Computer-implemented machine learning (ML)-based techniques for detecting payroll fraud are provided. In one set of embodiments, these techniques employ a number of ML algorithms to evaluate different types of fraud-relevant data in different ways, such as outliers in salary increases, payment patterns, and so on. In some cases, the ML algorithms may be chained such that the output of one ML algorithm feeds as input into another. The results of these ML algorithms (or chains of algorithms) are fed into a neural network-based final evaluation engine that outputs an indication of whether a given employee is suspicious and should be audited as a potential payroll fraud case.