Failures like the Silicon Valley Bank in 2023 is the extreme result of not accurately calculating risk in a timely manner. Nearly every financial institution has a focus on minimizing risk, but the way we calculate that inherently requires close analysis of categorical data and relationships. Yet the majority of our algorithms only work on static, numeric data. That means persisting the data, converting it using something like one hot encoding into numerical data that is bloated, sparse, and slow to analyze, then after analysis, often having to convert again to figure out the original categories. This is painfully slow, with the state of the art being measured in hours. If we could shift that analysis left, process the original categorical data as it streams in, without modification, that could cut mean time to insight down to seconds, and possibly save financial institutions some large dollar signs. That could also enable many other options, such as using graph NLP on flowing data, finding novel behavior, detecting anomalies such as cyber-attacks before they affect systems. The speed of an in-line data processing engine like Flink or KsqlDb combined with graph algorithms and categorical analysis is uniquely powerful. Come learn about a new open source streaming intelligence system that changes the game for risk analysis and other fast categorical data processing.