How to Deploy AI in Your Fraud Program From Strategy to Measurement
Summary
Companies are requiring the use of AI in order to improve efficiency, but the lack of clear guidelines means that AI implementations are still speculative rather than strategic.
This session gets rid of all the theory and shows you how to actually put AI into your fraud prevention programme. Jordan Harris gives real-world examples of the platforms that he uses, the measurement frameworks that he puts into place, and the way in which the operations are set up so that they can scale.
You'll get an overview of the practical workflow, including where AI should be incorporated into your current setup, how to assess its impact, which metrics are important, and how to demonstrate its return on investment to senior management. This approach is not about promoting a particular vendor; it's a straightforward explanation of what does and what doesn't work and the reasons why.
What You'll Learn
By the end of this session, attendees will be able to:
- Create an AI deployment that is practical and fits into your current fraud detection system without needing a full rebuild.
- Set up measurement frameworks which will record the real effect of AI on approval rates, on review capacity, and on fraud detection rates.
- Make the operational changes needed if you are to work effectively with cases that have been scored by AI and to be able to make confident decisions.
- When presenting the current business results to senior management, use metrics that link AI deployment to both revenue protection and operational efficiency.
- Avoid common pitfalls and learn from real deployments what works and what doesn't.
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