Abstract

To what extent does the collapse of a commercial bank spread contagion across cryptocurrency markets? How do markets behave around bankruptcy if digital assets remain stuck within the bank and cannot be withdrawn? We use a BEKK model to examine contagion effects across major digital assets during the Silicon Valley Bank (SVB) collapse period in early March 2023. We find evidence of contagion across major stablecoins and Bitcoin. We also examine the price action when nearly all withdrawals at SVB were prohibited. We find substantial abnormal movements in stablecoin cumulative returns and traded volumes, indicating a “flight to safety” from less to more authoritative and trusted stablecoins. The implications for practitioners and policymakers are discussed.

Introduction

Cryptocurrencies are gradually gaining traction in trading and becoming a notable segment of the financial landscape, providing new investment avenues. However, their inherent volatility continues to hinder their broader acceptance. Among cryptocurrencies, a subset known as stablecoins, which is intended to maintain a “stable” peg to a reference currency, is crucial to the market as it allows traders to work around the volatility issue and keep money in a form equal to US dollars (De Blasis et al., 2023). Access to those digital assets is typically given by new financial technologies (also called Fintech) such as digital wallets or Centralized cryptocurrency Exchanges (CEXs), with the aim of decentralizing finance through innovative offers of financial services. However, due to the high costs associated with the trading of cryptocurrencies in US dollars, the impossibility of using US dollars on cryptocurrency exchanges,1 and the simplicity and speed of transferring those virtual assets between exchanges, stablecoins occasionally trade at a premium2 to the underlying asset they imitate (De Blasis et al., 2023). The recent bankruptcy of Silicon Valley Bank (SVB), the largest commercial bank for nearly half of all venture-backed tech startups in Silicon Valley,3 in March 2023 tied up $3.3 out of $40 billions of stablecoins in its financial statement, making this exogenous shock a significant event to study.

Thus, the aim of this research is to examine the impact of bank failure on the stability of stablecoin markets. Specifically, we examine the question of whether halting access to digital assets through the bankruptcy procedure of a traditional finance institution spills over contagion effects across those innovative instruments. Answering this question is important as the March 2023 collapse of SVB, together with the ensuing instability in a number of major stablecoins, demonstrated the importance of the interlinks between traditional and digital financial systems. The results of this research are therefore relevant to policymakers in both the traditional and modern financial systems interested in avoiding the risk of financial contagion, as well as investors willing to defend their savings and balance their portfolios against market uncertainty.

Conclusion

This study examined the extent to which the collapse of a commercial bank spread contagion across cryptocurrency markets and how those markets behave around the failure when asset withdrawals are halted. We use a BEKK-GARCH multivariate model to test for financial contagion around the Silicon Valley Bank collapse across multiple digital assets. Arguably, this is the second-largest bank run after the global financial crises experienced in history. We find evidence of volatility spillover effects across major stablecoins and Bitcoin, with a “flight to safety” from less to more stable virtual currencies.

Hence, this study provides important evidence of the ways the traditional and cryptocurrency financial systems are interlinked. Arguably, it links centralized and decentralized finance, showing the ways in which spillovers can occur between what seem to be separate realms of finance. Academics, practitioners, and policymakers alike interested in destabilizing risk in the digital finance ecosystem should pay attention to the connections with traditional finance and thus to the results of this research. A limitation of this study is that it focuses on the most liquid stablecoins and cryptocurrencies. Future studies may, therefore, further explore the links between centralized and decentralized finance, including stablecoins and other means of digital value exchange.

Abstract

This paper empirically investigates the role of bank structural characteristics on firms’ creation in the Italian local credit markets from 2009 to 2020. By departing from the existing research, our analysis takes the perspective of the so-called “biodiversity argument” in banking (Ayadi et al., 2009, 2010). As this viewpoint echoes insights from the ecological sciences, we measure bank diversity by retrieving two “biodiversity” indexes: the Gini-Simpson index and, for robustness, the Shannon index. Our results suggest that the coexistence of different institutional models in the banking landscape benefits the formation of new firms – especially those taking the legal form of limited liability companies, as innovative start-ups. We also find that, at the outbreak of the COVID-19 crisis, bank diversity might have mitigated the adverse effects of the pandemic turmoil. Our policy recommendation is that authorities design regulations to encourage institutional variety in the banking market.

Introduction

A sizable part of the literature investigating the drivers of entrepreneurship (e.g., Parker, 2018; Verheul et al., 2002) focuses on the financing sources for nascent firms and the role played by banks.1 In this respect, an open research debate concerns the relevance of banks’ structural and organizational characteristics. According to some contributions, small banks (local, single-market, typically stakeholder-value institutions), exploiting the knowledge of the local economy and their organizational structures characterized by few management layers, would have an advantage over large (nonlocal, multimarket, shareholder-value institutions) in collecting and using soft information (e.g., Liberti & Mian, 2009; Stein, 2002)2 – and, thus, in forging lending relationships that are crucial for the financing of firms suffering more intense information asymmetries (Berger et al., 2015, 2017; Berger & Udell, 2002; Cole et al., 2004; Mkhaiber & Werner, 2021; Petersen & Rajan, 1994; Scott, 2004).

Other studies claim that the paradigm by which small banks are advantaged in lending to opaque firms is misleading. For instance, Bartoli et al. (2013) assert that “complementarity among transactions and relationship lending technologies is indeed a prevailing phenomenon, compared to specialization in one primary lending technology, and that complementarity is higher for large banks compared to small local banks.” (p. 5477). Black and Strahan (2002) provide evidence that large banks’ superior ability to diversify credit risks across borrowers allows them to reduce agency lending costs and, thus, finance risky and opaque firms at better conditions than smaller banks. Not least, by exploiting the rapid ICT advances, large banks can finance informationally opaque firms by using transaction technologies such as credit scoring, asset-based lending, factoring, fixed-asset lending and leasing (e.g., Berger et al., 2005, 2014; Berger & Udell, 2006; Carter & McNulty, 2005; De Young et al., 2011; Frame et al., 2001; Petersen & Rajan, 2002).

Results

Column 1 of Table 3 shows the pooled Tobit estimation results of our benchmark model (Equation (3)). The estimated coefficient of ZGINI displays a positive sign and appears statistically significant at the conventional level. In terms of numerical interpretation, we find that one standard deviation increase in ZGINI is associated with an increase in the latent outcome variable – the propensity of new firms entering the market – of around 5% (a rise of 0.1112 percentage points over a baseline of 2.15).14 This finding suggests that the coexistence of different institutional bank types in local credit markets may be paramount in facilitating new firms’ formation, thus supporting the biodiversity argument discussed in Section 1. Far from questioning the role of each bank model, our results align with the view that, beyond the strengths and weaknesses of any of these models, institutional variety in the banking landscape matters in financing the real economy. Therefore, to borrow the words of Ferri (2010), a policy implication of our analysis is that “authorities must be aware that any regulation – e.g., levelling the playing field – should not damage the biodiversity of banking (p. 3)”.

Abstract

The purpose of this paper is twofold: (i) to identify the thematic categories and central topics connecting digitization and operational efficiency in the banking sector; and (ii) to develop a research agenda to guide future research endeavors to advance knowledge related to digital operational efficiency (DOE). Employing a two-stage content-centric review approach, this study comprehensively outlines overarching themes and subsequently delves into specific dimensions. The first stage aimed to provide an overview of research connecting operational efficiency and digitization in the banking sector and to refine the search criteria for content analysis. The second stage employed content analysis concentrated on journal articles published between 2018 and 2023. Central topics include enhancing banking industry performance through industry 4.0 technology and partnerships, the impact of the COVID-19 pandemic on bank digital transformation, organizational adaptations for embracing new digital business models, and elevating customer experience through novel operational paradigms. The research agenda proposed include the following: DOE conceptual development; digital bank break-even analysis; optimization of the product and service portfolio offered by digital banks; and consumer experience and level of service of digital banks. By delving into the impact of industry 4.0 technologies, pandemic, and digital business models, this paper enriches our understanding of the complex interplay between digitization and operational efficiency. The proposed research agenda offers a roadmap for future scholarly endeavors, contributing to the evolution of theoretical frameworks in this field. This paper offers insights relevant for banking industry professionals, providing them with valuable guidance on how to enhance their performance, optimize their digital product and service portfolio, and elevate the overall customer experience in the rapidly evolving landscape of digital banking.

Introduction

It is of utmost importance for banks and the financial services industry to assess the outcomes of their operations. This evaluation holds significant weight as it showcases their financial efficiency to various stakeholders, including the market, investors, competitors, and ultimately fosters a sense of trust among their customers (Pio et al., 2023). A well-established avenue to achieve this is through the utilization of the Operational Efficiency Index (OEI), which is determined by dividing the cost by the revenue (Nguyen, Tripe & Ngo, 2018; Khan & Shireen, 2020; Allen & Rai, 1996).

By employing this methodology, financial analysts and investors are empowered to conduct thorough financial analyses and comparisons across companies (Bangarwa & Roy, 2022). This encompasses scrutinizing variables such as payroll expenditures, revenues across distinct lines of business (e.g., credit cards and corporate banking), thereby enabling the identification of the most proficient companies. Furthermore, this approach aids in discerning which entities are most conducive for investment or divestment (Luo, Fan & Zhang, 2017).

Conclusion

This research paper has navigated the dynamic landscape of digitization in the financial sector, unveiling a nuanced tapestry of insights that collectively redefine the contours of operational efficiency and customer experience. The synthesis of contemporary literature, empirical analyses, and conceptual deliberations has culminated in a comprehensive understanding of the pivotal dimensions shaping the trajectory of banking in the digital age.

In terms of contributions, this paper sheds light on the pivotal role of digital technologies and partnerships in shaping the performance of the banking industry. It unravels the transformative impact of the COVID-19 pandemic, underscoring how digitization became a pivotal strategy for resilience and adaptation. Moreover, it delves into novel operational approaches and emphasizes the paramount importance of customer-centricity, offering a comprehensive research agenda that propels future inquiries.

These insights bear implications for both theory and practice. The conceptual development of DOE lays a robust foundation for further theoretical exploration, while the analysis of digital bank break-even dynamics and optimization of product portfolios guide practical strategies for sustainable digital banking models. The elevation of customer experience underscores the imperative of crafting seamless, technology-driven interactions that resonate with evolving consumer expectations, shaping a customer-centric paradigm in financial services.

Abstract

We test whether firm sustainability positively affects access to bank credit. We carry out a panel data analysis of 125 listed Italian SMEs across 14 regions over 2017–2021. A quantitative score comprising 20 items grouped into three areas, disclosure, processes, and governance, is computed to determine the level of implementation of sustainable practices of sampled SMEs. Results show that SMEs with more sustainable orientation have greater access to bank credit and better ability to pay the cost of bank debt. This, in turn, reinforces bank-SME relationships. We contribute to the literature on SMEs, highlighting the relationship between sustainability and the availability of bank loans.

Introduction

Starting from the notions of sustainable development in the Brundtland Report published by the World Commission on the Environment in 1987 (WCED, 1987), further attention on sustainability has increased, culminating in several important initiatives such as the Paris Climate Agreements (COP 21) and the 2030 Agenda of the United Nations for Sustainable Development, both adopted in 2015. More recently, specific activities and regulations implemented by the European Union, including the EU Taxonomy and the European Green Deal, provide additional support for the green transition and integration of sustainability into company business models (BIS 2021).

From the perspective of banks, financial regulators are strengthening regulations in an attempt to encourage these institutions to support and promote sustainability practices. Recently banking supervisors have launched important appeals and recommendations to raise awareness on the potential effects of environmental risk, improve the understanding of climate risk measurement issues facing banks and on transitioning to a low-carbon and more circular economy. Specifically, increasing pressure is being exerted on all EU banks to integrate sustainability factors into the lending decision-making processes. In turn, increased commitment to climate issues could lead banks to develop a more effective environmental management system and environmental credit risk management which mediates bank riskiness and improves overall financial stability (EBA – European Banking Authority, 2020a, EBA – European Banking Authority, 2020b).

Conclusions, implications and future research lines

The ‘sustainability challenge’ represents a global issue that affects all companies, whether financial or non-financial, especially following the Covid-19 pandemic. Falling behind in this process of adaptation not only means losing a competitive advantage but also risking being pushed out of the marketplace altogether. This is true for banks, which are now being asked by regulators to rearrange all their main business processes (e.g., strategies, risk management, capital allocation, loan origination and monitoring, disclosure), adapting them to comply with specific ESG sustainability criteria. It is even more true, however, for non-financial firms, especially SMEs. These firms are a pillar of the European economy and a central player in value chains and community dynamics (Beck, 2013; Gomes and Pinho, 2023; Angilella and Mazzù, 2015). Thus, involving them in the sustainability process is vital to ensuring the success of the transition to sustainability in Europe. In addition, for SMEs, being included in the sustainability agenda is paramount to accessing key resources and opportunities, obtaining better financial conditions and entering into important partnerships (European Investment Bank-EIB, 2023).

Scholars have revealed that an increasing number of banks are starting to include ESG factors in their lending decisions (Scholtens, 2009, Goss and Roberts, 2011). Many financial intermediaries, facing the threats generated by ESG risks, tend to prefer lending to businesses that are more sustainable and, therefore, less risky (Weber, 2012). Academic research shows that this strategy is virtuous because granting credit to more sustainable companies creates value for banks in terms of a higher net interest margin and a reduction in default risk (Mirza et al., 2023). For these reasons, banks are also increasing their share of green operations by offering capital at lower cost to firms more compliant with environment goals (Attig et al., 2013, El Ghoul et al., 2018, Wellalage and Kumar, 2021).

Abstract

The application of big data technology to global tax management is becoming increasingly widespread. China has been implementing increasingly mature technologies for tax governance using big data systems in recent years. By collecting data through web scraping on the earliest implementation times of big data tax administration in various provinces of China, we explore the relationship between big data tax administration and corporate bank credit in emerging markets. Our results show that big data tax administration enhances firms’ ability to obtain bank loans. Mechanism tests indicate that big data tax administration improves the quality of corporate information disclosure, facilitating access to bank credit loans. We find that big data tax administration improves the corporate financing environment, enhancing the efficiency of resource allocation in the credit market.

Introduction

The digital economy has expanded rapidly around the world. Driven by continuous upgrades in Internet functionality and the widespread application of big data, significant changes are occurring in governments, corporate business models and in people’s daily lives (Chen and Srinivasan, 2024). Taxation departments provide a good example of such changes, as big data technology is expanding the traditional auditing model. “Big data tax administration” combines big data with tax auditing; it involves acquiring big data from Internet platforms and integrating and comparing multiple sources of data (Bassey et al., 2022). Its implementation, which has become a new trend in national tax governance, reflects the modernization of national governance capabilities and systems within the tax system and significantly enhances the efficiency of governments’ tax collection (Canares, 2016). A key question is whether, looking beyond strict tax enforcement, modernized tax governance can guide firms to improve the quality of their information disclosure, thereby enhancing the efficiency of resource allocation in capital markets? Examining this question yields insights relevant to governments worldwide who are implementing governance based on big data technology.

Bank loans are an important financial resource and whether they are allocated in a timely and appropriate manner is an important issue from both theoretical and practical perspectives. An extensive body of research discusses the various factors that influence firm credit, including corporate characteristics and the policy environment. However, few studies explore whether innovations and modernizations within the tax governance system can improve the financing environment for firms. Studying the effects and mechanisms of big data tax administration on corporate bank credit enhances the understanding of the social effects of modernized governance and provides empirical evidence for the motivational impact of “tax administration with data” on firms.

Conclusions and implications

The widespread adoption and application of the Internet and big data have reduced the cost of information flow, and the multi-party storage and sharing of big data has gradually become a new form of social capital. The use of big data by tax authorities not only improves the capabilities and efficiency of tax collection but also promotes the rational allocation of financial capital at the national level and facilitates enterprise financing. We investigate the effects of big data tax administration on corporate credit and find that implementing big data tax administration (1) has a positive impact on corporate bank credit; (2) improves the quality of corporate information, thereby promoting corporate credit and (3) has a more pronounced effect on enterprises with stronger (vs. weaker) financial constraints. The conclusions of our study are beneficial because they enhance academic understanding of the economic consequences of big data tax administration, and also have practical significance for tax authorities actively seeking external forces to improve tax collection procedures. Based on our theoretical and empirical analyses, we put forward the following policy recommendations.

First, the government should enhance cooperation between tax authorities and third-party online platforms, synchronously strengthening tax administration under the “delegation, management and service” framework. We show that by enhancing information collecting capabilities, big data tax administration improves the quality of corporate information disclosure, thereby optimizing corporate financing efficiency. In the context of the digital transformation of enterprises and the increasing application of big data in society, tax inspection departments should make full use of the data at the societal level, engaging in cooperation with online platforms and online assessment agencies. By utilizing the correlation between multi-party data and the data provided by enterprises, tax inspection departments can strengthen their verification of corporate information. This will help eliminate the corporate practices of inflating projects and manipulating information disclosure, and encourage and guide firms to improve their information quality.

Abstract

The recent banks’ failures have highlighted the importance of improving banking sector supervision, emphasizing the need to adopt a holistic approach to risk assessment based on an evaluation of a bank’s business model (BBM) that combines financial (e.g., bank’s balance data) and non-financial information (e.g., bank’s ESG performance). In this study, we explore the joint effect of BBM and their environmental (ENV), social (SOC), and governance (GOV) pillars performance on banks’ riskiness profile. The study uses a sample of 639 EU banks from 2013 to 2022 and applied a random effects model. Our findings suggest wholesale and retail banks could mitigate default risk, enhancing their ENV pillar performance. Differently, investment banks are encouraged to improve their governance best practices and structure to take advantage in terms of riskiness reduction. These results remain consistent after a series of robustness tests, including the 2SLS model and the Arellano coefficient estimation. Our paper offers practical implications for banking supervisory authorities and practitioners, encouraging to adopt a diversified ESG investment strategy according to bank-specific business models.

Introduction

Since the end of the financial crisis, supervisory authorities and policymakers have strengthened banking regulation to assess and monitor the vulnerabilities that have affected the financial system (FSB, 2023). Following these regulatory requirements, the bank business model (BBM) has been a topic of interest in the banking literature to understand better business models characteristics and their divergent impact on performance, efficiency, riskiness, and solvency (BIS, 2022). To stress the importance of business model analysis (BMA), some scholars have shown how business models (BMs) contain information that extends beyond the traditional indicators of bank risk and return, providing regulators and supervisors with better insights into the sustainability of bank profits and stability (Lartey, James, Danso, & Boateng, 2022). Focusing on the risk side, some authors (Marques-Ibanez & Scheicher, 2010) have highlighted how business models can signal a firm’s risk-taking propensity, facilitating incentives to hedge risk.

The recent medium-size banks’ demises (e.g., the SVB bank crisis) have also emphasised the importance of BMA and converging on a compressive evaluation of financial stability. Consequently, supervisory authorities encourage implementing a holistic approach to bank risk assessment to minimize these adverse events and contagion effects in the banking industry (BIS, 2019). Given the evolving market and business environments, supervisors do not look solely at capital adequacy at a single point in time, but rather, they assess BBM viability on longer time horizons.

Indeed, when conducting BMA, supervisors rely on various sources, including banks’ financial reporting; business plans; and internal reporting. BMA is conducted by examining banks’ business environment and dialogues with internal and external stakeholders. In assessing banks’ riskiness, supervisors should consider financial data and non-financial information disclosure (EU, 2014 Non-Financial Reporting Directive – NFRD) to detect risk drivers and crisis determinants not yet investigated in their current analysis models (EU, 2014).

Conclusion, limits, and implications

n this study, we provide empirical evidence concerning the banks’ business models capabilities to differentiate their risk mitigation effect through investment in individual ESG pillars.

Our findings show that regarding the environmental dimension, wholesale banks display a negative association between environmental (ENV) score improvement and default probability. Regulatory responses (EU, 2020) to global environmental challenges further amplify the potential default risks for banking institutions. Although, to a lesser extent, retail banks are also negatively associated with environmental issues. This differentiation arises due to the inherent customer-focused nature of retail banking (Liu & Wan, 2023). As the public’s environmental consciousness increases and regulatory frameworks develop with a concentration on consumer protection, retail banks’ risk profile undergoes slight transformations.

Regarding the social dimension (SOC), investment banks exhibit a strong initial positive relationship, which transitions into a negative one over a longer time horizon. The nature of investment banking activities, characterized by extensive stakeholder interactions and often tied to complex financial ventures, makes them particularly sensitive to social considerations. This susceptibility affects their risk assessments, influenced by social perceptions and stakeholder reputation. Additionally, retail banking, inherently rooted in direct interactions with individual consumers, emerges as exceptionally responsive to social factors. The intricate weave of societal norms, community sentiments, and individual consumer preferences plays a pivotal role in shaping the default risk profile for these banks.

Abstract

Purpose
This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach
This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings
The paper examines the multi-wave implementation of big data analytics in a Taiwanese bank.

Originality/value
The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Challenges with big data analytics

Despite its potential, the adoption of big data analytics is hindered by numerous challenges. Financial consideration and human resources are the two main factors obstructing its implementation. Studies suggest many banks are yet to realize the full benefits and returns of leveraging big data. Most organizations also lack adequate managerial and technical skills to gain value from big data usage. Indeed, many banks unfamiliar with this new practice lack the capabilities to embed big data analytics into their operations and processes. A difficulty firms face is reducing a large and diverse dataset derived from several sources into easy-to-interpret results that can support decisionmaking. It becomes critical for firms to employ experienced data scientists who can uncover insights that can help improve the bank’s operation and performance. Banks also face many non-technical challenges in big data applications. Organizations need the buy-in from organizational members from all ranks-from senior management to lower levels across various functions. The adoption of new technologies necessitates changes in operating practices which may result in organizational resistance. Security, privacy and government regulations can also impact big data applications.

Impact of big data analytics in bank A

Using insights from customer clustering and product affinity models, Bank A was able to adapt its offerings across channels. For example, the organization used cluster affinity scores to target specific marketing campaigns for those customers. Bank A implemented these changes in three waves across ten months. The results showed that the different divisions (e.g. wealth management and personal finance) improved their response rates using the two data analytics methods.

Abstract

     This paper explores the relationship between managers’ labor mobility and the financial reporting quality of banks. Using the state-level adoption of the Inevitable Disclosure Doctrine (IDD) as an exogenous shock discouraging labor mobility, we show that adoption of the IDD is associated with a decline in financial reporting quality, as measured by discretionary loan loss provisions. The effect is larger for banks with managers who have limited outside job opportunities and smaller for banks with tight regulatory oversight. Our results support the view from the career concern hypothesis that bank managers facing restrictions on mobility have greater incentives to engage in discretionary accounting.

Introduction

     Since the 2008 global financial crisis, accounting and finance researchers have shown considerable interest in bank transparency. The crisis highlighted the economic impact of bank opacity and gave rise to the notion that a lack of information on bank asset quality can harm financial stability. The opacity of banks can exacerbate agency problems by hindering market discipline, and increase the risks of panic and contagion during a crisis (Nier and Baumann, 2006, Morgan, 2002). During the recent global financial crisis, investors sought information on banks’ exposure to risks, but this was not readily available. Along with explicit government backing of the banking system, the public disclosure of stress test results helped to relieve the panic by providing investors with the needed information (Jungherr, 2018).1 Since then, extensive literature has discussed how much transparency in banking should be required, and how transparency affects financial stability (see Acharya and Ryan (2016) for a survey). There is, however, still a paucity of research identifying the determinants of bank transparency, despite the necessity of effective monitoring and regulations. We aim to fill this gap by examining the effect of bank managers’ labor mobility on financial reporting quality.

     In this paper, we define labor mobility as the degree of friction in the labor market, in terms of how easily individuals can transfer from one job to another. Job switching is an important channel through which individuals increase their compensation or find better-suited jobs (Topel and Ward, 1992). Therefore, the possibility of job switching is used as leverage in negotiating compensation levels and working conditions both explicitly and implicitly. This is well demonstrated by recent retention awards to bank executives, that were granted as competition for talent intensified among banks.2 The observed efforts made by banks to retain executives imply that a restriction on labor mobility would lower managers’ bargaining power in the job market. Accordingly, managers’ career concerns would increase. In response, to secure bargaining power, managers may have higher incentives to window-dress their performance by distorting information disclosed to the public. Consequently, the financial reporting quality would be compromised (hereinafter, we refer to this proposed effect as the career concern hypothesis). There are a number of studies on industrial firms testing and supporting this hypothesis (Ali et al., 2019, Chen et al., 2018, Tang et al., 2021), but not yet for banks.

Concluding remarks

     The 2008 global financial crisis confirmed a longstanding concern around bank opacity, namely that a lack of information on banks’ asset quality can lead to greater systemic risk. Given the economic impact of bank opacity, it is necessary to understand the determinants of banks’ financial reporting quality to enhance financial stability. To this end, we evaluate the effect of managerial labor mobility on financial reporting quality at banks. We focus on the degree of labor mobility because it constrains managers’ outside job opportunities, and thus affects their career concerns. This is particularly important in the banking industry because of its opaque nature. Despite the existence of accounting rules and regulations, bank managers still have some discretion over financial reporting due to their informational advantages and expertise in bank operation (Beatty and Liao, 2014; Kim and Kross, 1998; Cornett et al., 2009). We conjecture that a restriction on labor mobility motivates earnings management by triggering managers’ career concerns, and thereby impairs financial reporting quality at banks.

     This paper evaluates the impact of a legal shock discouraging labor mobility (i.e., the IDD) on discretionary provisioning by banks. We find that banks accrue loan loss provisions in a more discretionary manner after their home state adopts the IDD. Rejecting the IDD has the opposite effect, i.e., reducing discretionary provisions. These results suggest that restrictions on labor mobility impair the financial reporting quality of banks, making it harder for outside investors and regulators to understand bank fundamentals. We further perform a number of tests on the heterogeneous effects of the IDD. We find larger effects of the IDD on discretionary provisioning for smaller banks, private banks, and banks with fewer peers in the local market. These results support the view that managers’ career concerns are the channel through which labor mobility affects financial reporting quality. We also use alternative measures of financial reporting quality: the validity of provisions in estimating future loan losses, and just-meeting-or-beating earnings benchmarks. Our results using the alternative measures are broadly consistent with the earlier findings. First, after IDD adoption, banks’ loan loss provisions become less accurate estimations of actual loans charged off in the subsequent period. Second, we provide evidence that IDD adoption increases bank managers’ use of accounting discretion to meet or beat an earnings benchmark.

Abstract

     Financial statements are analytical reports published periodically by financial institutions explaining their performance from different perspectives. As these reports are the fundamental source for decision-making by many stakeholders, creditors, investors, and even auditors, some institutions may manipulate them to mislead people and commit fraud. Fraud detection in financial statements aims to discover anomalies caused by these distortions and discriminate fraud-prone reports from non-fraudulent ones. Although binary classification is one of the most popular data mining approaches in this area, it requires a standard labeled dataset, which is often unavailable in the real world due to the rarity of fraudulent samples. This paper proposes a novel approach based on the generative adversarial networks (GAN) and ensemble models that is able to not only resolve the lack of non-fraudulent samples but also handle the high-dimensionality of feature space. A new dataset is also constructed by collecting the annual financial statements of ten Iranian banks and then extracting three types of features suggested in this study. Experimental results on this dataset demonstrate that the proposed method performs well in generating synthetic fraud-prone samples. Moreover, it attains comparative performance with supervised models and better performance than unsupervised ones in accurately distinguishing fraud-prone samples.

Introduction

      Today, the incremental growth of fraud in business, especially in financial services, has become an earnest and costly problem. There exists no single definition for the concept of fraud in scientific sources. One of the clearest definitions available is the one provided by the Association of Certified Fraud Examiners (ACFE) in 2008. According to this definition, individuals and organizations may commit illegal actions such as deception or betrayal of trust for specific reasons, such as obtaining money, property, or individual or collective benefits, which are interpreted as fraud (Hashim et al., 2020, Sadgali et al., 2019, Syahria, 2019). The American Institute of Certified Public Accountants (AICPA) has also attributed the concept of fraud to any type of fraud, including minor employee theft, unproductive performance, embezzlement, misappropriation of assets, and fraudulent financial reporting (Hashim et al., 2020).

     As it can be understood from the above definitions, there are variants of fraud, among which this study is focused on fraud in financial statements. Financial statements are reports that detail an organization’s business activities and financial performance from various perspectives (Ashtiani and Raahemi, 2021, Jan, 2018). The most important contents of these reports include expenses, incomes, received or granted loans, profits, and losses (Ashtiani & Raahemi, 2021). These large amounts of numbers and figures provide an opportunity for profit seekers to cheat. Among the most common forms of fraud in financial statements are premature revenue recognition, spurious entries of incomes or profits, overstating assets, understating expenses, and concealment or false disclosure of expenses (Craja et al., 2020, Gray and Debreceny, 2014). According to the ranking provided by ACFE, financial statement fraud is the third most prevalent type of occupational fraud, after corruption and embezzlement (Hashim et al., 2020, Petković et al., 2021, Syahria, 2019). However, it has taken first place regarding the financial costs and the amount of loss it incurs (Omidi et al., 2019). Hence, early detection of this type of fraud can prevent its exorbitant financial consequences.

Conclusion and Future Work

     In this paper, a new approach has been proposed to detect fraud in bank financial statements. The basic idea is to adopt generative adversarial networks instead of over-sampling, under-sampling, or one-class classification techniques to make the approach applicable to real-world scenarios whose data is highly imbalanced with no or few fraudulent samples. The second idea is to tackle with high-dimensionality of the feature space by leveraging the ensemble of supervised and unsupervised models. In particular, the proposed approach utilizes a kind of generative adversarial model called MO-GAAL to fabricate a set of fraud-prone samples that have unconventional behavior on the one hand and are difficult to distinguish from fraud-free samples on the other hand. Further, samples are classified by an ensemble model, namely XGBOD, in which the outlier scores of each sample are first estimated by a collection of unsupervised models, and then these scores form a new feature vector to be classified by a supervised model named XGBoost. In summary, the ability to train an efficient decision-making model even in the absence of actual fraudulent samples is the main advantage of this work.

Abstract

     Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results.

Introduction

     The assessment of various financial aspects of banks occupies an essential place in the academic literature because of the crucial intermediation role of the banking industry in financial markets (Ioannidis et al., 2010; Tzeremes, 2015; Zopounidis et al., 2015). Along with an increasing need to use more sophisticated methods in banking research, several studies in this area employ operational research (OR) and artificial intelligence (AI) methods. Thus, the existing literature examines some fundamental research questions in banking research using OR and AI techniques, such as addressing the fairness issue in banking performance evaluation (Chen et al., 2020) and increasing the accuracy of the prediction of default risk and bank failure (Boussemart et al., 2019), as well as helping centralized organizations (e.g., headquarters of banks) to incentivize their units (i.e., bank branches) and optimize their performance (Afsharian et al., 2019). A rising trend in the utilization of OR and AI techniques to address banking challenges indicates their increasing importance and relevance for this field (Akkoç, 2012; Manthoulis et al., 2020; Yao et al., 2017).

Conclusion

     This article presented an extensive review of the crucial role played by OR and AI methods in banking research by analyzing a total of 338 studies published between 2010 and 2020. We described six general topics that employ OR and AI methods to address various crucial banking issues: banking efficiency, risk management, bank performance, banking regulation, M&A, customer-based studies, and fintech in the banking industry. We also outlined the most widely used OR methods, including DEA, ABM, MC, fuzzy logic, and AI techniques, including SVMs, NNs, and ensemble methods. This article contributes to the literature by complementing the prior bibliographic surveys, covering various general banking topics, and summarizing the different methods applied.

     We also suggested potential future research directions from both topic and methodology perspectives. Researchers could explore and verify various OR and AI methods in banking studies from a methodological perspective. Thus, regarding future research topics, efficiency forecasting related to the evaluation of financial stability could justify further exploration, as could the investigation of non-financial risks, such as conducting risks, which has received very limited attention in the academic literature to date. Future studies might also explore the impacts of government regulations and managerial behaviors on risk-taking by banks. Finally, future research could also apply other AI methods (e.g., unsupervised machine learning) or fresh combinations of OR and AI techniques to banking research.

Abstract

     Our investigation of 46 conventional and 22 Islamic banks from the Gulf Cooperation Council (GCC) countries during 2008–2021 reveals that sectoral diversification effects on stability are nonlinear and different for the two bank types. While Islamic banks’ stability is worsened only by moderate levels of diversification, conventional banks’ stability is enhanced by high levels and impaired by low levels of diversification. Furthermore, diversification acted as a stabilizer during the global financial crisis but exacerbated the adverse effects of the Covid-19 pandemic. Although regulators usually call for bank diversification, our results imply that it can be a double-edged sword.

Introduction

     Due to dependence on oil and gas revenue and their price plunge caused by the Global Financial Crisis (GFC) in 2008, the governments of the Gulf Cooperation Council (GCC) countries started introducing various policies aiming at developing diversified real and financial sectors.1 While oil and gas production still represents over 40% of the GDP in GCC countries, with the exception of Bahrain and UAE, (Esam, 2021) shows that there has been significant diversification away from hydrocarbon revenue since 2010 in all GCC countries, particularly in Saudi Arabia, UAE and Bahrain. Given the low development of capital markets and non-bank financial institutions in these countries, banks play a pivotal role in assisting the execution of these national diversification policies by providing financing to support investments in the existing and ‘new’ sectors. The proponents of diversification in banking would see this as an opportunity for banks to spread their loans and financing2 across different sectors and thus reduce bank default, liquidity, and bankruptcy risks (Diamond, 1984; Demsetz and Strahan, 1997; Rose and Hudgins, 2006), vulnerability to economic downturns and financial crises (Tabak et al., 2011; Doumpos et al., 2016), and become safer and more stable (Saunders and Walter, 2012). On the contrary, the opponents of diversification would argue that banks are better off by focusing on a single sector or a few sectors as that allows them to leverage their screening and monitoring knowledge and expertise in these sectors and thus reduce the unnecessary complexity, agency problems, overhead expenses (Klein and Saidenberg, 2010), earnings volatility (DeYoung and Roland, 2001), and write-downs and write-offs (Jahn et al., 2013) that usually come with diversification.

Discussion and conclusion

     Against the backdrop of the GCC countries’ ongoing economic diversification policies and banks being the primary financiers of the projects brought about by these policies, we investigate (i) if and how diversification of loans and financing across economic sectors impacts the stability of GCC banks, (ii) if the effects are the same on conventional and Islamic banks, and (iii) if diversification helps banks navigate smoothly through economic downturns such as the 2008 global financial crisis and the ongoing Covid-19 pandemic.

     anks navigate smoothly through economic downturns such as the 2008 global financial crisis and the ongoing Covid-19 pandemic. Analyzing a unique panel dataset of 46 conventional and 22 Islamic banks, we find that there is no one-strategy-fits-all even within these seemingly similar countries. Diversification effects on bank stability are nonlinear and different for Islamic and conventional banks. While Islamic banks’ stability worsens at moderate levels of diversification, conventional banks’ stability thrives at high levels and erodes at low levels of diversification. Given that the diversification measure is an inverse measure of concentration, this means that a high concentration of loans on one or a few economic sectors also benefits conventional banks. Lastly, we observe a temporal dimension of our results which suggests that diversification supported bank stability against the destabilizing effects of the global financial crisis but amplified the adverse effects of the Covid-19 pandemic.

Abstract

     Social distancing practices and staying at home have increased the time people spend on social media with the purpose of exchanging and consuming information about completing their routine practices safely. The Covid-19 pandemic has disrupted in-person banking operations and increased the physical threat for both retail bankers and customers. Consequently, the world has moved toward internet banking with the purpose to continue routine transactions for paying bills, purchasing groceries, and shopping of brands. The present study aims to explore how the fearful environment of Covid-19 pandemic increased the social practices of internet banking and what challenges are faced by different customers during their use of internet banking. By understanding these experiences, system developers and marketers can improve their internet banking apps to address the needs and expectations of different customers. The study has opted for a relativist ontological position and social constructionist epistemological position to understand different realities in the same context and how customers experience internet banking in a fearful environment. The study used multiple qualitative research methods: Gibbs reflection cycle, semi-structured interviews with internet banking users, and focus group interviews with executives of public and private sector banks. Social actors through social media played a role with respect to understanding the nature of uncertainty during Covid-19 pandemic that changed customers’ behavior from traditional banking to internet banking. Information was shared through social media to avoid ATMs, debit and credit card exchange with cashiers, and cash exchange as these are no longer safe options for both bankers and customers. The major theoretical contribution of this study is to merge social practice theory and affordance of technology theory in the context of internet banking adoption. This study has discussed practical implications for marketers and system developers of retail banks.

Introduction

     Existing research on internet banking suggests that internet banking is relatively new in developing countries compared to the developed world (Abbasi et al., 2017; Rahi et al., 2019). For example, out of 200 million population of Pakistan, only 3.1 million are registered internet users, which highlights a very low adoption rate of internet banking (Rahi et al., 2020). Prior research highlighted causes of low adoption rate of internet banking in Pakistan, such as weak legal system, high poverty, corruption, high cybercrime, low trust (Akhlaq and Ahmed, 2013), low literacy rate, disheartening experiences, low internet accessibility and high electricity shortfall, high resistance to adoption of new technologies (Abbasi et al., 2017), low security, and high uncertainty (Rahi et al., 2019, 2020). Other studies reported that technology adoption and acceptance is low in Pakistan due to low financial performance and privacy risks (Anwar et al., 2020; Khan et al., 2018). It was found that low knowledge and awareness can negatively influence intention to use telebanking; therefore, more training programs are required to educate customers in developing countries, such as Jordan (Alalwan et al., 2016, 2018; Chauhan et al., 2019). In Malaysia, approximately 50% of the population have adopted internet banking, which is relatively low compared to developed countries, such as the USA (Amin, 2016; Yuen et al., 2015). In Tunisia, high fallibilities and vulnerabilities, such as economic and political instability as well as low accessibility to internet banking, have negatively influenced customer attitudes and adoption rate (Chaouali et al., 2017). Most of the existing literature on internet banking in Pakistan highlights the causes of low internet banking adoption (Anwar et al., 2020; Khan et al., 2018; Rahi et al., 2019, 2020), but this study aims to understand how the fearful environment of COVID-19 pandemic forced people to adopt internet banking as a social practice as well as the challenges customers faced during their use of internet banking.

Contribution

     First, the extensively used TAM provided understanding about customer perception and usefulness of internet banking system (Alsajjan and Dennis, 2010; Chauhan et al., 2019; Marakarkandy et al., 2017; Mohammadi, 2015). However, the TAM did not aid understanding of how uncertain situations can play a role in the adoption of the social practices of internet banking through social interactions and social wisdom via social media platforms. Second, the studies on original TAMs and extension of TAMs (e.g. Chauhan et al., 2019; Munoz-Leiva et al., 2017; Kumar et al., 2011; Lai, 2017) did not consider the external environment (i.e. fear appeal and health risks of COVID-19) as a pushing factor of internet banking adoption. Third, although there are many theories, such as theory of reasoned action and theory of planned behavior (Asongu et al., 2018; Alsajjan and Dennis, 2010), social cognitive theory (Boateng et al., 2016), perceived risk theory (Roy et al., 2017), and models such as Unified theory of acceptance and use of technology model (UTAUT) (Choudrie et al., 2018; Rahi et al., 2019), UTAUT2, and information system success models (Baabdullah et al., 2019; Tarhini et al., 2016) for understanding the factors that can influence the adoption of internet banking, most of the existing literature does not explore the role of social media which can increase awareness, motivation, and competencies for internet banking adoption in an uncertain and fearful environment. Therefore, this study has provided the socially practiced affordance of technology (SPAOT) model that aids understanding regarding the meanings created about uncertainty through the social interactions of social actors using social media platforms, which can enhance the adoption of internet banking. For example, the COVID-19 pandemic has changed social practices from traditional banking to internet banking, especially in Pakistan, as there are a lot of physical (i.e. fear of illness as many people are not following social distancing practices), social, and psychological threats (low hygiene during ATM use, fear and stress about visiting bank branches) involved in traditional banking practices. The SPAOT model highlights that shared material through social media increased people’s awareness, motivation, and competencies to use internet banking in a country where there are issues of lower literacy, readability, electricity, and internet accessibility.

Abstract

     Mobile financial services (MFS) have been a powerful innovation to provide cost-effective services and wide coverage to the unbanked population of the world. The topic has gained considerable attention from researchers but lacks a clear guideline or directions for future prospects. Therefore, this article presents a systematic literature review on MFS adoption. Using the Web of Science database, the paper reviewed 118 articles and revealed significant models, conceptual frameworks, antecedents and variables that explain consumers’ adoption of MFS. The paper also outlines highly cited authors, studies and journals utilizing international platforms such as Google Scholar, ResearchGate and SCImago Journal Ranking. The findings of the systematic literature review indicate the technology acceptance model (TAM) followed by the unified theory of acceptance and use of technology (UTAUT) as the leading conceptual framework, and MFS adoption antecedents can be organized into six different categories, viz. Cognitive determinants, affective determinants, social-based determinants, trust-based determinants, barrier-based determinants and consumer-based determinants. The paper concluded with an agenda for future studies with corresponding gaps in the literature.

Introduction

     The eighth goal of sustainable development goals (SDGs) aims to strengthen financial institutions and ensure universal access to financial services by the year 2030. In this regard, mobile financial services have become an influential instrument in stimulating financial inclusion in developing countries. These services have created a dynamic platform by extending the reach and access to financial services to those who have insufficient resources and restricted access to conventional banking (Sihvonen, 2006). As reported by Global System for Mobile communications Association (GSMA), nearly 2.5 billion people in developing countries are unbanked and rely on cash or informal sources for their financial requirements (Plenderleith, 2018). Besides, around one billion of these population have access to mobile phones, which can serve as the foundation to provide various financial services such as balance inquiry, money transfer, payments, investment, insurance and credit facility. Indeed, many major banks have leveraged mobile devices as a principal medium for customers to access their accounts. They have created mobile apps and websites to provide their customers with an appropriate blend of comfort, convenience and user experience (Chawla and Joshi, 2017). Given the magnitude of competition, service providers are inclined to adopt online channels in an attempt to overcome the restriction related to human service encounters (Alalwan, Dwivedi, Rana and Simintiras, 2016a). In particular, the internet and the high penetration of smartphones, on the one hand, have prompted banks, software companies and microfinance institutions to provide these innovative services to extend their client reach and, on the other hand, enabled individuals to perform their financial activities cost-effectively regardless of time and place constraints.

CONCLUSION

     The area of mobile financial services is gaining worldwide attention, witnessing an increasing number of scientific and nonscientific articles. Although, the literature on MFS adoption has been reviewed in the past but existing reviews did not focused on every form of mobile financial services, that is, mobile banking, mobile payment and mobile money. Accordingly, this study highlights the scholarly contribution by synthesizing extant literature on MFS adoption. To do so, we have conducted a systematic literature review on 118 articles published in Web of Science database

     It was observed that the research pertaining to mobile financial services has started appearing in journals from the year 2007 and the count has been gradually increasing ever since. Besides, the publication outlets, the most commonly used theories and frameworks, countries, statistical tools applied in the articles were listed to outline the context and characteristics of MFS adoption studies. Further, a comprehensive list of antecedents, moderators, mediators and consequences employed by researchers to examine MFS adoption is provided. With respect to the antecedents, the literature brings out a myriad of factors that have been used frequently in the studies. To facilitate the interpretation, the antecedents have been classified into six different categories: Cognitive determinants, affective determinants, social-based determinants, trust-based determinants, barrierbased determinants and consumer-based determinants.