Modern banking has become a technical field, and getting more so. Big data, automated decision-making and sophisticated statistical models are common themes in the industry. Credit risk management is at the forefront of this trend, bringing banks’ long tradition of prudency and conservatism to the era of data analytics and modern technology.
A review of internal models cannot be thought of as a panacea of an internal models process. Especially since it is such a complicated process of interactions between technology, data and experts. Instead the review of internal models should act as a catalyst to bring change to internal models for the financial health of the enterprise.It is enough to mention IRB, IFRS9, and Stress Testing to paint a picture of how diverse and complex a credit risk modelling landscape is in today’s bank. In order to fulfill their purpose, these models must be correct, accurate and work in unison. What happens if they are not? Then they produce excessive estimation errors. Because major business decisions rely on the numbers produced by those models, a bank is likely to misjudge its risks, misprice its products, misestimate the expected loss and required capital. If so, such mistakes lead to a loss of the bank’s competitive power, take a toll on its profits and increase a chance of bankruptcy. While it is commonly perceived that regulatory scrutiny is guaranteed to keep bankruptcy as distant as it is supposed to be, bank’s profits and market position are its own concern. Validation is there, in particular, to make sure that model-induced errors are minimized, so that the bank’s profitability and business performance do not suffer. It is still a large-scope mandatory exercise, but it is important to remember that it is in the bank’s own interest to have solid and reliable internal models.