Fraud detection is described as a set of activities undertaken to prevent money or property from being obtained through illegitimate means. Machine Learning can help analysts determine which transactions are likely to be fraudulent, using techniques for fraud prevention and detection.
By employing such methods, machine learning can automate the analysis of patterns across large volumes of transactions, and distinguish between legitimate and fraudulent behaviors, while adapting over time to new inputs. This way, fraud detection should not be seen as a static process, but rather, as an ongoing cycle where organizations should continually learn and adapt.
A few examples of sectors that can highly benefit from data science fraud prevention techniques are retail, banking, healthcare, public sector, and insurance.
You can learn more about this term reading our use case on Fraud Detection.