In this episode, I start with a slightly strange moment at the Mastercard offices. I was catching up with someone I know and he told me I had started pushing a new narrative.

Okay. Apparently, the narrative was that rules are better than AI.

Honestly, I get why it looked that way. I talk about rules vs AI in fraud prevention quite a bit. But that is not really the point.

The point is control.

AI fraud prevention, fraud prevention AI, AI fraud detection, machine learning fraud prevention, all of it sounds great until the person responsible for money movement and customer acquisition has to approve the change. Then accuracy is not the only thing that matters. Trust matters. Explainability matters. Strategy visibility matters. And if leaders do not feel in control, they will choose worse fraud tools.

Not because they are irrational.

Because breaking the business is, technically speaking, not a good look.

What you will hear in this episode:

  • A breakdown of why the “rules vs AI in fraud prevention” debate misses the bigger issue
  • Why leaders often choose fraud detection rules over stronger AI fraud tools
  • How fraud risk management changes when the process touches money movement and customer acquisition
  • Why fraud decisioning depends on trust, not just model accuracy
  • What fraud AI tools often get wrong about explainability
  • How chargeback rate optimization can become more useful when users can compare low, medium, and high-risk strategies
  • Why AI trust in fraud prevention depends on clear KPIs, plain answers, and visible tradeoffs
  • Listeners can expect a conversation that moves from “which tool performs better?” to the more uncomfortable question: who actually feels safe enough to make the decision?


Who should listen:

  • Fraud leaders and fraud operators
  • Risk and compliance teams
  • Product teams building fraud AI tools
  • Financial institution leaders evaluating AI fraud prevention
  • Fraud technology vendors and solution architects
  • Anyone involved in fraud decisioning, chargeback rate optimization, or machine learning fraud prevention


Basically, if you have ever looked at a model and thought, “The performance is better, so why won’t they use it?” this one is for you.

Rss
Podden och tillhörande omslagsbild på den här sidan tillhör Chen Zamir. Innehållet i podden är skapat av Chen Zamir och inte av, eller tillsammans med, Poddtoppen.