How machine learning can cut costs on transaction monitoring: a new design for a new world!


July 21 2016

IT solutions companies are creating tools to increase robustness in the transaction monitoring process and the detection of unusual financial activity. These systems are based on standard typologies of money laundering:

  • Identifying spikes in value or volume of transactions
  • Monitoring high risk jurisdictions
  • Identifying rapid movement of funds
  • Screening against sanctioned individuals and politically exposed persons (PEPs)
  • Monitoring enlisted terrorist organisations
  • Etc.

If the IT system was able to learn from previous cycles and identify false positives before an alert was generated, it would be a ‘game-changing’ factor in transaction monitoring, speeding up and increasing the accuracy of identifying the truly suspicious activity. We are seeing increasing examples of machine learning in many areas of technology and financial institutions should grasp the opportunity to use it for repetitive analysis.