Financial Risk Management and Explainable, Trustworthy, Responsible AI

Author:

Fritz-Morgenthal Sebastian,Hein Bernhard,Papenbrock Jochen

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.

Publisher

Frontiers Media SA

Subject

General Medicine

Reference17 articles.

1. 2020

2. A survey on artificial intelligence assurance;Batarseh;J. Big Data,2021

3. BrunoG. HirenJ. RafaelS. BrunoT. 2020

4. Explainable machine learning in credit risk management;Bussmann;Comput. Econ.,2020

Cited by 29 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Credit Risk Analysis using Explainable Artificial Intelligence;Journal of Soft Computing Paradigm;2024-09

2. Multidimensional Financial Metrics for Corporate Financial Risk Assessment and Early Warning Mechanisms;Journal of Organizational and End User Computing;2024-08-05

3. The Strategic Role of Artificial Intelligence (AI) in Service Delivery Systems;Advances in Hospitality, Tourism, and the Services Industry;2024-07-26

4. Analyzing Trustworthiness and Explainability in Artificial Intelligence: A Comprehensive Review;Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering);2024-07-04

5. ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL MANAGEMENT;ShodhKosh: Journal of Visual and Performing Arts;2024-06-30

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3