  Modern society heavily relies on strongly connected, socio-technical systems.
As a result, distinct risks threatening the operation of individual systems can
no longer be treated in isolation. Consequently, risk experts are actively
seeking for ways to relax the risk independence assumption that undermines
typical risk management models. Prominent work has advocated the use of risk
networks as a way forward. Yet, the inevitable biases introduced during the
generation of these survey-based risk networks limit our ability to examine
their topology, and in turn challenge the utility of the very notion of a risk
network. To alleviate these concerns, we proposed an alternative methodology
for generating weighted risk networks. We subsequently applied this methodology
to an empirical dataset of financial data. This paper reports our findings on
the study of the topology of the resulting risk network. We observed a modular
topology, and reasoned on its use as a robust risk classification framework.
Using these modules, we highlight a tendency of specialization during the risk
identification process, with some firms being solely focused on a subset of the
available risk classes. Finally, we considered the independent and systemic
impact of some risks and attributed possible mismatches to their emerging
nature.
