Artificial intelligence and systemic risk

Danı́elsson, Jón; Macrae, Robert; Uthemann, Andreas

doi:10.1016/j.jbankfin.2021.106290

Cited by 30 publications

(12 citation statements)

References 20 publications

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“…Effective tax rate is the effective tax rate that is expected to remain low so that the plantation company pays a low amount of corporate tax (Daníelsson et al, 2022). In this study, the effective tax rate has a partial relationship to Tax Avoidance (Johannesen, 2014).…”

Section: Resultsmentioning

confidence: 88%

Study Literature Review of the Rise of Tax Avoidance Practices

Hasibuan¹,

Muda²,

Kesuma³

2023

IJSSR

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This study aims to find out how many companies are trying to avoid taxes. This research was conducted by observing a number of papers, namely by documenting various articles related to tax avoidance and then adapting them according to several categories until finally there were 313 articles that could be used. Initially there were 644 articles, then adapted based on the selection of abstracts and several inappropriate criteria so that the number became 404 articles, and then read again until finally there were 313 articles that could be examined. In this study, the fact is that many companies are trying to avoid taxes, utilizing the services of tax consultants in order to reduce the amount of corporate tax that must be paid. In addition, the company always tries to use a variety of very subtle techniques so that the tax authorities cannot find out, there is a very significant difference in relation to carrying out tax evasion by taking advantage of loopholes in the law compared to carrying out illegal tax evasion and this will certainly lead to new problem for the company. Thus, the urgency of paying taxes will always be supervised and monitored by the government due to certain targets in an effort to increase the realization of tax revenues every year.

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Section: Resultsmentioning

confidence: 88%

Study Literature Review of the Rise of Tax Avoidance Practices

Hasibuan¹,

Muda²,

Kesuma³

2023

IJSSR

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“…AI models work in infinitely complex environments and can have unexpected behaviours or nodes of information fed to them. AI models with fixed objectives can run into cases where they take critical decisions like no human would [45]. Humans can adjust their objectives in light of complex factors that may emanate from social, legal, political and environmental ecosystems.…”

Section: Ai In Prudential Bankingmentioning

confidence: 99%

Legal implications of automated suspicious transaction monitoring: enhancing integrity of AI

Turksen,

Benson,

Adamyk

2024

J Bank Regul

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The fast-paced advances of technology, including artificial intelligence (AI) and machine learning (ML), continue to create new opportunities for banks and other financial institutions. This study reveals the barriers to trust in AI by prudential banking supervisors (compliance with regulations). We conducted a qualitative study on the drivers for adoption of explainability technologies that increase transparency and understanding of complex algorithms (some of the underpinning legal principles in the proposed EU AI Act). By using human-centred and ethics-by-design methods coupled with interviews of the key stakeholders from Eastern European private and public banks and IT AI/ML developers, this research has identified the key challenges concerning the employment of AI algorithms. The results indicate a conflicting view of AI barriers whilst revealing the importance of AI/ML systems in banks, the growing willingness of banks to use such systems more widely, and the problematic aspects of implementing AI/ML systems related to their cost and economic efficiency. Keeping up with the complex regulation requirements comes at a significant cost to banks and financial firms. The focus of the empirical study, stakeholders in Ukraine, Estonia and Poland, was chosen because of the fact that there has been a sharp increase in the adoption of AI/ML models in this jurisdiction in the context of its war with Russia and the ensuing sanctions regime. While the “leapfrogging” AI/ML paths in each bank surveyed had its own drivers and challenges, these insights provide lessons for banks in other European jurisdictions. The analysis of four criminal cases brought against top banks and conclusions of the study indicate that the increase in predicate crimes for money laundering, constantly evolving sanctions regime along with the enhanced scrutiny and enforcement action against banks are hindering technology innovation and legal implications of using AI driven tools for compliance.

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“…The increasingly large accounting and finance datasets and the need for textual analysis have made machine learning (ML) a popular tool for empirical research. ML has been applied to predict misstatements in financial statements (Bertomeu et al, 2021), banking crises from excessive credit growth and aggregate leverage (Alessi & Detken, 2018), the percentage of assets lost if a borrower defaults or the loss given a default (Kellner et al, 2022) and the destabilising effects of systematic risk using artificial intelligence (Danielsson et al, 2022). There is scanty use of ML in empirical research on REITs; for example, Carstens and Freybote (2021) suggest that a forward-looking tone, which refers to a tone not explained by the past performance of a REIT, predicts total returns in the next quarter of REIT financial statements when real estate markets perform poorly.…”

Section: R Ev I Ew Of R Eleva N T Li T Er At U R Ementioning

confidence: 99%

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

Ngene

Wang

2023

Accounting & Finance

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We evaluate asset returns and volatility connectedness using the time‐frequency connectedness model and machine learning approaches. Using 48 years of monthly indices of equity real estate investment trusts (EREITs), mortgage real estate investment trusts (MREITs), stocks, commodities, and bonds, we find that shocks to EREIT, and MREIT returns have a transitory impact on other assets. However, asset volatility connectedness among assets occurs at lower frequencies as markets slowly process pricing information. Therefore, shocks to EREIT and MREIT decay gradually and spill over to the other three assets for long periods. We also find that the intensity of the time‐frequency connectedness of returns and volatility varies with business cycles and significant domestic and global non‐recession events. We attribute the dominance of real estate investment trusts (REITs) in destabilising the financial system through long‐term volatility transmission to the heavy linkage of REITs to highly illiquid underlying direct real estate markets and high REITs leverage, making them more sensitive to real estate fundamentals, monetary shocks and macroeconomic risks than stocks and bonds. The result of the algorithm‐based machine learning model broadly supports the dominance of EREITs in the transmission of returns and volatility and shows that commodities have more explanatory power in the transmission of volatility than in returns. The empirical findings have implications for strategic and tactical asset allocation and policy design for market stability.

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Artificial intelligence and systemic risk

Cited by 30 publications

References 20 publications

Study Literature Review of the Rise of Tax Avoidance Practices

Study Literature Review of the Rise of Tax Avoidance Practices

Legal implications of automated suspicious transaction monitoring: enhancing integrity of AI

Transitory and permanent shock transmissions between real estate investment trusts and other assets: Evidence from time‐frequency decomposition and machine learning

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