Machine Learning and Credit Risk: a suitable marriage?

Using Random Forests for Credit risk models

How can financial institutions apply Machine Learning techniques in the field of credit risk modeling in the age of Big Data and Artificial Intelligence?

Machine Learning techniques play an increasingly more important role in the business development of financial institutions and in their risk management from a regulatory point of view.

The larger availability of data and increased computational power are providing possibilities for financial institutions to use these new techniques to increase profitability and reduce costs.

Aside from improving model performance and reducing model error margins, Machine Learning techniques allow for countless more possibilities in the area of in model development.

In this paper, we present several Machine Learning approaches to modelling a Probability of Default model and provide insights into the advantages of using Random Forest algorithms.

Using random forests for credit risk models

More information?

Would you like to know more about machine learning and credit risk modelling? Please contact Koen Dessens or Dirard Mikdad via the details below.

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