They're attempting something fantastically difficult to do effectively (arguably beyond human capability) and arguing that their slightly-better-than-chance results indicate that their approach is promising. More likely, the system is picking up on some "low hanging-fruit" indicators in the data (perhaps that corporate filings with certain buzzwords are likely to fail, or that reference projects beyond a certain age, or similar) which would already be easily picked up on by human analysts, and that anything much beyond that would be impossible with anything remotely resembling the present method.

I want to posit a general hypothesis (perhaps it's already been said); better-than-chance performance by a classifier on some dataset is not evidence that a similar (or any) classifier can perform better on the same data.