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Impact of Artificial Intelligence in the FinancialIndustry: Disruption or Annihilation
Digital transformation has irrevocably redefined the landscape of contemporary business operations, heralding a new era in which DBMI has emerged as a critical strategic lever for competitive survival. Anchored on the dynamic capabilities theory, present study aims to meticulously investigates the influence of digital capability (antecedent) on DBMI and a keen focus on how DBMI, in turn, influences SME competitiveness (outcome). By adopting purposive sampling approach, data were collected from 132 SMEs representatives holding management positions in Malaysia using Google Form. The PLS-SEM analysis was rigorously carried out to verify the proposed hypotheses. The empirical findings illustrate that digital capability positively affects all components of DBMI. Of these, value creation, value proposition, and value capture innovation significantly bolster SME competitiveness, whereas value delivery innovation does not exhibit a similar effect. Considering the scarcity of empirical research on the antecedents and outcomes of DBMI, this study contributes to the theory of dynamic capabilities and improves the current literature on DBMI by providing empirical insights from Malaysian SMEs context. By affirming the role of digital capability and elucidating how DBMI can enhance competitiveness, the findings offer actionable guidance for SMEs navigating the complexities of a volatile business environment. Since this study focuses specifically on Malaysian SMEs within emerging economies, it paves the way for future investigations in developed economies and diverse regional contexts to further validate and expand upon the relationships identified between DBMI antecedents and outcomes.
The market-driven trajectory of current trends in AI combined with emerging technologies and thresholds of performance being crossed create a subset of new and novel risks relating to human bias and cognition at scale. Though the topic of AI Ethics and risk has been discussed increasingly over the past few years, popular talking points and buzzwords have been used adversarially to steer the conversation with increasing success. This has left a subset of risks in the blind spot of most discussions, risks which have now become both urgent and imminent. Automation is actively seeking to replace not only human cognitive bias, but higher human cognition, with a weaker, but fast and scalable version of cognitive bias using stochastic parrots like Large Language Models. These models are, in effect, adversarially trained against humans, generally with the goal of “persuasion”, or manipulation, and each is guided by a corporately curated set of poorly aligned biases. This paper discusses these dynamics and the predictable repercussions of allowing closed information ecosystems to form under the influence of corporately curated and adversarially trained cognitive bias.
Using post-Brexit regulatory freedom to strengthen the financial sector has been seen as a priority for the UK government under the Conservative Party following the 2017 general election. The laying before Parliament of the Financial Services and Markets Bill (July 2022) marked the start of a multi-year process aiming to transform the regulatory framework. Adding a competitiveness and growth secondary objective to regulatory mandates was an important part of the reform agenda set out in the Bill.
This article draws on theory (regulatory competition, regulatory procyclicality, and regulatory design) and recent history (post 2008-crisis regulatory reform) to question the wisdom of reinstating international competitiveness in regulatory mandates in this way. The key concern is that this change risks opening the door to an excessively deregulatory agenda and the undermining of regulatory independence. That the new competitiveness and growth objective will be used to advance deregulatory agenda was already becoming evident during 2022 as the UK debated the adaptation and evolution of EU-derived prudential and capital market laws to suit its new situation.
Since the competitiveness and growth objective bandwagon may have become unstoppable, the article directs attention to the role of accountability in protecting regulatory independence. Regulators need to be able to resist pressure to lower standards unduly, and an effective system of accountability can support them in doing so.
Marketing is changing rapidly with the advancements in AI systems. AI presents several opportunities as gaining insight, hyper-personalization, enhancing the customer experience, providing customers with better service, reducing operational costs, increase efficiency, etc. Artificial intelligence has become a game changer for marketers as it is for financial services providers. Therefore, it is paramount to build a good understanding of AI in the marketing context and mention the fundamentals of AI usage in financial services as AI is introduced as a significant competitive edge factor in financial marketing in recent years. This study aims to explore AI and marketing in a theoretical base and aims to explain the issue with a holistic approach.
Work as we know it has changed with the emergence of the Fourth Industrial Revolution (4IR). Prior
to the advent of the 41R, skill gaps have been widening. Though the 4IR comes with bright projections,
it also will predict the way things are done and the requisite skills to have. Clearly, not everyone will
be up to date with the needed skills in the future work environment. Intricate underlying factors about
the future of work have fuelled debates bothering on the issues of digitization on work and professions,
artificial intelligence (AI), and the uncertainty of work and careers left after automation. The changing
patterns of work and work arrangements, the gig economy, as well as fissured jobs (involving employment
as outsourced services) are of great concern. Also rife are matters around work-skill misalignment
expected in the 4IR as the presentation skills that employees have may not be relevant in the new world
of work. Therefore, skill obsolescence and the need to reskill and/or upskill is expected.
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of Artificial Intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on Explainable Artificial Intelligence. A research field that holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to explainable AI. Through the lens of literature, we review existing approaches regarding the topic, we discuss trends surrounding its sphere and we present major research trajectories.
This article reviews the reasons scholars hold that driverless cars and
many other AI equipped machines must be able to make ethical decisions, and the
difficulties this approach faces. It then shows that cars have no moral agency, and
that the term ‘autonomous’, commonly applied to these machines, is misleading,
and leads to invalid conclusions about the ways these machines can be kept ethical.
The article’s most important claim is that a significant part of the challenge posed by
AI-equipped machines can be addressed by the kind of ethical choices made by
human beings for millennia. Ergo, there is little need to teach machines ethics even
if this could be done in the first place. Finally, the article points out that it is a
grievous error to draw on extreme outlier scenarios—such as the Trolley narratives—as
a basis for conceptualizing the ethical issues at hand.
Because increasing a bank's capital requirement to improve the stability of the financial system imposes costs upon the bank, a regulator should ideally be able to prove beyond a reasonable doubt that banks classified as systemically risky really do create systemic risk before subjecting them to this capital punishment. Evaluating the performance of two leading systemic risk models, we show that estimation error alone prevents the reliable identification of the most systemically risky banks. We conclude that it will be a considerable challenge to develop a riskometer that is sound and reliable enough to provide an adequate foundation for macroprudential policy.
Because increasing a bank's capital requirement to improve the stability of the financial system imposes costs upon the bank, a regulator should ideally be able to prove beyond a reasonable doubt that banks classified as systemically risky really do create systemic risk before subjecting them to this capital punishment. Evaluating the performance of two leading systemic risk models, we show that estimation error alone prevents the reliable identification of the most systemically risky banks. We conclude that it will be a considerable challenge to develop a riskometer that is sound and reliable enough to provide an adequate foundation for macroprudential policy.
Fraud exists in all walks of life and detecting and preventing fraud represents an important research question relevant to many stakeholders in society. With the rise in big data and artificial intelligence, new opportunities have arisen in using advanced machine learning models to detect fraud. This chapter provides a comprehensive overview of the challenges in detecting fraud using machine learning. We use a framework (data, method, and evaluation criterion) to review some of the practical considerations that may affect the implementation of machine-learning models to predict fraud. Then, we review select papers in the academic literature across different disciplines that can help address some of the fraud detection challenges. Finally, we suggest promising future directions for this line of research. As accounting fraud constitutes an important class of fraud, we will discuss all of these issues within the context of accounting fraud detection.
The use of Artificial Intelligence (AI) in Digital technologies (DT) is proliferating a profound socio-technical transformation. Governments and AI scholarship have endorsed key AI principles but lack direction at the implementation level. Through a systematic literature review of 59 papers, this paper contributes to the critical debate on the ethical use of AI in DTs beyond high-level AI principles. To our knowledge, this is the first paper that identifies 14 digital ethics implications for the use of AI in seven DT archetypes using a novel ontological framework (physical, cognitive, information, and governance). The paper presents key findings of the review and a conceptual model with twelve propositions highlighting the impact of digital ethics implications on societal impact, as moderated by DT archetypes and mediated by organisational impact. The implications of intelligibility, accountability, fairness, and autonomy (under the cognitive domain), and privacy (under the information domain) are the most widely discussed in our sample. Furthermore, ethical implications related to the governance domain are shown to be generally applicable for most DT archetypes. Implications under the physical domain are less prominent when it comes to AI diffusion with one exception (safety). The key findings and resulting conceptual model have academic and professional implications.
Artificial Intelligence in Finance
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Economics, fairness and algorithmicbias
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