Big Data credit scoring model for a direct bank

Migration of an overdraft scoring model into the Big Data infrastructure for the prediction of default probabilities.

Implementation of a Random Regression Forest in Spark and H20 (Sparkling Water)

Automated retraining of the model with current data possible

Fulfilment of all risk management requirements


A German direct bank faces the challenge of migrating the prediction of loan defaults into the new Big Data infrastructure. The prototype of a scoring model for predicting loan default probabilities from a previous project is to be implemented in Spark.


In the first step, several possible Big Data technologies are evaluated for the implementation. After the evaluation, the model is implemented using the combination of Spark and H20 (Sparkling Water) using a random regression forest.


The implemented model can be used within the new Big Data infrastructure for online scoring of customers. The implementation makes it possible to re-train the model with current data at any time and thus optimise the bank's default risks. The risk management requirement is also met by historising and storing the models.

Are you interested in your own use cases?


An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.


There will be a interactive and Flexible application, including of different maps with two different views implemented.


Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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