Developed two core risk analytics features for the Julek-AK/AE2224-I-B04 repository, focusing on scalable risk interpretation and event-level review. Leveraging Python and the Pandas library, implemented a threshold-based binary encoding system to categorize risk levels, enabling rapid binary risk analysis for business decision support. Introduced event-level risk aggregation by grouping data on event identifiers and compiling risk values into per-event lists, supporting detailed event-specific analysis. Prioritized performance optimization in risk conversion processes, reducing processing time and enhancing workflow scalability. The work emphasized data aggregation, preprocessing, and analysis, laying a foundation for efficient, end-to-end risk monitoring without addressing bug fixes.