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.

A number of our customers were slightly unclear about the way Basel rules are translated into the European framework. This expert input took it on itself to examine how BCBS standards get translated into European Legislature and what their European counterparts are. To some extent, some confusion is understandable. European lawmakers – after all - make many changes, so much so that the European law is in fact materially non-compliant with Basel standards. This article examines the way in which the main component of the new (but pre December 2019) Basel standards are likely going to differ once implemented in the European Union. More specifically, the article examines the leverage ratio, Net Stable Funding Ratio and FRTB. The adoption of 2017 BCBS document may have rendered parts of this paper obsolete.

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.