book a demo to discover how Dotfile can help you automate the KYB process.
