Machine learning (ML) refers to the development of computer algorithms and statistical models to perform predictions and specific tasks without explicit instructions, rather using inferences and patterns instead. Machine learning is a subset of artificial intelligence and generally falls into two main categories: 1) supervised learning, in which the outcomes are known and labelled in training data sets and 2) unsupervised learning, in which no outcome is known and the goal is to have items self-organized into clusters based on common characteristics or features. Supervised learning uses techniques like neural networks, bayesian models, regression models, statistical models, or a combination thereof. Unsupervised learning uses techniques like k-means clustering and is often used for anomaly detection. Some computer systems have the ability to “learn” or make progressive improvements on a task based on algorithms and subsequent outcomes. As an example, machine learning in fraud prevention allows algorithms to make immediate decisions on new transaction decisions, but over time to “learn’ from the outcomes of the purchases and from that new data, self-correct to make increasingly accurate predictions going forward. The fastest and most reliable path towards the learning component relies on analysts’ insights, assisted by machine-learned predictions, to make well-informed decisions.