With some effort, machine learning models could improve the IRB models whilst meeting all prudential standards. The following are some examples of areas where machine learning models could be useful:
- Improving risk differentiation through improving model discriminatory power and offering valuable tools for identifying all key risk drivers and their relationships. ML models could also be used to improve portfolio segmentation, construct robust models across geographical and industrial sectors/products, and make data-driven decisions that balance data availability with model granularity requirements. Furthermore, machine learning models may aid in the confirmation of data features chosen by expert judgment in 'conventional' model creation, providing a data-driven viewpoint on the feature selection process.
- Improving risk quantification by increasing model predictive power and recognising material biases, as well as offering tools for identifying recovery patterns in LGD models. ML models could potentially aid in the calculation of the necessary modifications.
- Improving data collecting and preparation processes, such as input data cleansing, data treatment and data quality checks. Through unsupervised learning approaches, ML models could be effective for analysing representativeness. Furthermore, machine learning models could be used to discover outliers and correct errors. In addition, unstructured data (e.g. qualitative data like business reports) could be employed with ML models, expanding the data sets that can be used for parameter estimation.
- Improving credit risk mitigation approaches, including the use of machine learning algorithms for collateral valuation through haircut models.
- Providing reliable systems for model validation and monitoring where model challengers or a supporting analysis for alternative assumptions or techniques could be generated using machine learning models.