The contemporary advances in machine learning (ML) may have a profound influence on the risk management procedures, as these methods enable the analysis of very large amounts of data while contributing to an in-depth predictive analysis, and can improve analytical capabilities across risk management and compliance areas. Any market participant whose key activities involve digitalization and application of ML estimation techniques can benefit from:
- greater transparency of risk profiles,
- more targeted risk-based pricing whilst reducing the scope for judgment-based errors,
- faster client service enabled by quick processing of risk profiles and
- more effective management of existing portfolios.
Machine learning may become particularly indispensable in risk management. For instance, as regards the credit risk management, the automation of credit processes and the digitalization of the key steps in the credit value chain are likely to yield significant cost savings. In addition, credit risk costs can be further reduced through integrating new data sources and applying advanced-analytics techniques. These improvements generate richer insights for better risk decisions and ensure more effective and forward-looking credit risk monitoring. A particular contribution, already put into practice, is the use of ML methods for the estimations of crucial inputs in credit risk modelling: Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD), which we can estimate by applying different ML estimation methods depending on the data availability and our preferences. These methods can be particularly relevant in the case of data scarcity, such as risk modelling of low default portfolios (LDPs).
Finalyse is currently exploring multiple ways in which ML can be employed to benefit its customers, particularly to improve efficiency and decrease costs of Credit risk modelling. This article offers a brief non-exhaustive list of the approaches we are currently exploring.
The main three categories of Machine Learning are:
- Supervised – where the presence of the outcome variable is available to guide the learning process.
- Ensemble – where different methods are combined to find the optimal one.
- Unsupervised – where the outcomes are unknown and the algorithm clusters the data to reveal meaningful partitions and hierarchies.
All categories comprise different estimation techniques that are useful in risk management, but, at present, Supervised and Ensemble Machine Learning have wider applications in the field.