bankingciooutlook

The Role of Big Data in Tackling Financial Frauds

Banking CIO Outlook | Monday, May 03, 2021

While there are several benefits of using BDAI as a tool to combat financial crime, institutions should be mindful of the possible obstacles in implementing BDAI for AML enforcement and how they can meet regulatory requirements.

Fremont, CA: Many financial institutions have responded by dramatically raising the workforce in their anti-money laundering (AML) functions, mostly at considerable expense, in response to a regulatory climate that demands ever higher standards for AML enforcement. However, increasing headcount alone may not be enough to achieve the desired results, as it may result in an increased number of false positives while still ignoring signs of intricate money laundering schemes concealed within large amounts of unstructured and unorganized data.

Financial institutions have also struggled to keep up as offenders became more sophisticated and expert at evading conventional AML controls by utilizing complex and convoluted networks of companies and cross-border transactions.

More financial institutions are incorporating big data analytics and artificial intelligence (BDAI) solutions into their AML controls and processes to address these issues, and some regulators have promoted greater industry adoption of such regulatory technology solutions.

The BDAI-driven AML compliance framework integrates internal bank data on a customer, such as identity and transaction records, with external data sources, such as company ownership, to uncover secret relationships between the customer and its counterparties, as well as sophisticated payment networks.

The data points are compared to hundreds of situations that may indicate money launderings, such as smurfing and structuring, or fake invoicing, by BDAI systems. After BDAI has detected suspicious customers or transactions, human analysts can investigate the matter with the help of BDAI-driven analysis, which can quickly reveal payment patterns and relationships that would otherwise be lost in massive amounts of data.

Traditional database software and human analysts would be unsuitable and inefficient for such tasks, given the high volume, pace, and variety of data that needs to be analyzed in the case study above. Furthermore, conventional rules-based transaction surveillance could be ineffective at detecting complex money laundering techniques designed to elude monitoring thresholds, as well as producing an excessive number of false positives, diverting resources and attention away from transactions that warrant closer scrutiny.

While there are several benefits of using BDAI as a tool to combat financial crime, institutions should be mindful of the possible obstacles in implementing BDAI for AML enforcement and how they can meet regulatory requirements.

Various regulators have taken different strategies to establish regulatory requirements for BDAI and other regtech. Some regulators may prefer a highly prescriptive approach, with strict criteria on the technologies and specifications used, while others have indicated that they would take a risk-based, technology-neutral approach that does not favor or disadvantage the technology used to avoid impeding innovation. Financial institutions must take action to show that their internal controls and procedures are still strong after BDAI is implemented.

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