As SaaS platforms grow, so does the risk of fraudulent activity—fake accounts, suspicious transactions, abusive behavior, and data manipulation. Security officers like Sophie need a tool that can proactively detect, flag, and respond to fraud signals without slowing down legitimate users or overwhelming the team.
Manual fraud detection is reactive and unsustainable. The challenge is to design a real-time fraud detection platform that uses machine learning to identify anomalies, prioritize threats, and empower security teams to take quick, informed actions to protect users and data.
Consider the following factors to ensure a well-rounded, user-centered, and business-aligned solution
User Experience & Usability
Problem-Solving & Critical Thinking
Business Alignment & Feasibility
Visual & Interaction Design
Innovation & Creativity
Clarity & Presentation
1. User Flow or Journey Map
2. Wireframes or UI Mockups
3. Prototype (Optional)
4. Design Rationale & Case Study
5. Accessibility Considerations
6. Impact Metrics & Success Measurement
Beginner, Intermediate, Expert
2 - 3 days
Access Control, Accessibility, Admin UX, AI and Machine Learning UX, AI Automation, AI Chatbot Development, AI Content Assistance, AI Explainability, AI Personalization, AI Prioritization
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