Keynote | Technical | talkReduce()

Streaming analytics @ ING

Friday 17th | 14:50 - 15:00 | Theatre 19

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One-liner summary:

At ING, we believe that staying ahead in life and business means changing how the bank interacts with their customers, no longer a traditional model of waiting for the customers to come to the bank through their website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this.

Keywords defining the session:

- Streaming

- Analytics

- Event-Driven

ING is changing the paradigm on how the bank interacts with their customers no longer a traditional model of waiting for the customers to come to the bank through their website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by events for what ING becomes an event-driven bank to support the paradigm on reacting to all events that are relevant to the customer. In addition, we need to be able to detect relevancy from events coming from different sources not only within the bank but also outside the bank and react on real-time taking the right decision in the right moment in time taking into account the context and preferences of the customer. ING is using a streaming data processing platform powered by Apache Flink and supported by Apache Kafka that offers high-throughput and low-latency, ideally suited for complex and demanding use cases in the international bank such as customer notifications and fraud detection. These use cases require fast data processing and a business rules engine and/or models execution. Integrating these components together in a always-on, distributed architecture can be challenging. This presentation addresses how ING approaches the challenge, the role that Apache Kafka and Apache Flink play, and the event-driven architecture that enables ING to have a scalable and decoupled landscape. Also, we’ll have a brief overview of the use cases and you’ll learn why ING chose Flink for these use cases. Finally, we’ll share some lessons learned and useful insights for organizations who embark on a similar journey.