Banks and regulators grapple with ‘XAI’ challenge
A forum of industry leaders discusses how banks will define individual trading desks under FRTB, whether BCBS 239 compliance projects can help banks meet FRTB risk data challenges, which model validation obstacles banks still face and other key topics
Limited pool of talent hindering expansion of sophisticated strategies across buy and sell side
The revolution in artificial intelligence promises new leads in banks’ fight against dirty money. Alexander Campbell of Risk.net hosted a live online forum, in association with NICE Actimize, to investigate the applications of this emergent technology
In this paper, the authors derive an analytical solution for sub-SCR VTs starting with a model risk appetite (MRA) that defines acceptable errors for an insurer’s total SCR.
Federal Reserve warns EU delay would force US to reconsider 2022 implementation
The objective of this paper is to select effective risk indicators and thus establish a risk index system of P2P platforms so as to evaluate the risk performance of these platforms in China.
Models such as those used for IFRS 9, CECL or CCAR are prone to errors, and should be accounted for
Recent guidance on stress-test models could be expanded, says BoE exec
Risk managers want ‘transparency and clarity’ around AI-based models
This paper reviews the ways of measuring the performance of LGD models that have been previously used in the literature and also suggests some new measures.
In this paper, the importance of the empirical bootstrap (EB) in assessing minimal operational risk capital is discussed, and an alternative way of estimating minimal operational risk capital using a central limit theorem (CLT) formulation is presented.
Theis leaves role as head of market models at Standard Chartered to join German bank
ML models benchmarked against traditional iterations to avoid ‘black box’ perception
Banks have revised Basel’s model to suit their risk profile, but some remain sceptical of its impact on risk culture
CCAR cycle frustrates compliance with Fed model risk guidance
Machine learning being used to build challenger models for model validation
This paper considers the empirical evaluation of a collective risk model with the geometric as the primary distribution and the exponential as the secondary distribution.
The author introduces the triangular approximation to the normal distribution in order to extract closed- and semi-closed-form solutions that are useful in risk measurement calculations.
This paper proposes a qualitative method to assess the maturity of model risk management practices within banks.
Banks acknowledge they “cannot hide behind a complex tool” to assess interconnectedness
US regulators ask banks to assess cross-dependencies of models – prompting some to employ network theory