Basel III and Solvency II reporting requirements are, in spirit, oriented towards behavioural explanations of demand for funding capital. By definition, behavioural information within a financial institution must be sourced from operational systems. Given this, Basel III and Solvency II reporting are fundamentally a Business Intelligence and Information Management challenge. In the longer term it seems the regulatory outlook is for more and continuing ‘historicisation’ of operating numbers to support Predictive Business intelligence. So the data collection and specification process is intrinsically valuable to the success of any Regulatory or Supervisory Compliance project.
Precisise estimation of the institutional exposure to capital buffering is fundamental. Thus every quantitative tool you have at your disposal will need to be deployed to achieve this objective. The key source of risk is in the Product Hierarchy, in what can be referred to as “The Tariff Engine”1 (or as close to that concept as one can get in financial services). That is where the buck stops, since it is in the tariff engine that Risk Based Pricing can be expedited.
Product risk management in particular presents its own challenges stemming from not only the overwhelming amount of information, but also the requirement for appropriate models to be built that accurately represent the risk involved in a sufficiently disaggregated and fully delineated Product “hierarchy”. (In reality, the process is more complex than that and we expand upon this below). The model is the basis of the assurance to
Executive Management that the financial institution is operating at sufficient margin to be able to contribute to supervisory capital (above all the other calls on operating revenue from normal operations).
There are significant challenges involved in understanding the required computation process and organizing appropriate computer systems. Meaningful calculation of risk together with useful reporting of the results is essential.
We show how tools such as the R language and Business Intelligence suites can help you generate the information you need quickly and effectively. We introduce the process of data modelling, an essential step in processing the vast quantities of data involved in your portfolio. Then we provide a brief summary of the algorithmic elements of risk calculation which sit at the heart of the information management framework, outlined here as a solution blueprint to the increasing challenges of supervision in financial services.