Machine learning-based fraud detection tools have the potential to parse through troves of data—both structured and unstructured—to identify suspicious activity with greater accuracy and speed, and potentially enable firms to respond in real time.
Machine learning models are being used to analyze traditional and alternative data in the areas of credit decisionmaking and credit risk analysis, in order to gain insights that may not be available from traditional credit assessment methods and to evaluate the creditworthiness of consumers who may lack traditional credit histories.