> Our algorithms picked up these red flags and more, and assessed Sino-Forest as high risk when we ran our models on the company’s historical filings.

When you're backtesting your models, how do you distinguish between novel fraud that the industry is _now_ aware of vs. fraud that was visible but ignored -- if your model has learned _from_ Sino-forest, how do you know it would have caught Sino-Forest at the time?

> For instance, this sentence sounds like it could be indicative of terrible things going on behind the scenes but is in fact, just boilerplate disclosure: “We face risks and uncertainties related to litigation, regulatory actions and government investigations and inquiries.” You can see how ML models easily get confused.

Humans too, you're describing at least one comment in every HN thread about an SEC filing :D