  To probe the largest public-domain email database for indicators of fraud, we
apply machine learning and accomplish four investigative tasks. First, we
identify persons of interest (POI), using financial records and email, and
report a peak accuracy of 95.7%. Secondly, we find any publicly exposed
personally identifiable information (PII) and discover 50,000 previously
unreported instances. Thirdly, we automatically flag legally responsive emails
as scored by human experts in the California electricity blackout lawsuit, and
find a peak 99% accuracy. Finally, we track three years of primary topics and
sentiment across over 10,000 unique people before, during and after the onset
of the corporate crisis. Where possible, we compare accuracy against execution
times for 51 algorithms and report human-interpretable business rules that can
scale to vast datasets.
