Webinar

Mitigating Bonus Abuse for Gambling Operators

This webinar focuses on the online gambling vertical.

Based on our experience working with gambling operators, we decided to examine one of the biggest challenges of the industry: users abusing bonuses in online gambling fraud.

We will offer insights about how to improve your user journeys in a way that brings different organisation departments together so finance, IT, marketing can all be involved. We will also present a solution on how to leverage our fraud prevention system using your existing data.

Agenda

  • Facts and figures.
  • Define the problem and its occurrence.
    • Present relevant statistics,
    • Define the global market,
      • What is bonus abuse in monetary amounts?
      • Show screenshots of websites explaining how to abuse gambling operators.

Who is the one to bear the brunt of bonus abuse? – A stakeholder analysis

  • Examine from a fraud manager perspective,
  • Examine from a financial perspective,
  • Examine from an IT perspective,
  • Examine from a top management perspective.

User journeys resulting in unplanned, unwanted losses for operators

  • Decreasing friction but increasing layers of defense,
  • Evaluate authentication points in the customer journey,
  • Examining data points and trails left behind by these users.
  • Propose heavy vs light KYC,
  • Machine learning vs human intelligence: why they should work hand-in-hand.
  • Propose a customer journey with relevant heavy and light KYC steps.

Catching a bonus abuser in real-time:

  • An interactive demo.
  • Show how registration monitoring can lead to the detection of anomalies.
  • Present what this looks like on a platform as well as on the admin panel.
  • How the results were affected by user behaviour.
  • Present facts and figures about implementation and the results.

 

Watch our webinar to learn about how to prevent bonus abuse in your business.

You might also be interested in reading about:

Learn more about:

Digital Footprint | Browser Fingerprinting | Device Fingerprinting | Device Intelligence | Fraud Detection with Machine Learning

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