Financial networks represent the daily business interactions of customers and suppliers. Research in this domain has mainly focused on characterizing different network structures and studying dynamical processes over them. These two aspects, structure and dynamics, play a key role in understanding how emergent collective behaviors, such as those that arise during economic crises, propagate through networks. Business interactions between companies form a direct and weighted network, where the financial distress of a node depends on the ability of its customers to fulfill payments. In situations where there is no such inbound cash flow, a company may have to close down due to a lack of liquidity. Interconnection therefore seems to be at the core of systemic fragility. Whether the nature and form of this connection may have an impact on how distress is propagated is still an open question. In this paper, we study how disruptive events propagate through different network structures, under different scenarios. For this purpose, we use a liquidity model that describes how the economy of nodes evolves from a given initial state in terms of their interactions. From our experiments, we empirically conclude that most of the studied network dynamics reach a steady-state, even in the presence of large noise values.