This expert input focuses on the validation of Expected Credit loss model validation; more specifically, it explains why it is a good idea now, after the scramble to have IFRS 9 compliant models in time, to consider validation. This input addresses the challenges of a methodological review of all models, and more specifically, it addresses the review of selected variables – macroeconomic factors, obligor characteristics, etc. It shows how to make the best use of the new ability to compare the outcomes of the models against the observed losses. In addition, this article tackles another challenge: the review of data quality – particularly of the modelling data set and new data.

This Expert input concerns itself with the valuation of Private Equity, an intriguingly difficult topic, but also one well worth exploring, considering just how large a portion of the equity takes the form of a private equity. It shows why the private equity is an interesting prospect for investors and in what way the independence of the valuation is desirable and demanded by regulators. It also depicts how investing in private equity is often conducted and what such an approach entails for the investor. Lastly, it depicts various techniques used to estimate a private equity value and how the preferred technique is chosen depending on the information available.

This Expert input works as an overview of the basis of most available machine learning techniques and serves as a great stepping stone to get into the intricate world of ML. Whilst mostly written with credit risk in mind – offering some advice for the use of machine learning to help us model Expected credit loss and its components (PD, LGD, EAD), the list of the potential techniques depicted steps far beyond this relatively singular use and examines a multitude of approaches, ranging from Supervised ML (Decision trees, Artificial Neural networks, etc.) through ensemble ML (Random forests, Gradient boosting), to unsupervised ML (Deep learning, Clustering methods, etc.). The input concludes with several general tips and tricks regarding machine learning.

This Expert input gives a basic overview of IFRS 17 and how it is going to be different from the current standards (IFRS 4). We note that there are some similarities between some of the aspects of Solvency II and IFRS standards and examine them, as well as the differences. Furthermore, there are predictions on how the standards are going to influence the operations of insurers and their contracts. Since the insurers are likely to implement IFRS 17 in concert with IFRS 9, the expert input also focuses on how this joint implementation is likely to proceed, what the main challenges are and why it is ultimately a good decision to implement them together.

This expert input addresses PSD 2 and the possibly underappreciated impact it could have on (and arguably beyond) financial services, opening many hitherto closed doors. It looks at the regulatory progress all the way from PSD 1 to PSD 2. It notes that the development of the Single Euro Payments Area – SEPA – would not have been possible without PSD 1, and an analogous paradigm shift may be expected with PSD 2. The article notes that PSD 2 may lead to the emergence of new and a separation of old services in the hands of innovative FinTech companies, though the established players still hold a very strong position to compete, if only they adjust to the new reality.