Letting the "Gini" out of the bottle: How Machine Learning models can help banks capture value now

"Machine Learning" (ML) methods have been around for ages but Big Data revolution and plummeting cost of computing power are now making them truly excellent and practical analytical tools in banking, across a variety of use cases, including credit risk. ML algorithms may sound complex and futuristic but the way they work is quite simple. Essentially they combine a massive set of decision trees (i.e., a decision-making model that breaks out individual decisions and possible consequences, as known as "learners") to create an accurate model. By churning through these learners at high speeds, ML models are able to find "hidden" patterns, particularly in unstructured data that common statistical tools miss. Overfitting (the analytical description of random errors instead of underlying relationships) of the model is a typical concern that comes up in regards to ML. Overfitting of ML models can be avoided by carefully choosing input variables and the specific algorithm used.