StreamING models, how ING adds models at runtime to catch fraudsters

These days companies are collecting more and more data. It’s up to data scientists to create business value out of that data. Typically this is done by training models based on historical data stored on HDFS. Once the model has been trained it is ready to be scored. At ING Bank we need to score models in real time, blocking potential fraudulent transactions before causing damage to either the customer or the bank. As fraudsters invent new ways to commit fraud, we also need to add new models on a running system, without downtime. In this talk we’ll present our implementation of a real time streaming analytics platform that enables us to dynamically change the behaviour of our stateful Flink application. The end result is an environment where end users are provided a DSL they can use to dynamically stream in new models into the Flink job as well as to change the transformations within the operators. This will give them full control of the streaming analytics platform at runtime.