Fighting Financial Crimes with Artificial Intelligence

Table of contents

1. 1. Fighting Financial Crimes with Artificial Intelligence
   1. Executive Summary
   2. Financial Crime Continues to Increase
   3. Different Types of Financial Crime
      1. Fraud
      2. Money Laundering
      3. Cybercrime
   4. Fallout to Financial Services Firms of Successful Crime
   5. Challenges to Keeping on Top of Financial Crime
      1. Criminals Are Innovating Relentlessly
   6. Current State of Anticrime Measures in Financial Institutions
      1. Today’s Transaction Monitoring Systems
      2. The Burden of False-Positive Alerts in a TMS
   7. The Emergence of AI-Based Crime-Fighting Systems
      1. Benefits of AI-Based Models to Fight Financial Crime
   8. Challenges of Deploying AI Models when Fighting Financial Crime
      1. AI Models Can’t Just Be Handed Over to IT
      2. Model “Explainability” Can Be Difficult
      3. Fragmentation of Teams—and Therefore Siloed Data
      4. Real-Time AI Deployment: Current Realities and Constraints
      5. Managing AI Models in Production
      6. Managing Alerts from AI-Based and AI-Enhanced Monitoring Systems
      7. Other Operational Challenges
      8. Accelerating AI Model Deployment with AI-based AnalyticsOps
      9. Don’t Replace Your TMSs—Supercharge Them
   9. Real-World Case Study: Danske Bank
      1. Overcoming Challenges
      2. From Machine Learning to Deep Learning
      3. A Platform for the Future
   10. Conclusion