Abstract
This perspective paper is based on several sessions by the members of the Round Table AI at FIRM1, with input from a number of external and international speakers. Its particular focus lies on the management of the model risk of productive models in banks and other financial institutions. The models in view range from simple rules-based approaches to Artificial Intelligence (AI) or Machine learning (ML) models with a high level of sophistication. The typical applications of those models are related to predictions and decision making around the value chain of credit risk (including accounting side under IFRS9 or related national GAAP approaches), insurance risk or other financial risk types. We expect more models of higher complexity in the space of anti-money laundering, fraud detection and transaction monitoring as well as a rise of AI/ML models as alternatives to current methods in solving some of the more intricate stochastic differential equations needed for the pricing and/or valuation of derivatives. The same type of model is also successful in areas unrelated to risk management, such as sales optimization, customer lifetime value considerations, robo-advisory, and other fields of applications. The paper refers to recent related publications from central banks, financial supervisors and regulators as well as other relevant sources and working groups. It aims to give practical advice for establishing a risk-based governance and testing framework for the mentioned model types and discusses the use of recent technologies, approaches, and platforms to support the establishment of responsible, trustworthy, explainable, auditable, and manageable AI/ML in production. In view of the recent EU publication on AI, also referred to as the EU Artificial Intelligence Act (AIA), we also see a certain added value for this paper as an instigator of further thinking outside of the financial services sector, in particular where “High Risk” models according to the mentioned EU consultation are concerned.
Highlights
The European Commission is proposing one of the first laws globally to regulate the use of artificial intelligence
Artificial Intelligence Act (AIA) is a cross-sectoral regulation of Artificial Intelligence (AI), which addresses governance requirements around so-called high-risk AI systems, and which more generally recommends the adoption of principles in the spirit of creating trustworthy AI
Pursuant to the requirements of the AIA and existing supervisory expectations, those pursuing an AI-first bank/strategy must be equipped with suitable risk management as well as suitable infrastructure and technology (RiskTech, TrustTech, Algo Audit, Regulatory Sandbox)
Summary
The European Commission is proposing one of the first laws globally to regulate the use of artificial intelligence. Federated learning enables multiple entities who do not trust each other (fully), to collaborate in training a Machine Learning (ML) model on their combined dataset; without sharing data Another methodological area is the AI-based generation of synthetic data with a wide range of applications, ranging from shortening cycles for quantitative risk modeling, minimizing the effort to comply with regulation, to accelerating development and testing processes. The model training quality and its interpretability can be amplified by artificially creating future market data scenarios that have never been observed before but carry the statistical footprints of financial market behavior An example of such a procedure is given in Papenbrock et al (2021) who use an alternative approach to GAN to produce correlated market data. The project’s foundations had been laid out in the European Union’s Horizon 2020 research and innovation program called FIN-TECH11
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