video1.0<iframe src="https://www.loom.com/embed/f538e0a02b6941bf8d794d13d3df5f7c" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/f538e0a02b6941bf8d794d13d3df5f7c-974b98611b2195b0.gif606.761In this video, I present my risk analysis flagship project, which is an end-to-end credit scoring and fraud detection system featuring daily pipeline monitoring and a web showroom for stakeholders. The demo showcases a 30-day snapshot derived from a Kaggle dataset, highlighting key metrics like average probability of default and flagged fraud percentages. I also explain how the system tracks model performance through MLflow and sends alerts for any significant changes. I encourage you to explore the hosted demo and review the operational guide for a deeper understanding of the system's functionality. Your feedback and insights would be greatly appreciated as we continue to refine this production-style risk system.