It aims to determine whether the expansion of P2P lending platforms poses risks to the stability of traditional banking or whether it complements and strengthens the overall financial sy
Empirical Studies on P2P lending and Banking stability
Recent analyses of banking stability encompass insights from various researchers, including Nier (2005), Beck (2013), Swamy (2014), Ozili (2018), and Shahriar (2022), alongside evaluations by financial institutions such as the International Monetary Fund and the World Bank (2023).
Empirical studies on banking sector stability utilize diverse methodologies and datasets to identify key factors influencing stability According to Pham et al (2021), indicators like the equity-to-asset ratio, bank size, loans-to-assets ratio, and revenue diversification positively affect bank stability In contrast, factors such as the market share of mobilized capital, loan loss provisions, and market structure negatively impact the stability of banks.
In the study "Research on the Impact of Bank Competition on Stability—Empirical Evidence from 4631 Banks in the US," Yuan et al (2022) explored the relationship between bank competition and banking stability Their findings indicate that competition serves as a vital environment for industries, with bank competition influencing stability primarily through franchise value, borrowing costs, and operational behaviors Notably, the study identifies an inverted U-shaped relationship between bank competition and stability.
A study by Abbas et al (2022) analyzed the impact of GDP growth on the financial stability of Pakistan's banking sector The findings reveal that GDP growth significantly influences the stability of the financial sector, with all three proxies used in the research showing statistical significance This suggests that higher GDP is correlated with enhanced bank stability in Pakistan.
A study by Jun and Yeo (2018) examined the effects of peer-to-peer (P2P) lending platforms on financial stability, focusing on banks and the banking system The findings revealed that when traditional banks compete with P2P platforms for consumers with low credit scores, the risk of insolvency for individual banks rises Conversely, the study noted a decrease in the illiquidity risk for these banks.
16 the presence of P2P lending platforms in these markets also contributes to a reduction in systemic risk within the overall banking system, stemming from potential defaults by individual banks.
A study by Jun and Yeo (2020) investigated the impact of peer-to-peer (P2P) lending on bank risks, highlighting the importance of clearly defining the roles of banks and P2P platforms for sustainable growth The research indicates that P2P lending platforms should focus on the low-credit segment, while banks should have a limited role in P2P lending This strategy is essential to prevent any adverse effects on the stability of banks as P2P lending expands.
Despite extensive research on the impact of peer-to-peer (P2P) lending on banking stability, evidence of a direct relationship remains scarce Traditional borrowers often face strict regulations from commercial banks, leading to potential loan denials In contrast, advancements in IT have sparked innovation within the financial services market, creating new borrowing opportunities outside conventional institutions P2P platforms have emerged as popular alternatives, providing individuals with a broader range of lending options Yeo and Jun (2020) note a consistent increase in P2P borrowing, particularly in select countries Foo et al (2017) highlight P2P lending as a rapidly growing fintech trend that may challenge traditional retail banking Guo et al (2016) describe P2P lending as an efficient, convenient, and affordable online platform, allowing individual lenders to connect with borrowers independently of traditional banks.
In 2019, Cornaggia, Wolfe, and Yoo examined the impact of peer-to-peer (P2P) lending on the profitability of small banks in the United States, revealing that heightened P2P lending activity adversely affected these banks' financial performance.
Research indicates that P2P lending platforms significantly threaten smaller banks by affecting their market share and profitability A comprehensive analysis by Cole, Cumming, and Taylor (2019) revealed that P2P lending negatively impacts the market concentration of traditional banks, enhancing competition and leading to improved efficiency and better loan pricing for borrowers Additionally, Tang, Huan (2019) found that the rise of P2P lending in China resulted in decreased bank lending and heightened competition for loans, suggesting that these platforms act as substitutes for traditional banks Collectively, these studies demonstrate that the growth of P2P lending is reshaping bank competition by introducing alternative lending options and influencing the profitability of traditional banks.
Competition is a fundamental element in various industries, particularly in the banking sector, where it has intensified in response to a complex economic landscape Research by Goetz (2017) indicates that heightened competition among banks leads to a lower likelihood of failures and a reduction in non-performing loans, while simultaneously boosting profitability These insights imply that increased competition enhances overall stability in the banking industry by improving both profitability and asset quality.
Koetter et al (2012) identify two key theories regarding banking competition and stability: the stability theory of competition, which posits that increased competition among banks enhances their stability, and the fragility theory of competition, which argues that heightened competition leads to lower interest rates, thereby mitigating moral hazard and adverse selection issues This dynamic allows banks to issue more loans while reducing default rates, ultimately contributing to their overall stability Additionally, Diamond (1984) supports the notion that increased competition can further bolster the resilience of banks.
Increased competition in the banking sector can enhance stability by promoting better monitoring and borrower selection This heightened competition encourages banks to be more vigilant in their operations and cautious in their lending practices Additionally, as competition intensifies, borrowers benefit from lower interest rates, which reduces their incentive to engage in risky behavior This dynamic ultimately leads to a decrease in default risk, contributing to improved financial stability (Boyd and De Nicolo, 2005).
Increased bank competition can negatively impact stability by pushing institutions to engage in riskier activities to maintain market share As banks strive for higher profits and customer attraction, they may loosen lending standards and take on excessive risks, leading to a detrimental relationship between competition and stability Research by Beck et al (2013) indicates that heightened competition diminishes bank stability, particularly in countries with stringent regulations and developed financial markets, compelling banks to make risky decisions to remain competitive.
The rise of P2P lending has significant implications for bank competition, potentially supporting the theory of increased bank stability Research by Wang et al (2015) indicates that P2P lending platforms foster competition in the banking sector, prompting traditional banks to improve their efficiency and risk management practices In response to competitive pressures, banks are adopting more prudent lending standards, diversifying their loan portfolios, and enhancing their risk assessment processes.
Conversely, the theory of reducing bank stability or increasing fragility may also find support due to P2P lending According to a report by the Financial Stability Board
The rapid expansion of P2P lending platforms raises significant risks to financial stability, as noted in a 2017 report by the FSB Key concerns include the misalignment of incentives, insufficient risk management practices, and the potential for contagion effects resulting from platform failures, all of which could contribute to greater fragility within the banking system.
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Factors Affecting Banking Stability chi, 20
Peer-to-peer (P2P) lending, often referred to as FinTech credit, crowd-finance, or marketplace lending, facilitates direct connections between investors and borrowers via online platforms These innovative platforms serve as nonbank financial intermediaries, enabling users to split loans into payment-dependent notes and streamline the lending process.
P2P lending serves as a catalyst for diversification and competition in the financial sector, encouraging traditional banks to enhance their services and improve cost efficiency This competitive environment fosters stronger risk management practices, contributing to a more resilient banking system and overall financial stability Additionally, P2P lending promotes financial inclusion by offering access to credit for individuals and small businesses that may face challenges in securing loans from conventional banks.
21 traditional banks This can stimulate economic growth and reduce vulnerabilities associated with underserved populations.
P2P lending presents significant challenges and risks that require careful attention One major concern is credit risk, as the lack of traditional underwriting can lead to higher default rates, potentially impacting investors' financial health and the stability of banks acting as custodians Additionally, the P2P lending industry faces regulatory challenges, with inconsistent oversight across jurisdictions that may compromise investor protection and risk management To address these issues, establishing consistent regulations is crucial for maintaining stability Furthermore, liquidity risk is a factor, as P2P investments may not offer the same liquidity as traditional bank deposits, which can strain platforms during economic downturns when investors seek to withdraw their funds.
As the P2P lending industry expands and integrates with the wider financial system, it poses a potential risk to systemic stability, especially if lending or borrowing activities become concentrated on these platforms.
Banking concentration, or market concentration in the banking sector, indicates the extent of market control exerted by a limited number of banks within a specific region This concentration impacts competition, financial stability, and consumer welfare significantly Research by Demirgic-Kunt and Detragiache (1998) highlights that elevated banking concentration correlates with heightened financial fragility and a greater likelihood of banking crises across various countries.
The relationship between banking stability and banking concentration is a multifaceted one, with both positive and negative aspects On the positive side, a oa
A certain degree of banking concentration can enhance stability by allowing larger banks to diversify risks and leverage economies of scale This diversification reduces the effects of localized economic challenges and sector-specific issues Furthermore, larger banks typically enjoy cost efficiencies that boost profitability and resilience Additionally, concentrated banking systems can improve regulatory oversight, fostering better risk management practices and contributing to overall stability.
High banking concentration poses significant risks, primarily the systemic threat of large banks becoming "too big to fail," which could lead to catastrophic consequences for the financial system This excessive concentration diminishes competition in the banking sector, often resulting in less favorable terms and services for consumers Additionally, concentrated banking systems are more susceptible to external shocks, including economic crises and regulatory changes, further exacerbating instability.
The relationship between banking stability and concentration varies by country and is influenced by factors such as economic size, diversity, regulatory effectiveness, and banking sector structure To maintain financial stability, it is crucial to implement effective regulatory oversight and prudential measures that balance the advantages and risks associated with banking concentration Achieving the optimal balance between concentration and stability is a complex challenge that necessitates thorough consideration of specific country factors and regulatory protections.
Private credit by deposit money banks to GDP
The private credit-to-GDP ratio is a key indicator that measures the extent of credit provided by deposit money banks to the private sector in relation to the overall economy This metric highlights the significance of bank lending in supporting economic activities and offers valuable insights into the depth and accessibility of credit within a nation Furthermore, the interplay between private credit by deposit money banks and banking stability is intricate and evolving, underscoring the importance of monitoring this ratio for understanding financial health.
23 critical indicator of a country's financial landscape and its potential impact on the stability of its banking sector.
A high private credit-to-GDP ratio indicates that a significant share of a country's economic activity relies on bank lending, which can foster growth but also poses risks to banking stability Key concerns include credit risk, where substantial lending increases exposure to loan defaults, particularly during economic downturns, leading to non-performing loans that can erode banks' capital Asset quality is crucial, as a large volume of loans necessitates maintaining high asset standards; distressed loans can result in write-offs that harm financial health This ratio is also influenced by economic cycles, with borrowers more likely to repay during prosperous times, but default rates rising in downturns Additionally, liquidity risk arises from the need for banks to manage sufficient liquidity for withdrawals and potential loan losses, as poor liquidity management can trigger crises Systemic risk is another issue, where widespread lending to a specific sector can result in simultaneous defaults across multiple banks, potentially leading to a financial crisis.
Inflation is the ongoing rise in the general price level of goods and services within an economy, measured as an annual percentage change in price indices like the Consumer Price Index (CPI).
The Producer Price Index (PPI) indicates that inflation significantly impacts banking stability, with high or volatile rates posing risks to banks' profitability and asset quality Increased inflation diminishes the value of interest income, which constrains banks' ability to achieve sustainable profits, as highlighted by research from Barth, Caprio, and Levine (2004).
Inflation significantly impacts banking stability by eroding purchasing power and increasing the risk of non-performing loans As prices rise, borrowers face challenges in repaying loans, which can degrade banks' asset quality Additionally, central banks often combat inflation by raising interest rates, leading to higher borrowing costs for banks and reduced loan demand from consumers and businesses This combination of factors can pressure banks' profitability and restrict their lending activities, ultimately affecting their overall stability.
Inflation can lead to an asset-liability mismatch for banks, as the real value of long-term fixed-rate assets, such as mortgages, may decline while short-term liabilities remain stable This discrepancy exposes banks to potential losses and increased volatility Additionally, inflation-driven economic uncertainty can diminish consumer and business confidence, resulting in decreased borrowing and investment In response, banks may take a more cautious lending approach, which can contribute to economic slowdown and further impact their stability.
Economic growth refers to the increase in the production of goods and services over a specific timeframe, typically measured by indicators like Gross Domestic Product (GDP) or Gross National Product (GNP).
It is a critical indicator of a country's economic health and can lead to higher employment rates, improved living standards, and increased investment. (Tamplin, 2023)
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Next, in order to determine the correlation between banking stability and P2P lending across countries, we need to conduct a Pearson correlation test.
Source: Author's Stata processing results.
The Pearson correlation analysis indicates that the relationships between PcdmGDP and banking stability, as well as inflation and banking stability, are statistically significant, with p-values below 0.05 In contrast, the correlations involving bac, Inpp, gdpgrowth, and wpui with banking stability show p-values exceeding 0.05, suggesting these relationships are not statistically significant and can be excluded from consideration.
The analysis revealed that there is no linear correlation among the independent variables, as indicated by a significance level (Sig) greater than 0.05 and a correlation coefficient (r) of 0 This finding confirms that the pairs of independent variables, including bac and stability, Inpp and stability, gdpgrowth and stability, and wpui and stability, lack any linear relationships.
4.3 Analysis Linear regression (Pooled ols)
The author conducted a multiple regression analysis to examine the strong relationship between the dependent variable, Stability, and six independent variables, as represented in the regression equation.
Stability= P0 + B1*bac + B2*PcdmGDP + 3*lnpp + B4*inflation + B5*gdpgrowth + BO*wpui
Table 4.4: Linear regression (Pooled ols)
Source: Author's Stata processing results. stability | Coef | St.Err | t-value | p-value | [95% Conf | Interval] | Sig bac -.002 | 003 -0.72 47 -.007 003
Table 4.5: Linear regression (Pooled ols) results
Source: Author's Stata processing results. Mean dependent var 2.910 SD dependent var 0.496
Akaike crit (AIC) 289.216 Bayesian crit (BIC) 312.477
The adjusted R-squared value of 0.083 reveals that the independent variables in the regression analysis explain only about 8.3% of the variability in the dependent variable, leaving 91.7% of the variability due to external factors and random error This indicates that the model fails to account for a significant portion of the variation in the dependent variable.
The standardized regression equation for the study, after conducting the necessary checks and tests, can be presented as follows:
Stability = 3.18 — 0.002 * bac + 0.004 * PcdmGDP -0.022 * Inpp -0.007 * inflation + 0.001 * gdpgrowth -0.004*wpui.
The variable PcdmGDP exhibits a positive correlation with the variable Stability, indicated by a positive regression coefficient (8) in the regression equation.
The regression coefficient for the constant (Constant) is 3.18 and is highly statistically significant (p-value < 0.01), indicating its significance in the model.
The variables bac, Inpp, inflation, gdpgrowth, and wpui are statistically significant predictors of banking stability, as evidenced by their p-values being less than 0.05 This indicates a strong relationship between these factors and banking stability in the analyzed model.
We performed a multicollinearity test, analyzing the results shown in Table 4.6, which details the Variance Inflation Factor (VIF) This assessment helped identify the presence of multicollinearity among the independent variables in our regression model.
Table 4.6: Variance Inflation Factor (VIF)
Source: Author's Stata processing results. VIF 1/VIF gdpgrowth 2.39 418 wpui 2.289 437
The Variance Inflation Factor (VIF) is a crucial tool for detecting multicollinearity in statistical models Research by Hoang Trong and Chu Nguyen Mong Ngoc (2005) indicates that a VIF value exceeding 10 signals significant multicollinearity, while a VIF below 2 suggests that multicollinearity is not a concern among the independent variables.
The research findings revealed that all VIF coefficients were below 2, confirming the absence of multicollinearity in the regression model This indicates that the independent variables do not strongly correlate with one another, allowing for reliable interpretation of the regression coefficients The lack of multicollinearity enhances the model's stability and accuracy, facilitating more meaningful insights into the relationship between independent and dependent variables.
4.4 Analysis Random-effects GLS regression (REM)
Table 4.7: Random-effects GLS regression
Source: Author's Stata processing results. stability | Coef | St.Err | t-value | p-value | [95% Conf | Interval] | Sig bac -.002 002 -1.00 318 -.006 002
Inpp 007 004 2.07 038 0 014 we inflation 003 005 0.65 518 -.006 012 gdpgrowth | 007 003 2.55 011 002 013 we wpui 0 001 0.05 962 -.002 002
The coefficient for bac is -0.002, indicating a negative relationship between stability and the model's variables However, this relationship lacks statistical significance (p > 0.1), suggesting insufficient evidence to confirm the impact of these variables on banking stability.
The coefficient for PcdmGDP is -0.001, implying a negative association betweenPcdmGDP and stability However, this relationship is not statistically significant (p >
0.1), indicating that changes in PcdmGDP are not significantly related to banking stability.
The coefficient for Lnpp is 0.007, reflecting a statistically significant positive relationship with banking stability at the 0.05 level This indicates that higher Lnpp values are associated with enhanced stability in the banking sector.
The inflation coefficient is 0.003 and lacks statistical significance (p > 0.1), indicating that inflation does not significantly affect banking stability according to the variables analyzed in the model.
The coefficient for GDP growth is 0.007, demonstrating a statistically significant positive relationship with banking stability at the 0.05 level This indicates that increased GDP growth correlates with enhanced stability in the banking sector.
The coefficient for Wpui is zero, indicating a lack of relationship with banking stability Furthermore, this variable is not statistically significant (p > 0.1), suggesting it does not significantly affect banking stability.
Table 4.8: Random-effects GLS regression results (REM)
Source: Author's Stata processing results. Mean dependent var 2.910 SD dependent var 0.496
Overall r-squared 0.040 Number of obs 205
Overall, the model has a low R-squared value of 0.040, indicating that the included variables explain only a small portion of the variation in stability.
The analysis suggests that Lnpp and gdpgrowth are the only variables that show a significant relationship with stability.
4.5 Analysis Fixed-effects (within) regression (FEM)
Table 4.9: Fixed-effects (within) regression (FEM)
Source: Author's Stata processing results. stability | Coef | St.Err | t-value | p-value | [95% Conf | Interval] | Sig bac -.003 | 002 -1.38 168 -.007 001
Inpp 007 004 1.90 059 0 014 * inflation 003 005 0.70 484 -.006 012 gdpgrowth | 006 003 2.19 03 001 012 aad wpui 0 001 0.09 925 -.002 002
The variable "PcdmGDP" has a negatively significant impact on the dependent variable with an estimated coefficient of -0.003.
Similarly, the variable "gdpgrowth" also has a statistically significant impact on the dependent variable with an estimated coefficient of 0.006.
The variables "stability," "Inpp," "inflation," and "wpui" do not significantly influence the dependent variable, indicating that variations in these factors do not meaningfully affect the outcome when other variables are controlled.
Table 4.10: Fixed-effects (within) regression results(FEM)
Source: Author's Stata processing results. Mean dependent var 2.910 SD dependent var 0.496
Akaike crit (AIC) -531.150 Bayesian crit (BIC) -507.889
F-test: The F-statistic is 6.063, and the p-value is 0.000 F) is 0.0012 (< 0.05).
Based on the p-value, we can conclude the following:
At a significance level of 5%, the p-value is less than 0.05, providing sufficient statistical evidence to reject the null hypothesis of no first-order autocorrelation in the dataset.
The presence of first-order autocorrelation in the panel data must be acknowledged and addressed in subsequent analyses to maintain the accuracy of the results.
In conclusion, the Wooldridge test result suggests the presence of first-order autocorrelation in the panel data, and addressing this autocorrelation will be necessary for the data analysis process.
The regression analysis, correlation testing, and assumption checks confirm that the model is suitable, effectively addressing its limitations and revealing variable correlations The findings highlight that "Inpp" and "gdpgrowth" significantly impact the dependent variable, while other variables lack statistical significance The selection of the random-effects model is validated by the Hausman test, though it is crucial to address first-order autocorrelation in the panel data to enhance the robustness of the analysis.
My research on banking stability and P2P lending reveals notable differences compared to the study by Tram and Nguyen (2020), which examined factors influencing the stability of commercial banks in Vietnam Their findings indicate that operating expenses, economic growth, and inflation positively affect banking stability, aligning with my study's conclusion that GDP growth is a significant factor Both studies emphasize the importance of diversifying income sources, which correlates positively with banking stability However, my research uniquely explores the global implications of P2P lending, whereas Tram and Nguyen focus specifically on Vietnam's banking context Ultimately, both studies offer valuable insights into enhancing banking stability, albeit from different perspectives.
My study uniquely examines the impact of P2P lending on banking stability, contrasting with other research that investigates a wider array of factors such as bank competition, market structure, loan loss provisions, and the equity-to-asset ratio Additionally, the methodologies differ, as my research utilizes statistical regression analysis to analyze the relationship between P2P lending, GDP growth, and banking stability, while other studies may employ panel data analysis, econometric models, or case studies to delve into the complexities of banking stability.
This research examines the influence of P2P lending on banking stability, beginning with a clear identification of the research problem and a synthesis of existing literature It outlines specific objectives and proposes solutions to enhance P2P lending Additionally, GDP growth is considered as a significant factor in the analysis Employing a robust quantitative methodology, the study utilizes descriptive statistics, Pearson correlation tests, linear regression analysis, Random-effects GLS regression, Variance inflation factor, Fixed-effects regression, Hausman specification test, and the Wooldridge test for autocorrelation in panel data Data on P2P lending was sourced from the Cambridge Alternative Finance Benchmarks, and Z-scores were calculated to assess banking stability.
The research findings discussed reveal some correlations and relationships between banking stability, P2P lending, and GDP growth Firstly, the study by Jun and Yeo
The emergence of P2P lending platforms has significant implications for both individual banks and the overall banking system, particularly regarding insolvency risks for traditional banks competing in markets with consumers who have low credit scores While the risk of illiquidity for these banks decreases, the presence of P2P lending platforms helps to mitigate systemic risk within the banking sector Furthermore, research indicates a correlation between P2P lending and banking stability, highlighting the role of GDP growth in this dynamic, which is supported by the findings of Abbas et al (2022).
43 the significant role of GDP growth in determining the stability of the financial sector and its positive association with bank stability.
This study highlights the relationship between P2P lending, GDP growth, and banking stability It suggests that while P2P lending may impact the stability of individual banks, it also plays a role in reducing systemic risk within the financial market Furthermore, GDP growth is recognized as a vital determinant of overall financial sector stability, including the stability of banks.
Research indicates that GDP growth significantly influences banking stability, with P2P lending following closely To enhance this stability, the author recommends strengthening regulatory oversight with clear guidelines for P2P platforms, improving credit assessment processes, implementing borrower education initiatives, fostering collaboration among stakeholders, and conducting regular audits of P2P lending operations and financial health.
This research analyzes recent data on P2P lending from 2015 to 2020, utilizing a tailored research model It specifically investigates the impact of P2P lending on banking stability, an area that has been underexplored in prior studies Additionally, the study offers recommendations for policymakers, authorities, and commercial banks to effectively compete with P2P lending and enhance its operational conditions.
To improve the stability and sustainability of the P2P lending industry, it is essential to enhance regulatory oversight by establishing clear guidelines and standards for P2P lending platforms, which will foster transparency, fairness, and protect investors.
Enhancing the credit assessment and evaluation process is crucial for P2P lending platforms Implementing rigorous procedures to evaluate borrowers' creditworthiness and repayment ability allows lenders to utilize effective risk assessment methods This approach enables informed lending decisions and significantly reduces the risk of default.
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GDP Growth 8 44 5.3 Limitations of the research co 55 5 G5 SE 9 9 0 000000090 45
To enhance stability in the banking sector through GDP growth, it is essential for governments to adopt supportive fiscal and monetary policies that encourage sustainable economic development This includes maintaining stable inflation, managing public debt effectively, and implementing strategies to stimulate both investment and consumer spending By fostering a stable economic environment, banks can operate under improved conditions, ultimately contributing to greater stability in the financial system.
Promoting financial inclusion and access to credit is essential for economic growth Governments must focus on initiatives that broaden banking service access, create credit bureaus, and promote responsible lending By providing individuals and businesses with affordable credit options, economic activities can thrive, boosting GDP and strengthening the stability of the banking sector.
Encouraging innovation and technological advancements in the financial sector can significantly benefit the economy To support fintech companies and startups, governments and regulators must establish a conducive environment through supportive regulations Additionally, fostering collaboration between traditional banks and fintech firms, along with investing in digital infrastructure, is essential for driving growth in this dynamic industry.
45 advancements can increase efficiency, boost productivity, and stimulate economic growth, thereby benefiting banking stability.
Strengthening financial regulation and supervision is essential for maintaining the stability of the banking sector Governments must enhance regulatory frameworks, improve risk management, and ensure effective oversight of banks and financial institutions This includes monitoring capital adequacy, liquidity management, and compliance with anti-money laundering and cybersecurity regulations By promoting sound banking practices and maintaining regulatory vigilance, governments can significantly contribute to the overall stability of the financial system.
In today's interconnected global economy, fostering international cooperation is vital Governments must participate in international forums to align policies, share best practices, and tackle cross-border challenges Collaborative efforts can help mitigate risks, promote financial stability, and create a conducive environment for sustained GDP growth and banking stability.
To enhance the stability of the banking sector and promote sustainable GDP growth, it is essential for governments, regulators, and stakeholders to work together Collaboration among all parties is vital to prioritize initiatives that bolster economic stability, ensure financial inclusion, embrace technological advancements, and implement effective regulations.
The research encounters notable limitations, primarily due to challenges in data availability and reliability across various countries This inconsistency in obtaining comprehensive data hampers effective cross-country analysis Moreover, differences in data collection methods and reporting standards further affect the reliability and comparability of the findings.
Research on the impact of P2P lending on banking stability is limited, indicating a need for more comprehensive studies Current literature in this field is scarce, highlighting the necessity to further explore the relationship between P2P lending and the stability of banking institutions.
The limited research on P2P lending stems from its recent emergence compared to traditional banking, alongside its swift growth and changing landscape, which complicate the assessment of its long-term effects on banking stability Additionally, the intricate relationships among P2P lending platforms, conventional banks, and regulatory environments further complicate the research process.
Further research on P2P lending should focus on comparative analyses between the banking sector and P2P platforms across various countries, examining stability indicators like capital adequacy, liquidity ratios, default rates, and profitability to uncover differences in stability and associated risks Additionally, the regulatory environment significantly influences stability; therefore, studying the effectiveness, comprehensiveness, and enforcement of regulations for both traditional banking and P2P lending will offer insights into their stability levels, risk management practices, and investor protection.
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