The U.S. Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) issued its Final Rule for Customer Due Diligence (CDD) under the Bank Secrecy Act (BSA) for banks and other covered financial institutions on May 11, 2016. The Final Rule, effective July 11, 2016, is designed to formalize new and existing customer due diligence (CDD) requirements.
The Final Rule codified four anti-money laundering (AML) provisions or “pillars” found in Section 352 of the USA Patriot Act. FinCEN also added a fifth pillar, requiring covered institutions to:
- Identify and verify the identity of the beneficial or true owner(s) of the account by determining who directly or indirectly owns 25 percent or more of the equity interests of the legal entity customer; or
- Determine which individuals control, manage, or direct a legal entity customer, including an executive officer or senior manager, or any other individual who regularly performs similar functions.
In addition, financial institutions must verify that customer information is based on “reasonable and practicable risk-based” procedures. Covered financial institutions must comply with the fifth pillar rule by May 11, 2018 (the “Applicability Date”).
When undertaken in a comprehensive way, data science can help financial institutions reduce money laundering, terrorist financing, and other financial crimes risks. Data science and Artificial Intelligence (AI) can strategically address FinCEN’s Fifth Pillar requirements without undertaking expensive and highly manual processes of conventional Know Your Customer (KYC), Customer Due Diligence (CDD), or Enhanced Customer Due Diligence (EDD). Banks and financial institutions are already required to maintain a Customer Identification Program (CIP), but data science and AI can alleviate the requirements of asking clients highly intrusive questions or blindly trusting the answers that are provided on forms and applications.
A data science/AI approach enables banks and financial institutions to surpass regulatory required KYC decisions. Through tactical and strategic financial crime focused algorithms, coupled with independent and external sources of information, data science/AI-powered solutions can define specific risk factors for affiliated institutions or customers and predict future risk trends.