Ravelin's Responsible AI Principles

As we develop new products and AI systems to help our clients gain valuable insights and enhance fraud detection capabilities, we will adhere to these Responsible AI Principles throughout the entire life cycle of each AI system. We will review and update documentation annually or sooner where appropriate.

For each AI system we have considered:

• Its purpose and how it will solve the problems intended through its use.

• Impact on individuals and clients, including any additional oversights required.

• Proposed model inputs and outputs and the quantity and suitability and accuracy of the data sets.

• Procedures to collect and process data, including labelling, cleaning, enrichment and aggregation.

• Limitations to the generalisability of models based on the training and testing data used.

• Features to enable clients to report errors and provide feedback.

• Oversight and control functions.

• The system UX, features and client training materials.