3. Risk quantification
Risk quantification comprises determining the probability of default (PD), loss given default (LGD) and risk-adjusted return on capital (RAROC). It provides the basis for pricing and other credit terms.
Commercial lending today is still largely based on a manual process involving in-depth analysis of baseline data and evaluation of soft factors. In such cases, the decision depends, to no small extent, on the loan officer’s experience, which has a relevant influence on the weighting of the various risk items. This is not necessarily a disadvantage: The human factor can be a corrective when it comes to data from manipulated books or unrealistic forecasts regarding sales and growth. On the other hand, this can allow systemic errors to creep in, with a negative impact on the lender’s margin. One study of corporate loans in Portugal concluded that, in 2018 and 2019, especially for longer-term loans, the risk-adjusted return was almost 3 percent too low. Sector-specific differences were also identified. For example, companies in the construction, real estate, transportation and logistics sectors received their loans with conditions that underestimated the actual risk, while the manufacturing, wholesale and retail sectors tended to receive overpriced loans.
Latent mistrust of self-reported borrower data has resulted in a mesh of manual processes and cross-checks that are still in use today, because trust in these methods is higher than in modern technologies. However, scientific studies show this to be ill-founded.
The whitepaper ‘Machine Learning in Credit Risk’ from Banco de España explains that the use of AI or ML algorithms in risk modelling leads to predictions that are up to 20 percent better. One additional benefit of ML models is that they not only calculate the borrower’s ability to repay, but can also evaluate other price-relevant criteria. For example, there are applications that also take behaviour into account, such as observed loyalty towards the institution or price elasticity with regard to cross-selling strategies, in order to find the optimal segmentation. The positive effects for the bank are reflected in reduced losses, better or more favourable capital requirements and lower operating costs.