Data & Analytics
Be part of something bigger
As a cooperative with millions of clients around the world, Rabobank has access to enormous amounts of data. But data needs the right people to process it and extract valuable insights from it. That is what we do within Data & Analytics. There is no field that is so closely interwoven with every part of the bank. Working in Data & Analytics therefore means contributing to major social issues.
You develop innovations that improve the financial well-being of our customers. You contribute to the datafication of the food chain. Or you will ensure that Rabobank is and remains a data-driven, future-proof bank. And you will do all of this with a close team of highly motivated data professionals who help and challenge each other. Together you are part of a solid Data & Analytics community within Rabobank.
Be part of something bigger. Be part of Data & Analytics.
Articles of our colleagues
Model validation: essential for the bank and for society
Read the story of FrancescoMaking history as a credit risk modeller; it is happening now
Read the story of MartinEngineering risk models with the customer at the counter in mind
Read the story of NatachaBuilding business intelligence solutions for a better world: it's challenging and fun
Read the story of OlenaGlobally consistent and high quality data, that's the work of a Data Steward
Read the story of LaraData analysis to prevent customers from getting into financial trouble
Read the story of JurjenInspiring colleagues to work more and smarter with data
Read the story of JadeFAQ
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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. However, this is just the tip of the iceberg.
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Rabobank, and other big banks, have millions of customers, with many products and millions of transactions each year. This obviously results in big data. Within Data & Analytics, we use client data, historical data of mortgages, savings, payments and countless other (external) data sources.
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There are many great machine learning applications in banking. Early detection of financial distress of customers is a great example of a supervised learning problem. Both supervised and unsupervised techniques are used to get to know our customers, for example to detect anti money laundering schemes or risky transactions. Rabobank also uses machine learning to categorize transactions in the banking app.