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The current discussion about a “risk culture” in financial services was triggered by the recent series of financial crises. The last decade saw a long list of hubris, misconduct and criminal activities by human beings on a single or even a collective basis in banks, in the industry or in the whole economy. As a counter-reaction, financial authorities called for a guidance by a “new” risk culture in financial institutions based on a set of abstract, formal, and normative governance processes. While traditional risk research in economics and in banking was focused on the statistical aspects of risk as the probability of loss multiplied by the amount of loss, culture is a paraphrase for the behavior in collectives and dynamics of organization found in human societies. Therefore, a “risk culture” should link the normative concepts of risk with the positive “real-world” decision-making in financial services. This paper will describe a novel view on “risk culture” from the perspective of human beings interacting in dynamical and intertemporal commercial relations. In this context “risk” is perceived by economic agents ex−ante as the consequence of the time lag between the present and the uncertain future development (compared to a probability distribution calculated by observers ex−post). For all those individual decisions—to be made under uncertainty—future “risk” includes the so-called “normal accidents”, i.e., failures that will happen at some uncertain point in time but are inevitable, and the only questions are when failure will happen and how to maintain function in the first line of defense. Finally, the shift from an abstract definition of “risk” as a probability distribution to a role model of “honorable merchants” as a benchmark for significant individual decision-making with individual responsibilities for the uncertain future outcome provides a new framework to discuss the responsibilities in the financial industry.
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the “unknown unknown.” While machine learning has been tested successfully in the regime of the “known,” heuristics typically provide better results for an active operational risk management (in the sense of forecasting). However, precursors in existing data can open a chance for machine learning to provide early warnings even for the regime of the “unknown unknown.”