Case study

SensaAML™ - AML Case Study

By SymphonyAI team

Operational efficiency and comprehensive risk coverage have been an elusive goal for financial institutions across the globe. Criminals have been able to exploit this weakness over the years with money laundering exceeding $4 trillion globally. SensaAML™ offers risk coverage not just for average money laundering, but tax evasions and crime financing schemes not covered by standard scenarios and rules. It can accomplish this while offering cost savings and operational efficiencies as shown in the case study below.

  • Self-learning and dynamically adaptive
  • Complete risk coverage with supervised and unsupervised ML
  • Auditable Explainability of behaviors as they emerge and evolve
  • Combines best in class graph machine learning and TDA
  • State of the art approach: 44 patents and growing
  • Seamlessly augment your existing TMS, CMS, and FIU processes or replace. Your choice.
  • Radical increase in transparency, with a sizably material improvement in efficiency and costs

Transactions of a global bank – Baseline

Transactions of a global bank – SensaAML™ Results

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