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Driving the acceptance of AI for financial crime prevention
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"Driving the acceptance of AI for financial crime prevention"

Organizations just starting to implement machine-learning and AI approaches for financial crime prevention face many new regulatory challenges with interpretability, responsible use, model validation and ongoing performance monitoring. Financial crime compliance programs need to leverage tech the right way to optimize the value of advanced modeling techniques, conquer financial crime prevention and maintain trust among stakeholders.

Read "Driving the acceptance of AI for financial crime prevention" to learn how to:

  • Generate stakeholder buy-in from the start
  • Document decisions and testing throughout the model development process to satisfy regulatory examination and committee reviews
  • Keep model validation up-to-date and understand why staying current is important
  • Continuously monitor AI models for a detailed understanding of estimations and metrics

Between regulatory, operational and financial crime challenges, firms can’t afford to launch haphazard AI modeling. You’ll need to leverage the right tech and approach to improve performance and efficiency.


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