Anti-Money Laundering (AML) payment risk is making onboarding very complex, though artificial intelligence (AI) is rapidly maturing and can help.
Onboarding used to be a simple matter of filling in a form and transferring assets. But "Know Your Customer" and Anti-Money Laundering regulations designed to vet a potential clients' payment partners and history have mandated companies perform a detailed due diligence on their clients, and maintain a file of information regarding that due diligence.
Because of this, the initial form filled in by a client is far more extensive than in the past. In addition, clients must provide supporting documents that substantiates the information disclosed on the form. AI can assist with this process by extracting the supporting verification and in turn comparing it against the disclosed data.
AI is actually not a single technology but rather a distinct set that includes: Natural Language Processing (NLP), Data Mining and Text Analytics, Semantic Technologies (ontologies, RDF stores, linked data, sentiment analysis) and machine learning. Over the past decade this set of technologies has evolved to the point where they are now being embraced by the financial services companies and card issuers.
AI and linked data can be leveraged to perform a search on a client. The resulting data garnered from both free and paid sites can then be analyzed, categorized, and filtered to identify any issues of concern. Performing these tasks manually is time consuming for a single client. If the client is a company then similar checks must be performed on the company's officers and board members. Employing AI to perform the "heavy lifting”greatly reduces manual effort and frees up the onboarding specialist to focus on the results.
Ontologies are a standard way of defining the knowledge about a domain. The World Wide Web Consortium (W3C) defines the Web Ontology Language (OWL) as Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
In the KYC/AML space the ontologies can be used in the following areas:
Ontology based information extraction, which is used to extract relevant information from unstructured documents, such as monitoring a website to detect people involved in money laundering.
Ontology based new information discovery through inference, which is used to detect money laundering schemes or connections of money laundering collaborators.
Ontology can also inform rule verification and enable seamless integration of external data with internal data.
To summarize, ontologies are the structured data models on “steroids." The formal description of the ontology is much richer. By creating standard ontologies and sharing these across countries, business entities can help the implementation of the KYC/AML regulations.
An alternative to using ontologies is schema.org. This new open source project was started by Google, and is defined as “a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond.” This can be seen as an alternative of using ontologies, which are replaced by a simpler meta data.
The AI technology stack mentioned above can offer a great value if it is implemented wisely. The technologies can be used to:
Reduce manual labor and errors.
Insure the optimal compliance monitoring. By defining compliance ontologies for each country and using ontology based rule engines to verify transactions, a very efficient compliance verification engine can be created.
George Roth is CEO of Recognos Financial.
