If you work in almost any white-collar industry today, keeping up with artificial intelligence has become a strange daily routine. You do not need to be a software engineer or a tech specialist to feel it. Every morning, standard office workers, analysts, managers, and creators open their laptops to face a continuous stream of updates, new model announcements, and endless video tutorials claiming that the tools they settled into six months ago are already outdated. Trying to

This notebook compares the risk weight the two approaches produce for the same exposure, as its PD varies. The FIRB weight moves with the PD; the STD weight does not, so it stands as a fixed level for each credit quality step. Plotting them together shows which approach is the more conservative, and where. It is the last post in the series, and the one that puts a number on a question the fifteen scenarios could only answer anecdotally. Across the worked examples Foundation

The maturity factor scales the risk weight up as an exposure runs for longer. A loan that matures in five years ties up capital, and stays exposed to a downturn, for far longer than one that matures in a year, so the formula charges more for it. The factor is what applies that charge. It has two drivers. The first is , the exposure maturity in years, which the earlier notebooks derived from the cash-flow schedule and can run from 1 to 5 years. The second is , the maturity a