Events
IAGA SUMMIT San Francisco 2019
Vasilios P. Chrisos, Principal, PwC Financial Crimes Unit
Vasilios is a Principal in the Financial Crimes Unit of PricewaterhouseCoopers LLP (“PwC”). He brings to his engagements nearly 25 years of experience assisting both financial institutions and non-financial companies on all aspects of financial crime compliance with a particular focus on anti-money laundering (AML) and sanctions matters. In his work Vasilios advises casino operators and other gaming companies on the design of comprehensive, risk-based AML and sanctions compliance programs. He and his teams assist with the development of AML/sanctions risk assessments, assessment of policies, procedures, and controls, execution of enhanced due diligence measures for higher-risk patrons (including local language media research, open and human source inquiries), development of transaction monitoring capabilities, and investigations of potentially suspicious levels of play.
Transaction monitoring and AI: the future and the here and now
While certain gaming industry functions, such as marketing, have taken advantage of developments in artificial intelligence (AI), data gathering, and other emerging technologies, anti-money laundering (AML) programs have been slow to catch up. Instead of planning for the future, the industry’s approach to AML compliance is often reactionary in nature – casino operators tend to significantly invest in their programs only in response to regulatory actions, which at times are accompanied by large civil money penalties (e.g., fines).
Today, AML-related transaction monitoring or surveillance methods are typically comprised of a suite of rules or “detection scenarios,” which are models that generate alerts when a specific set of transaction or account conditions occur. Across all financial services industries, including gaming, these alerts are then typically reviewed manually by an investigator who is required to make a determination as to whether the activity was indeed suspicious.
Based on transaction monitoring requirements detailed by various regulatory bodies, financial institutions (FIs) tend to implement and manage 25-100 different scenarios in order to capture various financial typologies they believe are commensurate with their risk profiles. Typically, these scenarios apply broad monitoring strokes to enormous amounts of data, resulting in unproductive and duplicative alerts.
Over time, FIs add more scenarios without decommissioning others, which results in higher alert volumes and consequently requires more time or staff to review the alerts. Tis archaic method of AML compliance costs money in the
P68 NEWSWIRE / INTERACTIVE / MARKET DATA
“Over time, FIs add more scenarios without decommissioning others, which results in higher alert volumes and consequently requires more time or staff to review the alerts. This archaic method of AML compliance costs money in the
short- and long-term and does not promote a proactive compliance culture.”
short- and long-term and does not promote a proactive compliance culture.
Meanwhile, technological advancements such as faster payments and open banking are changing the very nature of the financial services industry, calling for FIs to quickly and accurately flag suspicious transactions while analysing new types of data. Given the rapid evolution of both technology and the financial services industry, it is time for FIs to start
investing in innovative solutions and moving away from the traditional scenario implementation approach.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING AI and machine learning are primary examples of new technology gaining acceptance in AML compliance programmes. For example, companies are beginning to use a type of machine learning called natural language processing to surveil electronic and audio communication and flag any suspiciously used terms or phrases while accounting for tone and sentiment.
Additionally, companies are using AI-enabled analytics to aggregate and review data, allowing investigators to visualise complex financial relationships, and more easily detect unusual or suspicious activity. We have seen companies use these analytics to build a holistic view of their customers, including their relationships and transactional behavior. For example, they are using AI to enhance their customer due diligence and onboarding processes by mapping
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