Minimizing Risk and Driving Growth via Best-In-Class KYC 

You may have read our recent news that Bank of Asia is leveraging an AI Digital Worker for negative news detection (aka ‘adverse media screening’) as part of the bank’s Know Your Customer (KYC) new client onboarding process. Taking this step, Bank of Asia reveals both a common risk-reducing step by businesses as well as a new trend that we see taking shape across industries regarding the purpose of KYC programs. 

Traditionally, KYC programs have had a risk-minimization focus, existing almost exclusively to ensure that a bank or other business does not face business risks, fines, and competitive disadvantages that arise from doing business with unsavory actors and outright criminals. The Bank of Asia news highlights how AI Digital Workers will improve capacity for managing anti-money laundering (AML), KYC, and sanctions risk at the bank. That’s a very predictable benefit of applying AI Digital Workers to compliance operations.  

But there’s also a major business-growth component to Bank of Asia’s adoption of AI Digital Workers. It reflects a trend that is emerging among growth-driven companies’ compliance operations teams that handle adverse media screening. According to the bank’s Vice President and Director Deon Vanterpool, “We [Bank of Asia] will be using KYC “AI Digital Workers” to help us achieve both our growth objectives and customer satisfaction goals.”  

Now, you may be asking yourself, “Did they really say growth objectives and customer satisfaction will be improved by better adverse media screening – a traditional cost center activity? How can that be?” Well, to understand this emerging trend in KYC fully, it is worth a quick review of how adverse media screening has progressed in recent years.  

The emerging impact of adverse media screening 

Adverse media screening is a critical component of FinCrime operations within banks and other financial institutions (FIs). Most FIs developed or emphasized adverse media screening in response to regulators around the world enforcing requirements for it as part of overall KYC enforcement. Today, the US’s FinCen, the UK’s Financial Conduct Authority (FCA), and the EU’s European Commission all impose mandatory KYC checks for new customer onboarding as well as ongoing reviews and monitoring to ensure proper customer due diligence (CDD).  

While the CDD rule spurred on many FIs to screen and monitor for negative news, those that had a higher-risk customer base had already been focusing on such efforts for years. More recently, businesses in other industries have also recognized the need for adverse media screening, because nefarious actors are leveraging cross-industry vendor networks to advance their crimes. Whether in banking, transport & logistics, travel, defense equipment manufacturing, etc., businesses must collect and analyze details about customers to be onboarded. This helps to avoid inadvertent support for criminals of money laundering, terrorism, sexual exploitation, fraud, and a host of other crimes. The US Financial Action Task Force (FATF) lists 21 predicate crimes.    

The challenges of traditional adverse media screening

Adverse media screening can reveal a customer’s or prospect’s association with crimes that other checks may not catch, leading a bank or other company to investigate more deeply or end its client relationship altogether. But implementing a comprehensive and robust adverse media screening capability has proven elusive to many organizations. Absent a highly automated, predictable and effective program, organizations pour lots of resources into ineffective manual adverse media screening, and still do little to reduce their risk.  

A traditional adverse media screening process is plagued by the following issues:  

• Large amounts of time and resources needed to document the reviewed news articles, followed by narrative generation that explains which news items indicate risk. 
• Volumes of search results that require excessive analyst review time. 
• Narrow scope of search results that fail to incorporate data sources beyond public news. 

• Relevant news about customers that appear in foreign languages, and thus, go unaccounted for by the search routine. 
• Mismatched or out-of-context search results (I.e., false customer name match, customer named is not the focus of the news, etc.). 
• Lack of speed and efficiency in the screening process preventing consistent periodic review of all customers.  

Without a means for great risk-based decision making at high volume and speed, organizations end up creating customer onboarding policies that are either too stringent or overly relaxed. The former hurts customer onboarding and revenue generation efforts, while the latter opens the organization to hefty fines, penalties, and brand reputation damage. 

AI-driven adverse media screening changes the equation 

Artificial intelligence (AI) has greatly improved automation of negative news screening while streamlining and optimizing search results analysis. Just as Machine learning (ML) and natural language processing (NLP) — subsets of AI – have been modernizing other areas of financial crimes compliance like transaction screening alerts review, they are now transforming negative news screening and analysis. Matching is automated and happens fast, monitoring makes it continuous, and adjudication of search results closes the loop (with escalation to people as necessary).  

How AI solutions are making leaps forward in adverse media screening: 

• Broader and deeper search across the Internet, including the deep web and other sources not indexed by search engines.  
• Sourcing of global and local news in different languages, originating from a single input language. 
• Contextual matching to ensure only negative mentions of a customer get escalated. 
• Grouping of risk events and indicators into specific categories (e.g., criminal, environmental, financial, etc.) for alert grouping. 
• Prioritizations of alerts to be analyzed based on risk scores to indicate a true match. 

• Auto-adjudication of alerts based on an organization’s own risk criteria settings. The ML capability understands the organization’s established criteria and adjudicates alerts automatically based on alert risk and confidence scores. The AI solution also creates a narrative for the decision. 
• A combination of real-time and scheduled batch alert screening / review enables an organization to choose its own frequency of screening and alerts review. 
• Auditable results via saved narrative reports that accompany each adjudicated alert. A narrative report provides the disposition decision, confidence level, input information, watch list information, and the reasons explaining the decision.  

The AI Digital Worker Evelyn by WorkFusion is the perfect example of how a company can deploy AI to gain immediate adverse media screening capability and KYC compliance operational scaling and proficiency. As an AI Digital Worker, Evelyn delivers an immediate and tangible impact by automating the time-consuming, error-prone tasks of adverse news alert review. Evelyn accurately reviews the news at superhuman speed, allowing compliance teams to identify true potential risk faster and with greater accuracy. Watch Evelyn in action. 

Thanks to AI-led automation like Evelyn, adverse media screening has become much easier across industries: 

Banking & Finance use cases: 

• Low, medium, and high-risk customer onboarding 
• Entire customer base ongoing monitoring 
• Vendor/supplier monitoring 
• Beneficial owner monitoring 
• Counter-party screening 

Other industries use cases: 

• Vendor/supplier monitoring 
• Claimant monitoring 
• High-risk public relations detection 
• High-risk user detection 

Time and again, organizations leverage Evelyn to screen and know their customers, ensuring regulatory compliance and reducing their compliance operations costs in the process. But now, in addition to these operational and risk-based benefits, growth-oriented businesses are setting their sights on automated KYC for fast customer onboarding, higher customer satisfaction, improved customer retention, and thus business growth. Bank of Asia’s Deon Vanterpool summed up their use of AI-based Evelyn nicely, “With a strong emphasis on providing the best customer experience possible, our use of advanced technology and big data enables us to open accounts efficiently and effectively for customers from anywhere in the world… to help us achieve both our growth objectives and customer satisfaction goals.” 

To learn more about AI’s dramatic impact on adverse media screening and KYC to help grow a business while minimizing risk, schedule a demo of Evelyn today.