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.
Previously, previously, previously, previously, previously, previously, previously, previously.
Downtown just isn't safe. Just look at all these gang members gathered in Union Square:
Wow, I thought physiognomics had died back in the early XX century, but now you throw a little bit of ML on it, name it something fancy, and on it lives… what else does this new modern world have for us?
We're coming up on the three-year anniversary of the germ theory of disease being recategorized from settled science to woke lunacy, so I suggest consulting your history books. Backwards.
Those hardened Breach of Contract hoods are the worst!
This is a significant upgrade of the algorithm! Previously, the functionality was a good deal simpler. In fact, I can reveal here the original check-in for the git repo that grew into the fully-fledged project advertised above. This is the original code as used by law enforcement teams nationwide:
As you can see, even at this early stage the functionality you expect from your Law Enforcement Service Provider was all there in nascent form. Recent additions have really just expanded upon this highly respected beginning.