In this paper we have presented our state-of-the-art model for predicting financial crime. By incorporating public data sources with a random forest classifier, we are able to achieve 90.12% predictive accuracy. We are confident that our model matches or exceeds industry standards for predictive policing tools.
Crucially, our model only provides an estimate of white collar crimes for a particular region. It does not go so far as to identify which individuals within a particular region are likely to commit the financial crime. That is, all entities within high risk zones are treated as uniformly suspicious.
Recently researchers have demonstrated the effectiveness of applying machine learning techniques to facial features to quantify the "criminality" of an individual.
We therefore plan to augment our model with facial analysis and psychometrics to identify potential financial crime at the individual level. As a proof of concept, we have downloaded the pictures of 7000 corporate executives whose LinkedIn profiles suggest they work for financial organizations, and then averaged their faces to produce generalized white collar criminal subjects unique to each high risk zone. Future efforts will allow us to predict white collar criminality through real-time facial analysis.
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