7 Understanding financial fraud and foundational detection strategies

 

This chapter covers

  • Understanding the criticality and evolving landscape of financial fraud
  • Identifying key financial fraud typologies and their characteristics
  • Applying a four-layer framework to foundational fraud detection techniques
  • Detailing AI-powered enhancements for each layer in fraud system
  • Exploring advanced AI modeling, including LLMOps for fraud management

The image of a masked bandit robbing a stagecoach in the Wild West, pistols drawn and demanding "Your money or your life!", might seem like a relic of a bygone era. In those days, theft was a physical act, involving the tangible transfer of gold coins or paper currency. Fast forward to today, and the landscape of financial crime has undergone a dramatic transformation. The modern-day "bandits" are no longer confined to dusty trails; they navigate the intricate pathways of the digital world, exploiting vulnerabilities in systems where vast sums of money change hands not as physical cash, but as fleeting data points on a ledger. The rise of e-commerce, digital payments, and online banking has created an unprecedented flow of these digital transactions, and with it, a fertile ground for sophisticated fraudsters.

7.1 The criticality of fraud detection and prevention

7.2 Demystifying financial fraud: scope and typologies

7.3 The distinctive nature of financial fraud

7.4 Foundational approaches to fraud detection: the four-layer framework in action

7.4.1 A glimpse into the framework: foundational detection techniques

7.4.2 Data asset layer for AI-powered fraud detection

7.4.3 Modeling layer: AI techniques for fraud detection

7.4.4 Strategy & monitoring layer: from scores to actionable intelligence

7.4.5 Application layer: deploying and operationalizing fraud defenses

7.4.6 Synthesizing the layers: a lens for intelligent fraud defense

7.5 Summary