Many financial institutes use scoring models to lower credit risk in credit appraisals, and in the granting and supervision of credit. Credit scoring models based on classical statistical theories are widely used. However, these models are less resilient when it comes to large amounts of data input; as a consequence, some of the assumptions in the classical statistics analysis fail. This influences the accuracy of prediction and of model generalizations. In this blog post, we will explain how machine learning can be used in credit scoring to achieve a more accurate scoring from large amounts of data.