KYC Data & Analytics

Crack your brains with huge datasets and the latest tools

There are few organisations where you can work with so much rich data as at an international financial services provider like Rabobank. Our gatekeeper role is priority number one, which is why we invest heavily in the right data and latest analytics models within KYC. You will have every opportunity to develop yourself and make use of the most innovative technology, advanced systems and artificial intelligence.

Are you dedicated, curious and precise in your work? Would you like to work with colleagues to constantly find new patterns and insights to reduce financial crime? Then KYC Data & Analytics at Rabobank will suit you.

Nina Bussmann - Data Scientist financial & economic crime

Frequently asked questions about working in KYC Data & Analytics

  • As a data analyst in the KYC domain, you build data solutions based on state-of-the-art modelling technology. These solutions help the bank identify suspicious transactions and high-risk customers more accurately. We also use network analytics to detect misuse faster and better. We work on a specific solution in multidisciplinary squads (6-8 people).

    In addition, there are area-specific guilds that work to improve quality, professionalise the way we work and organise fun events. For example, we regularly organise hackathons to tackle interesting problems within the bank using the latest technologies.

  • For some roles within KYC Data & Analytics, an IT or data background is a plus but not a hard requirement. Often, experience in a similar role is sufficient. Moreover, there are many opportunities for you to develop in the field.

  • The squads work at the intersection of business and IT and it depends on the role within the team which technologies you use. As an IT professional, data engineer or data analyst, you will work with: Python/Pyspark, SQL, Dataiku, GitHub, DevOps, DataBricks and Azure.

  • Rabobank is an international organisation with clients all over the world. All squads speak English and are multicultural. The data we analyse and use in the models come from all our customers, national and international. By looking beyond national borders, we detect misuse or fraud earlier.

  • In certain models, we use clusters to detect, for example, remarkable deposits of money. These clusters have a self-learning capability and are thus becoming smarter and smarter. In addition, we have, for example, developed our transaction monitoring model entirely in-house. This enables us to use the latest technology and run this model entirely in the cloud.