Table 7 presents a thematic analysis of fintech measures and the various types of bank risk examined in past studies. Fintech indicators are categorized into three main groups: bank-level, country-level, and fintech keywords. Bank risk studies are classified into four primary risk types: insolvency risk, credit risk, liquidity risk, and market risk. The following sections discuss the relevant themes and subthemes within these categories.
Fintech measures
Measuring fintech using bank-level data
Our findings provide a comprehensive understanding of fintech measures at the bank level with a focus on specific indicators such as financial innovation (FI) and online channel use (OCA). FI is a transformative force in the banking sector and is highlighted in three studies. It includes advances such as training and development (TD) (Tran et al. 2022), financial innovation (FI) (Zouari-Hadiji, 2023), and fintech-based financial inclusion (FFI) (Banna et al. 2021). TD is an essential component of FI as it aims to improve the capabilities and qualifications of employees as well as individual and organizational performance. Banks save money and time by running employee training programmes. Tran et al. (2022) used TD to measure the fintech index and reported that fintech has a positive and significant effect on bank risk. A study by Banna et al. (2021) constructed a comprehensive FFI index that considers the number of mobile money agents, mobile money accounts, nonbranch commercial bank agent branches, point-of-sale terminals, and mobile and internet banking transactions. Their study found that fintech had a negative and significant effect on bank risk and demonstrated the complex effects of different fintech measures.
OCA is another important indicator to predict fintech. Mobile banking and banking applications have attracted customer savings and deposits, highlighting the impact of OCA on the adoption of financial technology. Tran et al. (2022) chose mobile banking in OCA to measure the fintech index because of its strong appeal to customer savings and deposits. He et al. (2020) used a dummy variable to measure the use of online channels with a value of 1 for banks that used online channels and zero otherwise. Using a panel, they reported descriptive statistics between adopting and nonadopting banks to further illustrate the effect of OCA on bank performance.
Measuring fintech using country-level data
The most prominent fintech indicators at the national level are the digital finance index (DFI) and the commercial bank digital transformation index (Trans). Three studies use the DFI, compiled by Peking University’s Digital Finance Research Centre, as a proxy for fintech development (Deng et al. 2021; Hu et al. 2022; Wang et al. 2023). Similarly, three studies use the Commercial Bank Digital Transformation Index (Trans), compiled by the Institute of Digital Finance at Peking University, which includes cognitive, organizational, and product transformation dimensions (Cao et al. 2022; Chen and Shen, 2023; Liang et al. 2023). The index provides a macro-level view of fintech adoption to help policymakers and researchers assess digital transformation and technological advancements in the financial sector.
Other country-level indicators include macroeconomic uncertainty (EU), listed bank digital construction (Digital), the U.S. macroeconomic uncertainty index (USMU), the number of mobile phone users (Mobile) (Liang et al. 2023), noninterest income (NII) (Gao et al. 2023; Wan and Luo, 2023), the M2/M1 ratio (Khan et al. 2021), and patents (Dietzmann and Alt, 2019). Macroeconomic uncertainty (EU) represents a forecast of future uncertainty that increases the procyclicality of lending behaviour and hinders financial integration, with fintech developments helping to reduce information asymmetry. The digital construction of listed banks (digital) reflects the level of fintech development and is crucial for evaluating digital transformation. The USMU and the number of mobile phone users (Mobile) illustrate the correlation between U.S. economic policy and China, with mobile phones as the main channel for digital banking products. The interaction of these variables measures the fintech index, which has a significant positive effect on banks’ risk-taking (Liang et al. 2023). Noninterest income (NII) was chosen as a measure of fintech because of the shift from traditional interest income to noninterest income, which has a significant negative impact on bank risk (Wan and Luo, 2023). The M2/M1 ratio, which represents the ratio of broad money to narrow money, is used as a proxy for financial innovation and shows that increasing the money supply encourages fintech, which has a significant negative relationship with bank risk in ASEAN banks (Khan et al. 2021). Dietzmann and Alt (2019) suggest that patents are a suitable indicator for measuring innovation activities. The results show that patents have a negative and significant effect on bank risk. Therefore, financial innovation, especially information technology-related innovation, reduces banks’ risk-taking and risk-planning.
Measuring fintech using fintech keywords
As evidenced by eleven studies, social media platforms (SMPs) play an important role in the financial technology landscape. SMPs facilitate customer engagement, marketing, and service delivery and serve as powerful tools for fintech firms to engage with wider audiences, gather customer insights, and improve their service offerings. This research focuses on their role in shaping consumer behaviour and driving fintech adoption to influence the market. Many studies have adopted quantitative methods and fintech keywords to analyse the fintech index using text mining technology. In text mining, intelligent algorithms and data mining techniques are used to extract practical information from large text datasets, including frequency statistics, classification, and clustering (Cheng and Qu, 2020).
The process begins by classifying fintech into dimensions on the basis of its function in commercial banking applications and creating a keyword dictionary for each dimension. Crawler software is then used to conduct keyword frequency searches in search engines such as Baidu (Chen et al. 2022; He et al. 2023; Li, 2023; Li et al. 2022b; Zhang et al. 2023; Zhao et al. 2023). Since the data from the crawler cannot be directly analysed, some studies use the factor analysis method to calculate the fintech index (Chen et al. 2022; Chen and Shen, 2023; Wang et al. 2021; Yao and Song, 2021), whereas others calculate word frequency and then synthesize the fintech index (Li et al. 2022b; Zhao et al. 2023). Additionally, some scholars use text mining and principal component analysis to construct a fintech application index (Yao and Song, 2023).
In addition to using search engines, some studies measure the fintech index using documents such as annual reports, databases, and news information. For example, one study used text mining to analyse information in the annual reports of commercial banks and Python to crawl text data and calculate the frequency of keywords to build a bank fintech index (Wu et al. 2023). Another study used content analysis techniques by dividing fintech into five dimensions with specific keywords, coding each bank’s report by year, and building a fintech index based on the occurrence of keywords (Sajid et al. 2023). Another article exploited keywords through text mining techniques to capture text information from databases and calculate keyword occurrences to build a bank fintech index (Ni et al. 2023).
Fintech and bank risk research
The rise of fintech, driven by digital transformation and rapid technological advancements, has significantly influenced bank risk in multiple dimensions, including insolvency risk, credit risk, liquidity risk, and market risk. Understanding and adapting to these changes is crucial as financial institutions integrate digital innovations and navigate evolving risks shaped by shifting global market conditions and socioeconomic changes (Verhoef et al. 2021). Previous research on the interaction between fintech risk and banking risk has identified different types of risk, which can be further categorized into specific areas such as insolvency risk, credit risk, liquidity risk, and market risk.
Insolvency risk
Fintech enables banks to enhance efficiency, reduce operational costs, and expand financial inclusion. However, the rapid digitalization of banking services and heightened competition from fintech firms and nonbank entities exert pressure on traditional banking models. Continuous and substantial technological investments and adaptation to disruptive innovations are more urgent than ever to maintain profitability, particularly for banks that are unable to compete effectively. Moreover, external shocks, such as pandemics and economic downturns, further increase the insolvency risk by disrupting traditional revenue streams and unpredictably accelerating digital transformation (Papadopoulos et al. 2021).
The primary subtheme within insolvency risk is the Z score, a prominent measure used in fourteen studies. The Z score comprehensively assesses a bank’s financial stability by combining various financial ratios to predict the likelihood of insolvency. It is an essential tool for evaluating the impact of fintech innovation on financial stability (Li, 2023; Wan and Luo, 2023) and has been widely adopted in the literature as a leading indicator of bank risk-taking, reflecting overall risks from both interest-based and noninterest business activities (Banna et al. 2021; Chen et al. 2022; Chen and Shen, 2023; Deng et al. 2021; Gao et al. 2023; He et al. 2020, 2023; Hu et al. 2022; Li et al. 2022b; Sajid et al. 2023; Wang et al. 2021; Wu et al. 2023; Zhao et al. 2023). Higher Z scores indicate less risk exposure and greater bank stability, whereas lower Z scores suggest greater risk-taking and lower stability (Banna et al. 2021; He et al. 2023).
Another subtheme of insolvency risk is economic capital (EC), which was used in two articles. EC directly reflects a bank’s overall risk and facilitates comprehensive risk management. When risk levels are high, the demand for economic capital increases; conversely, when risk levels decrease, the demand for economic capital decreases accordingly (Yao and Song, 2021, 2023). Additionally, the risk-weighted asset ratio (RWAR) was used in two studies to measure insolvency risk. RWAR assesses the proportion of high-risk assets held by banks and serves as a proxy for proactive risk-taking behaviour (Zhao et al. 2023). One study employed seven indicators to measure bank risk-taking, including RWAR, an indirect measure of RWAR (RWAR2), credit risk-weighted assets (CRWA), market risk-weighted assets (MRWA), operational risk-weighted assets (ORWA), transactional financial assets, and fixed assets. This comprehensive approach ensures robust results by calculating the indirect measure of RWAR as total equity/capital adequacy ratio/total assets, indicating that a higher RWAR signifies a greater degree of proactive risk-taking by commercial banks. Risk-weighted assets include credit, market, and operational risk-weighted assets, in addition to trading financial assets, loans, and fixed assets (Liang et al. 2023).
Credit Risk
The proliferation of fintech-driven credit assessment tools, powered by big data and artificial intelligence, has reshaped lending practices. While these innovations improve risk modelling and expand access to credit, they also introduce new vulnerabilities that should be approached with caution. The reliance on alternative data sources and automated decision-making may lead to an underestimation of borrower risk, particularly in volatile economic conditions. Additionally, digital lending platforms and peer-to-peer lending expose banks to counterparty risk, which increases the complexity of credit risk management (Kraus et al. 2022).
A significant subtheme in credit risk is the nonperforming loan (NPL) ratio, which was used to measure credit risk in fourteen studies (He et al. 2020). NPL is a category of borrowers who fail to make monthly principal and interest payments over time (Khan et al. 2021). The NPL ratio directly measures a bank’s loan portfolio quality and the level of credit risk. Since a bank’s credit business is directly related to its NPL, it is reasonable to use the NPL as a proxy variable to measure the risk of commercial banks (Ni et al. 2023). A greater NPL value indicates greater risk (Cheng and Qu, 2020). In addition, several studies have revealed a relationship between digital transformation and the NPL: for banks with a high level of digital transformation, there is a negative relationship between digital transformation and the NPL, whereas for banks with a low level of digital transformation, the relationship is positive (Cao et al. 2022). Therefore, the NPL ratio is a crucial indicator for evaluating the effectiveness of credit risk management, especially in relation to changes driven by financial technology in lending practices (Chen et al. 2022; Li et al. 2022b).
The loan loss reserve ratio (LLR) subtheme was mentioned four times. One article employed the LLR to measure credit risk and defined it as the ratio of loan loss reserves to total loans (Cheng and Qu, 2020). The LLR is calculated as the ratio of a bank’s loan loss reserve to net total loans, with a higher value indicating greater credit risk (Wang et al. 2021). Another article used the nonperforming loan provision coverage ratio (NPLP) as a robustness test for NPL (Zhang et al. 2023). The NPLP coverage ratio is crucial for assessing financial health and the effectiveness of credit risk management. It plays a significant role in enhancing investor confidence and facilitating the comparability of performance among banking institutions. Additionally, it ensures regulatory compliance and provides a secure framework for banking operations and adherence to industry standards.
Liquidity risk
The rise of digital banking and payment innovations has reshaped customer behaviour and accelerated the speed of fund movements. The convenience of digital transactions and mobile banking may lead to a surge in liquidity volatility as deposit withdrawals become more immediate and less predictable. Additionally, banks that are involved in fintech partnerships or digital asset trading may face heightened liquidity pressures, particularly during financial crises, when customers and investors react swiftly to perceived risk (Škare et al. 2021).
From a liquidity risk perspective, the liquidity ratio (LIQ) is a central theme that was highlighted in three studies (He et al. 2020; Tran et al. 2022; Wan and Luo, 2023). The liquidity ratio measures a bank’s ability to meet its short-term obligations without incurring significant losses, and its importance increases as fintech advances promote faster and more efficient transactions. Robust liquidity management ensures smooth operations and maintains customer trust (Khan et al. 2021). A higher liquidity ratio indicates lower liquidity risk, making it a crucial indicator of bank liquidity (Tran et al. 2022). Additionally, the loan-to-deposit ratio (LD) is another important measure of liquidity risk that was mentioned in two studies (Li et al. 2022a; Wu et al. 2023). The LD ratio is calculated as total deposits divided by total loans and provides insight into a bank’s liquidity management (Wu et al. 2023). A higher LD ratio indicates a bank’s reliance on stable funding sources, further highlighting its ability to manage liquidity risk effectively (Li et al. 2022a).
Market risk
Fintech innovations, such as algorithmic trading and blockchain-based financial instruments, are transforming financial markets. While these technologies improve efficiency and market access, they also introduce new systemic risks due to increased market interconnectivity and reliance on digital platforms. The resulting price fluctuations in fintech-driven financial products and macroeconomic uncertainties such as inflation and recession risk add complexity to market risk management. This complexity underscores the need for adaptability and continuous learning in risk management practices (Xie et al. 2022).
Two articles used annual value at risk (VaR) to measure market risk and bank risk management (Chen and Shen, 2023; Zouari-Hadiji, 2023). VaR utilizes financial market indicators, such as stock prices and returns, to assess the dependence between banks and markets and to identify systemically important banks. Additionally, two articles employed marginal expected shortfall (ES) to measure market risk (Chen and Shen, 2023; Li, 2023). ES is calculated using industry returns weighted by market capitalization and total market value and provides a detailed measure of potential losses under extreme market conditions (Li, 2023). ES and VaR are important for assessing potential losses due to market volatility and extreme events. These measures are particularly relevant in fintech as innovations often introduce new market dynamics and risks that require careful monitoring and management. Their ability to measure potential losses under different scenarios makes them indispensable for understanding and mitigating market risk in the rapidly evolving financial landscape (Deng et al. 2021; Wu et al. 2023).
In addition to the direct relationship studies between fintech and bank risk, a substantial body of research has examined the mediating and moderating mechanisms. Six studies focused on mediating mechanisms, six focused on moderating mechanisms, and two comprehensively covered both aspects (Table 8), providing a wealth of knowledge for our understanding of this complex relationship.
With regard to insolvency risk, studies have highlighted the significant impact of fintech. Li et al. (2022b) tested the mediating effects of operating income (INC) and the capital adequacy ratio (CAR) on the relationship between fintech risk and insolvency risk and concluded that fintech reduces banks’ risk-taking by improving these financial metrics. This improvement provides banks with a buffer against potential financial shocks, thereby reducing insolvency risk. Additionally, Sajid et al. (2023) investigated operational efficiency (OE) as a mediating variable and reported that fintech innovations improve the operational efficiency of banks, which in turn reduces insolvency risk. These findings suggest that fintech products can drive growth in the banking industry by making banks more efficient and less prone to insolvency. Deng et al. (2021) used governance (Gov), bank competition intensity (HHI), and per capita savings (PCS) as mediating variables. Their findings indicated that fintech influences bank risk-taking by affecting market competition and residents’ willingness to save. Improved governance structures ensure better oversight and management of fintech initiatives, which reduces insolvency risk. Increased competition due to fintech can lead to more prudent risk management practices among banks. Furthermore, higher per capita savings driven by fintech-enhanced financial inclusion, can provide banks with a more stable deposit base, thereby reducing insolvency risk.
Gao et al. (2023) used the bank competition level (BCMP) as a moderating variable. Their results showed that a higher bank competition level (BCMP) motivated banks to prefer aggressive investment strategies, that off-balance-sheet business innovation (OBI) may hinder the transmission path of risk management changes and operating efficiency, and that bank competition may lead to further accumulation of banking risk, thereby reducing the inhibitory effect of off-balance-sheet business innovation (OBI) on bank risk-taking (BRT). In addition, Gao et al. (2023) used bank agency cost (BAC) as a mediating variable. Their results showed that lower BACs improved profitability. Reducing profitability pressure subsequently motivates banks to reduce transfer risk to depositors. With the support of emerging technologies, bank loan risk can be effectively identified and monitored, enabling banks to control BRT more effectively and reducing insolvency risk.
In the context of credit risk, several studies have examined ILR and NIM as mediating variables. Li et al. (2022b) explored the mediating role of the Loan impairment to total loan ratio (ILR) in the relationship between financial technology and credit risk. ILR measures the share of interest income compared to total loans and indicates the bank’s revenue efficiency from lending activities. Fintech innovations, such as enhanced data analysis and automated credit scoring, are revolutionizing the efficiency and accuracy of the loan approval process. This transformation is leading to a better loan portfolio and a better risk-return profile. As fintech empowers banks to evaluate and manage loans more effectively, ILR can increase, indicating higher interest income from loan portfolios, better lending efficiency and potentially lower credit risk. By examining ILR as a mediating variable, Li et al. (2022b) showed that fintech’s impact on credit risk is channelled partially through its impact on bank income from lending activities.
Deng et al. (2021) used the net interest margin (NIM) as a mediating variable to explore the relationship between fintech and credit risk. NIM, which represents the difference between the interest income generated by banks and the amount of interest paid out to their lenders relative to the amount of their interest-earning assets, serves as a critical measure of a bank’s profitability and efficiency in managing its assets and liabilities. Deng et al. (2021) posited that fintech innovations can enhance a bank’s operational efficiency and decision-making processes, thereby improving NIM. For example, fintech solutions such as advanced data analytics and automated credit assessment tools can lead to more accurate loan pricing and better management of interest rate spreads. An improved NIM suggests that the bank is generating higher interest income relative to its interest expenses, which reflects better financial health and reduced credit risk. By examining NIM as a mediating variable, Deng et al. (2021) demonstrated that fintech’s influence on credit risk is partially conveyed through its impact on NIM, which highlights how technological advancements can increase financial performance and stability by improving banks’ profitability metrics.
With regard to liquidity risk, Li et al. (2022a) examined the mediating effect of the capital adequacy ratio (CAR). They found that fintech innovation increases CAR and indirectly improves liquidity management. Fintech solutions, such as blockchain for transparent transactions and AI-driven predictive analytics, help banks optimize their capital allocation and maintain adequate capital buffers. For example, blockchain technology can streamline back-office operations and reduce transaction time and costs, whereas AI can predict cash flow needs to allow banks to allocate capital more efficiently. An enhanced CAR means that banks have a more substantial capital base to absorb potential losses and meet regulatory requirements. Efficient operations and robust capital buffers enable banks to meet short-term obligations without incurring significant losses, thereby maintaining liquidity. The robust findings of a study by Wu et al. (2024) strongly support the idea that fintech-driven increases in CAR lead to better liquidity management by ensuring that banks are well capitalized and can effectively deal with liquidity shocks. This shows how technological advances contribute to financial stability by strengthening key metrics that support effective liquidity management.
The analysis of the moderating variables reveals several insights into the relationship between fintech and different types of bank risk. With regard to market risk, Li (2023) concluded that banks’ digital transformation (DT) and the digital divide (DD) effectively moderate the impact of fintech. Banks with higher levels of digitalization can better manage the risks introduced by fintech, whereas those with lower levels of digitalization face increased market risk. This finding indicates that the digital divide within the banking industry can exacerbate market risk for less digitalized banks. Further research by Zhu and Jin (2023) supports the idea that digital transformation can significantly reduce the probability of bank failure by improving operational efficiency and risk management capabilities.
With respect to credit risk, Ni et al. (2023) examined financial regulation (FRI) as a moderating variable. Their findings suggest that financial regulation can effectively dampen the adverse effects of fintech on bank risk, thereby mitigating the exacerbating impacts of fintech on credit risk. Liang et al. (2023) explored the role of the capital adequacy ratio (CAR) and loan-to-asset ratio (LC) as moderating variables. Their study revealed that a robust CAR strengthens the fintech-credit risk relationship by ensuring that banks have enough capital to absorb potential losses from fintech-induced risk. Moreover, a stable LC helps manage credit risk by maintaining a balanced loan portfolio, which is crucial to mitigate the risks associated with fintech developments. According to Bouteska et al. (2022), stringent financial regulations can create a buffer that prevents excessive risk-taking by banks, which ensures more stable credit risk profiles in the face of fintech developments.
With regard to liquidity risk, Wang et al. (2023) investigated the role of economic uncertainty (EPU) as a moderating variable and reported that it plays a significant role in shaping the relationship between fintech risk and bank risk. Economic uncertainty can amplify the liquidity risks associated with fintech, highlighting the importance of considering broader economic conditions when assessing fintech’s impact on bank risk. Wu et al. (2024) suggested that banks with greater exposure to fintech during economic downturns may face greater liquidity pressures due to sudden shifts in consumer behaviour and market dynamics.
Understanding mediation and moderation mechanisms is crucial when analysing the relationship between fintech and different types of bank risk. The mediating mechanisms reveal that fintech innovations significantly impact the risk profile of the banking sector and bring potential benefits, such as improving operational efficiency, increasing operating income and capital adequacy, structuring liabilities, and shaping market competition and savings behaviour. For instance, fintech reduces banks’ risk-taking by increasing operating income (INC), which provides banks with a buffer against potential financial shocks. Operating efficiency (OE) as a mediating variable also increases the operational efficiency of banks, leading to reduced insolvency risk. This insight underscores the need to consider direct and indirect effects when assessing the influence of fintech on bank risk and offers promising prospects for the future of banking.
Moderating factors such as digital transformation (DT), the digital divide (DD), macroeconomic uncertainty (EPU), financial regulation (FRI), macroprudential policy (MP), and economic uncertainty also significantly influence the relationship between fintech and different types of bank risk. For market risk, banks with a higher level of digitization can better manage the risks introduced by fintech, whereas those with a lower level of digitization face increased market risk due to the digital divide. With respect to credit risk, the capital adequacy ratio (CAR) and loan-to-asset ratio (LC) reinforce the fintech credit risk relationship by ensuring that banks have sufficient capital to absorb potential losses and maintain a balanced loan portfolio, respectively. Economic uncertainty increases the liquidity risk associated with fintech, making it important to consider broader economic conditions when assessing the impact of fintech on bank risk. The findings suggest that understanding these moderating effects is vital for developing strategies to manage and mitigate the risks associated with fintech innovation. The importance and urgency of addressing this dynamic cannot be overstated, as it has significant implications for the future of banking and financial stability. Further research and policy interventions are necessary to achieve a balanced approach that promotes fintech growth while ensuring the stability of the banking sector. The emphasis on potential risks and the need for immediate action underscore the seriousness of the situation and the critical need for the involvement of all stakeholders.
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