How machine-learning models can help banks capture more value Digital McKinsey

Machine learning (ML) methods have been around for ages, but the big-data revolution and the 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, also 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 rather than underlying relationships) of the model is a typical concern about ML.