How is data science used within Rabobank?

Data Science is responsible for the development of (applied) analytics solutions based on large data sets. The common denominator of all the solutions we build, is that we try to utilize big data sources to create business value. In order to do that, we need to know what the business needs, analyse the data and translate business needs to a suitable analytics solution.

Why is Data Science important in banking?

There is a lot of information in data. Utilising this information can further improve the clients experience, but it can also streamline internal processes and contribute to more revenue.

What do data scientists do in finance?

Data scientists in finance mostly work on applied analytics solutions, meaning that the solutions data scientists build end up in production. In order to do this data scientists need to create robust data pipelines and develop models in such a way that they can be put into production. Typical data science puzzles in finance are about knowing your customers, risk detection and natural language processing.

What are the most used analytics techniques at Rabobank?

At Rabobank, we use a wide variety of analytics techniques, depending on the use case and the available data. Examples are Logistical regressions, Monte Carlo simulations, Autoregressive econometrical models and Machine Learning techniques, but this is just the tip of the iceberg.

What is data validation in banking?

Data validation is the set of tools and methods used to assess the data supporting the risk measurement and management process. Specifically, we verify the following aspects: data collection process, data quality, data treatment as well as the existence of policies and rules regulating the whole data life cycle, from data entry to reporting. We do this for both historical data and current application data.

Data Science

For sharp analyses and valuable insights

Data must be given meaning. We do this within Data Science: statistics for advanced users with the aim of interpreting the data and drawing valuable conclusions from it. At Data Science we solve the most complex problems with algorithms. We build and validate models to cover risks and optimise processes. We use them to create new or improved products and services. And with which we remain a solid bank that complies with the relevant laws and regulations.

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Stories of our colleagues

From complex regulations to understandable models

Read the story of Marleen

Translating complex problems into understandable answers

Read the story of Edgar

How risk impacts the bank, our customers and society

Read the story of Karen

Working at the heart of the bank, making it future proof with predictive models

Read the story of Paula

Engineering risk models with the customer at the counter in mind

Read the story of Natacha

Making history as a Credit Risk Modeller; it is happening now

Read the story of Martin

Model validation: essential for the bank and for society

Read the story of Francesco

Building business intelligence solutions for a better world: it's challenging and fun

Read the story of Olena

I keep things running smoothly between modelers and data analysts

Read the story of Ron

It really gives a kick to build models that are used around the world in the food & agri sector

Read the story of Jasper

Working at a data-driven and innovative bank

Read the story of Joost

Using data to make the world a better place

Read the story of Edwin

FAQ

Data Science is responsible for the development of (applied) analytics solutions based on large data sets. The common denominator of all the solutions we build, is that we try to utilize big data sources to create business value. In order to do that, we need to know what the business needs, analyse the data and translate business needs to a suitable analytics solution.
There is a lot of information in data. Utilising this information can further improve the clients experience, but it can also streamline internal processes and contribute to more revenue.
Data scientists in finance mostly work on applied analytics solutions, meaning that the solutions data scientists build end up in production. In order to do this data scientists need to create robust data pipelines and develop models in such a way that they can be put into production. Typical data science puzzles in finance are about knowing your customers, risk detection and natural language processing.
At Rabobank, we use a wide variety of analytics techniques, depending on the use case and the available data. Examples are Logistical regressions, Monte Carlo simulations, Autoregressive econometrical models and Machine Learning techniques, but this is just the tip of the iceberg.
Data validation is the set of tools and methods used to assess the data supporting the risk measurement and management process. Specifically, we verify the following aspects: data collection process, data quality, data treatment as well as the existence of policies and rules regulating the whole data life cycle, from data entry to reporting. We do this for both historical data and current application data.