This week has been defined by a rigorous focus on model optimization and enhancing the scalability of our platform. We successfully boosted the performance of our ensemble machine learning model, specifically increasing recall by fine-tuning hyperparameters and adjusting our autoencoder thresholds. To ensure these improvements are sustainable and reproducible, we developed an automated pipeline to perform grid searches for continuous tuning. On the frontend, we’ve prioritized user experience by introducing pagination to the dashboard, significantly reducing visual clutter and improving interface responsiveness as our data volume grows.

Looking ahead to next week, we are elevating our optimization strategy by utilizing Optuna to perform advanced hyperparameter tuning across each individual model and the ensemble as a whole. We will be validating these refined models against synthetic datasets while simultaneously developing our middleware and postprocessor logic. Our UI efforts will continue with the expansion of pagination across all sites and the implementation of context-aware map zoom functionality. We also remain focused on our collaboration with our liaison to integrate real-world fraud examples into our final testing phases.