AI at ING: The why, how, and what of a data-driven enterprise

This will be presented in English.

Bas Geerdink (ING)
16:2017:00 Friday, June 21, 2019

必要预备知识 (Prerequisite Knowledge)

  • General knowledge of AI, big data, architecture concepts, and financial enterprises

您将学到什么 (What you'll learn)

  • Learn from a big financial enterprise how to apply AI in practice

描述 (Description)

AI is at the core of ING’s business. It is a data-driven enterprise, with analytics skills as a top strategic priority, and is investing in AI, big data, and analytics to improve business processes such as balance forecasting, fraud detection, and customer-relation management. In innovation, AI plays a key role in going beyond banking and redefining traditional banking services in fintech accelerator programs. The company’s working on a large scale with big data technologies to set up a data lake where all data flows from the bank come together. Once stored in stating or streaming form, this massive amount of data can be used for traditional and new innovative use cases.

Get an overview of the journey that a large bank as made from traditional IT toward in-house development with modern open source frameworks and Agile methodologies. Along the way, Bas Geerdink examines an important part of ING’s architecture. He gives some examples of interesting data-driven use cases, and you’ll look under the hood when he shares some results from running the models in production. Learn the lessons Bas and ING learned from the innovation department about the way working and deployment mechanisms create a successful data-driven team. You’ll learn from a big financial enterprise how to apply AI in practice, and you’ll get inspired by a working architecture of the data lake and innovative use cases at the bank, such as look ahead and instant lending where AI plays a crucial role.

Photo of Bas Geerdink

Bas Geerdink


Bas Geerdink is a programmer, scientist, and IT manager at ING, where he’s responsible for the fast data systems that process and analyze streaming data. Bas has a background in software development, design, and architecture with broad technical experience from C++ to Prolog to Scala. His academic background is in artificial intelligence and informatics. Bas’s research on reference architectures for big data solutions was published at the IEEE conference ICITST 2013. He occasionally teaches programming courses and is a regular speaker at conferences and informal meetings.

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