AI in enhancing KYC and EDD processes has revolutionized the financial sector by automating customer verification, reducing false positives, and ensuring more accurate risk assessments in real-time.

Customer due diligence (CDD) processes, including know your customer (KYC) screening may take many different forms using AI based automated solutions available in the market. 

The financial institutions may also have a regulatory requirement to implement AI based identification and verification checks before and after onboarding customers. The requirement to automate KYC processes in mainly due to the digitalization that caused a significant increase in the graph of financial crime activities, especially in high-risk jurisdictions or countries.

The digital onboarding of customers includes non-face-to-face dealings with customers, therefore a robust AI based system is highly needed by the institutions to perform real-time customer identity and data checks by using document verification and/or biometric tools. 

Further, high-risk customers need to be identified and verified before onboarding, therefore AI technology may help institutions in predicting the risk profile of the customers based on the data or information provided to the institution. The available PEP data, high-risk profile customers data, data shared by regulators, negative lists, watchlists and etc are efficiently scanned through the use of AI and ML technology right at the time of onboarding customers. AI based KYC technology help in restricting the access of criminals, and fraudsters to the financial system or channels. 

In many institutions, EDD is a manual process using human intelligence, but with the advent of AI technology and other innovative technologies, more and more institutions are using AI tools to automate their EDD processes as much as they can.

When walk-in customers visit the branch for one-off deposit transaction then AI based KYC technology would immediately identify the persons as the real customers or criminals based on the real-time search and screen of credentials from the available negative lists and negative media news portal searches.

The matches would be immediately shown as results, and the criminals would not be allowed to deposit or withdraw or use any of the financial services offered by the institution. Due to AI based enhanced scrutiny the institution would be able to reduce the level of risk of processing transactions of criminals or fraudsters.

The purpose of to ensure that customer is identified as the one who he or she claims to be, and the purpose of transaction is matched with the risk profile. 

AI in Enhancing KYC and EDD Processes

Multilingual Natural Language Processing

Use of AI lead to automated EDD screening using natural language processing (NLP), or more specifically the multilingual natural language processing (MNLP).

NLP allows application or programs to understand the language written on a document as well as the contextual shades. NLP may also extract exact data and provide deep data insights. Use of NLP technology may help in processing vast amounts of data and save compliance cost.

MNLP automates EDD screening for unstructured data sources such as online media news and media portal results. The emphasis is to assume negative news may have been covered about a customer or a prospective customer in different news-papers or news portals.  

For example, a US based financial institution at the time of onboarding a high-net worth customer from Malaysia would need to perform some form of enhanced due diligence because of the large amount of funds involved. In this case, the account opening team and the compliance team of the US financial institution should not only assume that the news about such potential customer would be written by press in the US national newspaper.

Instead the account opening team would also need to perform specific searches for the customer in Malaysian newspapers using Malaysian language. In this case the use of AI based verification would make the process of identifying media news about the potential customer in different newspapers, easy and efficient.

Using True Multilingual Natural Language Processing 

There is a need to apply true MNLP and not to translate the document into English first to first run an English NLP algorithm. In this approach institutions may lose a lot of the natural language. 

As every language has its own syntax, rules, and language parameters, therefore with each language the institutions need to take a high degree of understanding and care, using MNLP technology which enable proper scanning and processing of the documents. Just translating words alone would not give a real outcome considering real context, and some words even would have dual meanings.

Final Thoughts

In the age of increasing digital transactions and heightened financial crime, the financial sector’s emphasis on robust Customer Due Diligence (CDD) processes is more crucial than ever. Automated AI-driven solutions, especially in Know Your Customer (KYC) screenings, are transforming the way institutions validate and onboard their clientele, catering to the pressing need for real-time identity verifications in non-face-to-face interactions. By employing tools like document validation and biometrics, these AI-based systems sift through vast troves of data, including PEP lists, regulatory databases, and negative news, to discern and mitigate risks right at the point of onboarding.

Additionally, with the proliferation of multilingual data sources, the adoption of Multilingual Natural Language Processing (MNLP) ensures a comprehensive and nuanced understanding of global data, circumventing pitfalls of mere translation. Institutions leveraging true MNLP can accurately parse various languages, understanding their unique syntax and nuances, making their enhanced due diligence processes more efficient and cost-effective, and safeguarding the financial ecosystem from potential threats.