Machine Learning For AML & KYC Programs

In combination with ‘know your customer (KYC) processes to help catch suspicious banking activity and reduce risk through the analysis of data and the identification of patterns, you can execute a three-part anti-money-laundering (AML) approach to detect, prioritize, and evaluate predetermined indicators found in various data sources.

  • Detection is the act of looking for and/or identifying indicators using data from any source.
  • Prioritization is the act of applying plausibility criteria to identified indicators so as to place the indicators in context and apply appropriate resources.
  • Evaluation is the act of formally gathering additional data to evaluate the identified indicator for the purpose of risk management.

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The ability to actively monitor and identify data disparities and suspicious activity early on can help companies fight money laundering activities and the adverse effects they create, including disciplinary action, substantial fines, and damage to a company’s reputation.

Just last year, the Financial Industry Regulatory Authority (FINRA) fined a firm $16.5 million for having serious deficiencies in its AML program. FINRA also fined a firm (and its affiliated introducing firm) $17 million for having a poor customer identification program (CIP), among other deficiencies, that can lead to money laundering and the inability to prevent its detection.