Banking 4.0: The era of artificial intelligence‐based fintech

Kumar, Aishwarya; Srivastava, Ankita; Gupta, Puneet Kumar

doi:10.1002/jsc.2526

Cited by 13 publications

(5 citation statements)

References 44 publications

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“…The global spending on AI in the financial industry exemplified the rapid technological growth, reaching $12 billion by 2020 [Kumar, 2022]. This exponential increase reflected the industry's recognition of AI's transformative potential.…”

Section: Technological Growthmentioning

confidence: 99%

Advancements of AI and Machine Learning in FinTech Industry (2016-2020)

Kamuangu

2024

JEFAS

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The confluence of Artificial Intelligence (AI) and Machine Learning (ML) with the Financial Technology (FinTech) sector has ushered in a paradigm shift, fundamentally altering the contours of financial services. This scholarly endeavor undertakes a meticulous scrutiny of the evolutionary trajectory of AI and ML within the FinTech domain spanning the pivotal period of 2016 to 2020. Inextricably interwoven with notions of efficiency, security, and innovation, this exploration traverses the realms of operational processes, anti-fraud mechanisms, the bespoke landscape of personalized financial services, and the overarching influence on financial institutions. The canvas of this inquiry unfurls its historical panorama by anchoring in the pre-2016 epoch, elucidating the nascent manifestations of AI applications in finance. A discerning lens is cast upon pivotal technologies and algorithms that formed the bedrock of subsequent advancements. The narrative then unfurls to encapsulate the ascendancy of predictive analytics, the assimilation of both supervised and unsupervised learning paradigms, and the nuanced integration of Natural Language Processing (NLP) in the discerning analysis of financial data. Venturing into the substantive body of discourse, the examination scrutinizes specific strides, notably the assimilation of Robotic Process Automation (RPA) for the augmentation of operational efficiency. A close inspection follows the evolutionary trajectory of AI-driven algorithms tailored for the prophylaxis of fraud, fortifying the bulwarks against malfeasance within the financial ecosystem. Furthermore, the intricate tapestry of personalized financial services unfolds through the prism of recommendation systems, showcasing a nuanced blend of tailored financial offerings.

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Section: Technological Growthmentioning

confidence: 99%

Advancements of AI and Machine Learning in FinTech Industry (2016-2020)

Kamuangu

2024

JEFAS

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“…Credit shocks also influence the digital financial inclusion, as digital transformation is more substantial if the banking sector is stable (Sodokin et al, 2022). At the same time, digitalization also influences the nature of credit trends, as AI use and the occurrence of FinTech and BigTech firms change the financial landscape, and these changes will be more substantial in the future (Kumar et al, 2022). The analysis of credit dynamics can be extended even to such novel areas as crowdlending (Krishnan et al, 2021).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Accelerate or brake?—The connection of credit gaps and economic disequilibrium

Fazekas,

Póta,

Becsky‐Nagy

2024

Strategic Change

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Credit supply shocks significantly aggravate the impact of economic recessions. Understanding the underlying reasons why credit market shocks occur and detecting the market turbulences in time can give a decision support tool for the economic policy to intervene more efficiently on the market and to reduce the effect of economic downturns. The goal of the current article is to investigate the factors that signal possible credit shocks by analyzing the quarterly data of 17 European countries over the period 1995–2021. The focus of the article is on the credit given to the nonfinancial corporations. In our analysis, we have built on the credit gap methodology by determining the deviations of the lending activity from its trend, and then we have modeled these credit gaps using fundamental macroeconomic indicators, such as the balance of the current account, the ratio of short‐ and long‐term capital investments and government debt. Our conclusion is that in the presence of fundamental disequilibrium in the economy excessive lending creates positive credit gaps that increase the chance of negative credit market shocks. According to our new findings, the existing credit gap methodology can be improved by incorporating real economic factors; slowing long‐term capital investment and increasing the deficit of the current account signal the emergence of a credit gap. We found significant regional heterogeneity as in Southern European countries, and the credit gaps were more substantial. Among the investigated macroeconomic factors, the governmental indebtedness was especially high in these countries suggesting that countries with high public debt rates are more prone to develop credit gaps.

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“…If there is a technical failure or a hacking attack, it can lead to the collapse of the financial system and the risk of data leakage. [ 39 , 40 ] For example, in 2017, the US credit rating agency Equifax was hacked, resulting in the leakage of 143 million personal information, which could more severely affect financial institutions and individual privacy. Since the application of AI models requires a large amount of data storage and computing resources, financial institutions need to invest a lot of capital to support their construction and maintenance.…”

Section: Potential Hazards Of Artificial Intelligence In Financementioning

confidence: 99%

Ethical perspective on AI hazards to humans: A review

Chen,

Luo

et al. 2023

Medicine

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This article explores the potential ethical hazards of artificial intelligence (AI) on society from an ethical perspective. We introduce the development and application of AI, emphasizing its potential benefits and possible negative impacts. We particularly examine the application of AI in the medical field and related ethical and legal issues, and analyze potential hazards that may exist in other areas of application, such as autonomous driving, finance, and security. Finally, we offer recommendations to help policymakers, technology companies, and society as a whole address the potential hazards of AI. These recommendations include strengthening regulation and supervision of AI, increasing public understanding and awareness of AI, and actively exploring how to use the advantages of AI to achieve a more just, equal, and sustainable social development. Only by actively exploring the advantages of AI while avoiding its negative impacts can we better respond to future challenges.

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Banking 4.0: The era of artificial intelligence‐based fintech

Cited by 13 publications

References 44 publications

Advancements of AI and Machine Learning in FinTech Industry (2016-2020)

Advancements of AI and Machine Learning in FinTech Industry (2016-2020)

Accelerate or brake?—The connection of credit gaps and economic disequilibrium

Ethical perspective on AI hazards to humans: A review

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