The key points covered in the text relate to the limitations of batch-based risk analysis processes in the past, which often resulted in delayed insights and incorrect data leading to significant financial losses. The author suggests that these issues can be addressed by adopting a data streaming platform approach, which enables real-time processing of large amounts of data, reducing latency and improving accuracy. By utilizing event-driven design and leveraging capabilities like Flink in Confluent Cloud, businesses can create accurate, up-to-date risk aggregation and analysis, simplify their architecture, reduce duplication of data, and save costs. The proposed solution involves creating 'current state' aware data products with in-stream processing, providing continuous updates on real-time exposures, and integrating with downstream analytics and reporting tools to create regulatory risk reports as needed.