Financial institutions are entitled with applying due diligence procedures on their customers to make sure that they are not involved in any activities related to financial crimes. A legitimate and strong Customer Due Diligence (CDD) program is essential to protect the reputation of a financial institution (FI) and also the whole integrity of the financial systems in addition to the necessary regulatory requirements. Together with the digitalization and the enhancements in the technology, the financial services landscape is faced with challenges related to verification of identities and ensuring their compliance with Anti-Money Laundering (AML) and know Your Customer (KYC) requirements. Effective CDD plays a vital role in achieving solid ID verification processes and AML/KYC requirements, therefore financial institutions (FIs) should focus on the ways to improve their CDD procedures by making investments on enhanced ID verification methods, screening, financial activity monitoring and regular risk assessment within the frame of AI and machine learning technologies.

1. ID Verification

The traditional online ID verification methods such as Knowledge-Based Authentication (KBA) or two-factor authentication which were once effective in providing the necessary security measures now remain insufficient in authenticating or verifying whether the user logging in is the actual owner of the account. Unfortunately, the cyber criminals find ways to breach the systems and commit identity fraud crimes with the emerging technological facilities. Therefore, effective and accurate digital ID verification methods have become even more important during the digital times as the financial ecosystem has become more vulnerable to identity fraud. However, the solution lies in the innovative technology: cutting-edge AI-based digital ID verification methods offer simpler, more secure and cost-effective process handling both for the institutions and the consumers in comparison to conventional ones. Behavioral analysis, facial and voice liveness detection, age and gender analysis are among the most effective digital ID verification methods powered by AI and machine learning technologies.

Automated artificial intelligence-based ID verification systems offer a great increase in the efficiency of the banks and financial institutions (FIs) especially for the digital onboarding processes as they help reduce the need for manual intervention to minimum which decreases the operational workload and time spent on them tremendously. Utilizing digital ID verification methods during customer onboarding also meets the expectations of digital customers concerning pace, safety, flexibility and smoothness of the experience.

The importance of digital ID verification lies in the fact that one of the key elements of CDD is identifying and verifying the identity of the customers. As the tendency towards digital financial experiences grows every year, financial institutions need to understand and find ways to verify their customers’ identities on the digital environment in order to satisfy customer expectations and ensure regulatory compliance with AML and KYC. In addition to the security, efficiency and customer satisfaction benefits, conducting an accurate and effective digital ID verification enables powerful customer due diligence (CDD) during the business relationships and transition monitoring activities.

2. Name Screening

Name screening is the second step of an effective customer due diligence (CDD). The aim of name screening is to check if the potential customer is associated with any risky activities or included in blacklists. There are specific methods applied during name screening processes:

• Sanction lists and blacklists: Governments and financial authorities issue sanction lists and blacklists so that the individuals or organizations that might be related to illegal financial crimes can be screened and the necessary preventions can be applied against financial crimes such as money laundering and terrorist financing.
• Adverse media screening: Conducting a search about a customer against popular or reputable information resources in order to understand whether the customer has any criminal record or is associated with any of them, namely has adverse reputation.
• Wolfsberg’s guidance on PEPs: According to the Wolfsberg Principles, Politically Exposed Persons (PEPs) hold a risky position for getting involved in financial crimes because they have the potential to create an influence on the decision-making processes of business relationships and also, they can achieve access to governmental accounts and funds. Therefore, PEP screening is also essential for an effective CDD.

3. Activity Monitoring

Customer Due Diligence (CDD) is not only about getting to know the customer, but also checking and monitoring the customer activity regularly for AML (Anti-Money Laundering) and CTF (Counter Terrorist Financing) purposes. For an effective and ongoing monitoring, it is essential to take customer’s account activity and KYC (Know Your Customer) compliance updates into consideration. Collecting, analyzing and utilizing that much of data can be challenging for the financial institutions in the monitoring processes; however, the solution lies in the AI again as it allows for a clustering of data more simply without creating defined scenarios. Additionally, reducing the false positives can benefit the whole activity monitoring process with less cost and operational work while at the same time with better outcomes in terms of efficiency. It is possible to reduce the false positives by installing AI-powered solutions again such as setting filters for the fake alerts or creating automated responses for specific issues.

4. Periodic Risk Assessment with the Help of AI and Machine Learning

AI and machine learning put a new face on the risk assessment processes of the financial institutions. They can automate the whole process and by extension they can minimize the human errors, improve the pace of regular tasks, organize the unstructured data and decide whether a customer is posing any risks. AI and ML technologies are able to scan all the financial activities of a customer including the past ones and with the data clustering and classification abilities, they can create behavior-based customer profiles and apply risk scoring as a result of ongoing monitoring. Financial institutions can generate or prioritize alerts depending on the type and level of risks defined with AI-based technologies. Finally, ongoing risk assessment conducted with AI and ML enables FIs to apply a peer group analysis for a specific suspicious activity and use the associated data as a basis for potential behavioral activities of the customers that might be related to risky situations.

Burçin Güney, Account Manager