How machine-learning models can help banks capture more value
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. Overfitting of ML models can be avoided by carefully choosing input variables and specific algorithms. One way to guard against overfitting is to use the popular Random Forest algorithm. This is an ensemble of many intentionally “weakened” decision trees, essentially a partial set of variables with each iteration of the model, thereby reducing the reliance on specific variables. In another example, ML model performance is also tested on a holdout sample not used during the model-development process. If the model performance on the sample is significantly degraded, it’s a sign of overfitting.
Where ML is superb is in analyzing long-tail data, which typically account for half of a bank’s portfolio but are not well understood through traditional statistical methods. Think of accounts with low share of wallet. We usually know little about them, and strategies to engage them tend to be quite reactive. But ML has the ability to generate insights into their behaviors to actively target the accounts that are potentially profitable.
Let’s take as an example an ML project focused on optimizing line decisions in credit cards. The company was seeking to optimize credit-line decisions for their cards business; that is, they wanted to make better decisions about where to increase and decrease credit lines.
The existing models were performing and already had a very respectable predictive power. We used the existing traditional account data and set up our ML model as a challenger to the existing credit-line strategies.
