Bridging the Gap: Leveraging Machine Learning and Data Analytics for Transaction Monitoring

Identifying suspicious activity in correspondent banking transactions is one of the most difficult tasks in financial crime compliance. The growing number of transactions, combined with insufficient data on either the purpose of the transactions or the parties involved, makes the task increasingly challenging—and risky—for financial institutions. Traditional analytic methods have proven inadequate for the task, so Genpact used big-data analytics and machine learning to devise a new methodology for identifying risky transactions.

This white paper covers:

  1. The Challenges of correspondent banking
  2. A novel approach to correspondent banking transaction surveillance
  3. Implications for correspondent banking due diligence

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