This study takes a multidisciplinary risk factor approach to enhancing corporate crime enforcement with machine learning.
In this project, researchers seek to overcome challenges to corporate crime enforcement by unifying cross-disciplinary financial fraud research under a risk factor framework and by leveraging recent advancements in artificial intelligence. The goal is to examine whether two machine learning algorithms—random forest and neural network—can be used to enhance corporate fraud risk detection/prediction beyond more commonly employed analytical techniques. Despite its severe and lasting social and financial ramifications, corporate financial crime remains one of the most understudied crime types, as it is often hindered by two challenges. First, its multidisciplinary nature requires both financial and criminological expertise among others to conduct proper investigations. Second, corporate crime data is fraught with constraints such as high dimensionality, complex interactions, and nonlinear functional forms that are ill-suited for classical statistical modeling. The lack of research coupled with the limited resources in corporate crime enforcement represent a great impediment to the advancement of fraud interventions.