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Introduction

Use of AI-driven machine-learning techniques for anti-money laundering has been limited by a lack of decision transparency for regulators. Oracle is looking to address this by providing a pictorial tool to explain the decision-making narrative.

Highlights

  • Ovum's 2017/18 ICT Enterprise Insights program found that across risk and compliance, AML is the area where the most retail banks are looking to increase IT spend in 2018.

Features and Benefits

  • Understand the main trends and pressures in anti-money laundering compliance.
  • Assesses the potential for Oracle's platform developments in this space.

Key questions answered

  • What role can machine learning have in tackling financial crime?
  • How can machine learning become accepted by regulators?

Table of contents

Ovum view

  • Summary
  • Regulators remain tough on ensuring AML detection effectiveness
  • ML techniques can drive self-tuning, but regulatory acceptance is key for frontline deployment
  • Oracle's ability to generate and show supporting narrative is an important step in driving regulatory acceptance

Appendix

  • Further reading
  • Author

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