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Financial Risk Management and Explainable, Trustworthy, Responsible AI

This perspective paper is based on several sessions by the members of the Round Table AI at FIRM, 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...

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Autores principales: Fritz-Morgenthal, Sebastian, Hein, Bernhard, Papenbrock, Jochen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919207/
https://www.ncbi.nlm.nih.gov/pubmed/35295866
http://dx.doi.org/10.3389/frai.2022.779799
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author Fritz-Morgenthal, Sebastian
Hein, Bernhard
Papenbrock, Jochen
author_facet Fritz-Morgenthal, Sebastian
Hein, Bernhard
Papenbrock, Jochen
author_sort Fritz-Morgenthal, Sebastian
collection PubMed
description This perspective paper is based on several sessions by the members of the Round Table AI at FIRM, 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.
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spelling pubmed-89192072022-03-15 Financial Risk Management and Explainable, Trustworthy, Responsible AI Fritz-Morgenthal, Sebastian Hein, Bernhard Papenbrock, Jochen Front Artif Intell Artificial Intelligence This perspective paper is based on several sessions by the members of the Round Table AI at FIRM, 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. Frontiers Media S.A. 2022-02-28 /pmc/articles/PMC8919207/ /pubmed/35295866 http://dx.doi.org/10.3389/frai.2022.779799 Text en Copyright © 2022 Fritz-Morgenthal, Hein and Papenbrock. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Fritz-Morgenthal, Sebastian
Hein, Bernhard
Papenbrock, Jochen
Financial Risk Management and Explainable, Trustworthy, Responsible AI
title Financial Risk Management and Explainable, Trustworthy, Responsible AI
title_full Financial Risk Management and Explainable, Trustworthy, Responsible AI
title_fullStr Financial Risk Management and Explainable, Trustworthy, Responsible AI
title_full_unstemmed Financial Risk Management and Explainable, Trustworthy, Responsible AI
title_short Financial Risk Management and Explainable, Trustworthy, Responsible AI
title_sort financial risk management and explainable, trustworthy, responsible ai
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919207/
https://www.ncbi.nlm.nih.gov/pubmed/35295866
http://dx.doi.org/10.3389/frai.2022.779799
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