Banking on AI: mandating a proactive approach to AI regulation in the financial sector

Truby, Jon; Brown, Rafael Dean; Dahdal, Andrew

doi:10.1080/17521440.2020.1760454

Cited by 84 publications

(47 citation statements)

References 1 publication

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“…Although AI brings many benefits, increasingly sophisticated technologies also increase the potential for abuse. Financial institutions are concerned with data ownership, consumer privacy and cybersecurity (Truby et al, 2020). Regulation is needed to address these concerns, as uncontrolled innovation can have devastating consequences (Accenture, 2019).…”

Section: Contextmentioning

confidence: 99%

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Hentzen

Hoffmann

Dolan

et al. 2021

IJBM

101

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PurposeThe objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.Design/methodology/approachCombining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.FindingsThe results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.Research limitations/implicationsThe review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.Originality/valueThe authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

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Section: Contextmentioning

confidence: 99%

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Hentzen

Hoffmann

Dolan

et al. 2021

IJBM

101

View full text Add to dashboard Cite

show abstract

“…On the one hand, the prospects of AI in the economy domain include: enhancing productivity and innovation, reducing costs and increasing resources, supporting the decision-making process, automating decision-making [145][146][147]. On the other hand, the constraints of AI involve: making biased decisions, having an unstable job market, losing revenue streams and employment, and generating economic inequality [148][149][150].…”

mentioning

confidence: 99%

The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

2020

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The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics the behaviors commonly associated with human intelligence. Today, various AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. More recently, AI applications have also started to become an integral part of many urban services. Urban artificial intelligences manage the transport systems of cities, run restaurants and shops where every day urbanity is expressed, repair urban infrastructure, and govern multiple urban domains such as traffic, air quality monitoring, garbage collection, and energy. In the age of uncertainty and complexity that is upon us, the increasing adoption of AI is expected to continue, and so its impact on the sustainability of our cities. This viewpoint explores and questions the sustainability of AI from the lens of smart and sustainable cities, and generates insights into emerging urban artificial intelligences and the potential symbiosis between AI and a smart and sustainable urbanism. In terms of methodology, this viewpoint deploys a thorough review of the current status of AI and smart and sustainable cities literature, research, developments, trends, and applications. In so doing, it contributes to existing academic debates in the fields of smart and sustainable cities and AI. In addition, by shedding light on the uptake of AI in cities, the viewpoint seeks to help urban policymakers, planners, and citizens make informed decisions about a sustainable adoption of AI.

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“…The regulation of AI must be sector-specific (Nitzberg and Zysman 2021 ). For example, there might be one approach to AI regulation in the financial sector (Truby et al 2020 ) and another approach to AI in healthcare (Sharma and Manchikanti 2020 ). However, some policy features might span over multiple sectors, such as for regulation to be proactive and responsive.…”

Section: Ai and Public Policymentioning

confidence: 99%

AI under great uncertainty: implications and decision strategies for public policy

Nordström

2021

AI & Soc

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Decisions where there is not enough information for a well-informed decision due to unidentified consequences, options, or undetermined demarcation of the decision problem are called decisions under great uncertainty. This paper argues that public policy decisions on how and if to implement decision-making processes based on machine learning and AI for public use are such decisions. Decisions on public policy on AI are uncertain due to three features specific to the current landscape of AI, namely (i) the vagueness of the definition of AI, (ii) uncertain outcomes of AI implementations and (iii) pacing problems. Given that many potential applications of AI in the public sector concern functions central to the public sphere, decisions on the implementation of such applications are particularly sensitive. Therefore, it is suggested that public policy-makers and decision-makers in the public sector can adopt strategies from the argumentative approach in decision theory to mitigate the established great uncertainty. In particular, the notions of framing and temporal strategies are considered.

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Banking on AI: mandating a proactive approach to AI regulation in the financial sector

Cited by 84 publications

References 1 publication

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

AI under great uncertainty: implications and decision strategies for public policy

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