Financial infrastructure is all about networks and systems these days. Paper and telephone have largely been succeeded by computer systems that talk to each other. That applies to intra-banking, securities trading and payment and securities settlement. That is what one can call financial infrastructure. It is about IT and operational systems.
Systems have also another meaning when it comes to finance. Irrespective of the type of link, participants in the market operate according decision making drivers, which can be rules (take for instance a controller of a securitization vehicle or a CDO vehicle that needs to channel the income streams to the different classes of securities holders, derivatives that are offered without human intervention and priced on Black Scholes formula, or an algorithmic trading engine that makes decisions based on programmed and machine-learned algorithms), can be learned (be it business school, a finance course or real world) or can be human emotions. But the interconnected nature of finance and the fact that also participants in the market settle obligations on the basis of a current stock they have, but often also on an expected flow they will receive (“I pay my expenses and obligations, based on my drivers, and with a buffer I have and with income I need to receive from others based on their drivers”), makes it very sensitive for unpredictable systemic events. If somebody stops paying you for whatever reason, then you cannot pay others anymore. And those others can then also not pay others, and so on. Systemic risks thus exists as soon as credit actions or actions with future dependencies are taken.
An added complexity with these systemic dependency, is that time between the outgoing event and the incoming event can be extremely short for the human nature or extremely long for the human needs. Take for instance the flash crashes at stock exchanges, often initiated by some inconsequential event but extrapolated or aggravated by the extremely quick execution of the algorithms with the many algorithmic trading engines that operate at exchanges with high liquidity (leading to so-called “high-frequency trading”). At the other end of the spectrum, too slow also exists: take for instance the credit crisis in 2008 and the effect on some banks – best known is Lehman Brothers: many banks held final
Much of the recent interventions in financial regulation are to control such systemic risks somewhat.
I will try to cover both items (infrastructure, as well as systemic risk management) for the different types of financial activities.