How AI Can Help Overcome Challenges in Correspondent Banking Relationships

The Financial Crimes Enforcement Network (FinCEN) recently levied a $2 million civil money penalty against a Southwest Border bank for willfully violating the Bank Secrecy Act (BSA). The bank failed to fulfill certain due diligence obligations in relation to its handling of the opening and maintenance of correspondent banking (CB) accounts.

FinCEN’s Acting Director Jamal El-Hindi, stated: “The bank plainly failed to ask obvious due diligence questions in connection with its foreign bank account relationship, and did not follow up on inconsistencies in answers to the questions that it did ask.”

The ability to make and receive international payments via CB is vital to the global economy. Whether it is Texas-based manufacturer exporting batteries to Mexican customers, or an engineer working in Asia sending money to his family in Europe, financial institutions (FIs) rely on CB relationships to move their customers’ money. Director El-Hindi continued: “Smaller banks, just like the bigger ones, need to fully understand and follow the 312 due diligence requirements if they open up accounts for foreign banks. The risks can, indeed, be managed, but not if they are ignored.”

By its simple definition, CB is an arrangement in which a correspondent bank provides services to another bank (the respondent), often so the respondent can obtain access to overseas financial, trade, or other products. As a result of regulatory requirements and increasing risk related to shell companies, CB relationship management has become more challenging, costly, time consuming, and riskier. Some FIs no longer want to manage CB relationships linked to money service businesses (MSB), or CBs in high-risk jurisdictions in fear of regulatory scrutiny, investigation, and possible penalties.

According to World Bank estimates, global remittances will increase to more than $636 billion in 2017. According to the April 27, 2017 World Bank report “Migration and Development Brief,” the global average cost of sending a $200 transfer was 7.45 percent in the first quarter of 2017, which is substantially higher than the three percent goal.

World Bank experts stated that a chief barrier to reducing remittance costs is due to the de-risking by international banks. The report cites: “When they (international banks) close the bank accounts of money transfer operators, in order to cope with the high regulatory burden aimed at reducing money laundering and financial crime, this poses a major challenge to the provision and cost of remittance services to certain regions.”

High remittance volume brings increased regulatory scrutiny, risk, and compliance costs that chomps at FIs’ heels. The unfortunate outcome is that FIs often choose to de-risk foreign CB relationships rather than deal with the inherent risk and problems of maintaining CB accounts. According to a November 2015 World Bank survey, 75 percent of major global banks stated that they had seen a decline in their CB relationships. The World Bank report further stated that the high-risk Caribbean region noticed the most CB decline.

In 2016, the U.S. Department of Treasury’s Office of the Comptroller of the Currency (OCC) issued the following guidance to their regulated FIs regarding best practices for managing a CB relationship:

  • Establishing and maintaining an effective governance function to review the method for risk reevaluation and to monitor the appropriateness of recommendations regarding foreign correspondent account retention or termination;
  • Communicating foreign correspondent account termination decisions regularly to senior management;
  • Communicating with foreign financial institutions, considering specific mitigating information these institutions may provide, and providing sufficient time to establish alternative banking relationships before terminating accounts, unless doing so would be contrary to law or pose an additional risk to the bank, national security, or reveal law enforcement activity, and;
  • Ensuring a clear audit trail of the reasons and method used for account closure.

Effective CB relationship risk management boils down to having the adequate tools to efficiently resolve entities that bank through CBs. Recurring challenges for compliance and investigative teams include establishing an economic purpose and verifying complementary lines of business for CB customers. Banks often find themselves filing conservative suspicious activity/transaction reports (SARs) because the necessary data is unavailable.

Entity resolution and entity relationship investigation is integral to curbing the de-risking cycle. The ability to establish behavioral histories related to the volume of transactions and their amounts, expected volume of transactions and amounts, current and ongoing ultimate beneficial owner data, and any/all adverse media related to the entity in question, is the key to ensuring accurate CB risk management and KYCC.

Expanding the same data points to an entity’s related parties (customers) provides a holistic risk picture for compliance and investigative teams within a bank or covered FI. By leveraging data science, such as artificial intelligence (AI) and machine learning, time-mongering and costly CB and pseudo-customer investigations become automated, providing investigators with the most essential data, as outlined above.

Furthermore, global businesses are now identified through a North American Industry Classification System (NAICS) code, replacing the Standard Industrial Classification (SIC) system codes in 1997. NAICS codes are reviewed and updated every five years. The NAICS separates entities and businesses into industries based on relationships in their processes. Currently, there are 20 sectors and 1,057 industries.

Existing AI-enhanced solutions analyze NAICS codes for CB customers and compares the focal entity and counter parties of an alert to similar entities in transactional data that share common NAICS codes. The resulting AI analysis reports if the transaction makes sense from a valid economic purpose perspective, and if the entities are engaged in complementary lines of business.

Advancements in data science, AI and machine learning enable compliance teams and anti-money laundering (AML) investigators to fulfill their regulatory obligations with precision, and further assist FIs in the de-risking quandary.