RISK ANALYSIS OF BANKING FINTECH BUSINESS IN THE DIGITAL ECONOMY ERA: EVIDENCE FROM CHINA'S BANKING RESEARCH
Keywords:
Financial Risk, Financial Technology Business Risk Assessment, Quality Function Deployment, G1 Entropy Method, Index ImportanceAbstract
With the rapid development of the digital economy and the extensive promotion of financial technology, the digital transformation of banks has achieved remarkable results. The accompanying financial risks of banks can not be ignored and become a hot topic in the world. Therefore, this study creatively uses the Quality Function Deployment theory in marketing for reference, and combines the G1 entropy method in fuzzy mathematics for quantitative calculation, and deeply discusses the risk assessment of banking financial technology business in the digital economy era. As a technical means of logical analysis, the theory of Quality Function Deployment has certain frontiers and progressiveness. The quantitative calculation by G1 entropy method fully takes into account the subjective and objective factors, and combines the advantages of G1 method and entropy method, which can make the research conclusion objective and reasonable as a whole. More importantly, we applied the proposed theory and method to the risk assessment of banking fintech business, constructed 15 risk assessment indicators and ranked them in importance, and verified the scientific and rationality of the method against the background of actual bank data in China. In this study, we have realized the innovation in theory and the application in practice. In theory, we have studied the financial risk of banks from the perspective of interdisciplinary analysis. For the first time, we have applied the Quality Function Deployment theory to the risk assessment in the field of banking finance, which has promoted the development of the academic field of financial risk and enriched the literature in this field. On the practical level, it puts forward relevant opinions and suggestions on the risk management and control of bank fintech business, which provides reference for the Chinese government to formulate the risk management and control plan of bank fintech business, and also provides experience support for similar academic cases in the world.
References
Jiang H, Zhang J. Discovering systemic risks of China’s Listed Banks by CoVaR approach in the digital economy era[J]. Mathematics, 2020, 8(2): 180.
Hou X, Gao Z, Wang Q. Internet finance development and banking market discipline: Evidence from China[J]. Journal of Financial Stability, 2016, 22: 88-100.
Zhuo J, Li X, Yu C. How to integrate financial big data and fintech in a real application in banks: A case of the modeling of asset allocation for products based on data[J]. Information, 2020, 11(10): 460.
Xie C. Intelligent evaluation method of bank digital transformation credibility based on big data analysis[J]. Journal of Computational Methods in Sciences and Engineering, 2022 (Preprint): 1-11.
Kumari A, Devi N C. The Impact of FinTech and Blockchain Technologies on Banking and Financial Services[J]. Technology Innovation Management Review, 2022, 12(1/2).
Dvorski Lacković I, Kovšca V, Lacković Vincek Z. A review of selected aspects of big data usage in banks’ risk management[J]. Journal of Information and Organizational Sciences, 2020, 44(2): 317-330.
Peng H, Lin Y, Wu M. Bank Financial Risk Prediction Model Based on Big Data[J]. Scientific Programming, 2022, 2022.
Al-Dmour A, Al-Dmour R H, Al-Dmour H H, et al. The effect of big data analytic capabilities upon bank performance via FinTech innovation: UAE evidence[J]. International Journal of Information Systems in the Service Sector (IJISSS), 2021, 13(4): 62-87.
Financial Services[J]. Technology Innovation Management Review, 2022, 12(1/2).
Ali Q, Salman A, Yaacob H, et al. Does big data analytics enhance sustainability and financial performance? The case of ASEAN banks[J]. The Journal of Asian Finance, Economics and Business, 2020, 7(7): 1-13.
Buchak G, Matvos G, Piskorski T, et al. Fintech, regulatory arbitrage, and the rise of shadow banks[J]. Journal of financial economics, 2018, 130(3): 453-483.
Elsaid H M. A review of literature directions regarding the impact of fintech firms on the banking industry[J]. Qualitative Research in Financial Markets, 2021.
Lee C C, Li X, Yu C H, et al. Does fintech innovation improve bank efficiency? Evidence from China’s banking industry[J]. International Review of Economics & Finance, 2021, 74: 468-483.
Nguyen L, Tran S, Ho T. Fintech credit, bank regulations and bank performance: a cross-country analysis[J]. Asia-Pacific Journal of Business Administration, 2021.
Jagtiani J, Lemieux C. Do fintech lenders penetrate areas that are underserved by traditional banks?[J]. Journal of Economics and Business, 2018, 100: 43-54.
Balyuk T. FinTech lending and bank credit access for consumers[J]. Management Science, 2022.
Le T D Q, Ho T H, Nguyen D T, et al. Fintech credit and bank efficiency: International evidence[J]. International Journal of Financial Studies, 2021, 9(3): 44.
Zhao J, Li X, Yu C H, et al. Riding the FinTech innovation wave: FinTech, patents and bank performance[J]. Journal of International Money and Finance, 2022, 122: 102552.
Chan S, Ji Y. Do interest rate liberalization and fintech mix? Impact on shadow deposits in China[J]. China & World Economy, 2020, 28(1): 4-22.
Lien N T K, Doan T T T, Bui T N. Fintech and banking: Evidence from Vietnam[J]. The Journal of Asian Finance, Economics and Business, 2020, 7(9): 419-426.
Wang R, Liu J, Luo H. Fintech development and bank risk taking in China[J]. The European Journal of Finance, 2021, 27(4-5): 397-418.
Deng L, Lv Y, Liu Y, et al. Impact of fintech on bank risk-taking: Evidence from China[J]. Risks, 2021, 9(5): 99.
Banna H, Hassan M K, Rashid M. Fintech-based financial inclusion and bank risk-taking: Evidence from OIC countries[J]. Journal of International Financial Markets, Institutions and Money, 2021, 75: 101447.
Yao T, Song L. Examining the differences in the impact of Fintech on the economic capital of commercial banks’ market risk: evidence from a panel system GMM analysis[J]. Applied Economics, 2021, 53(23): 2647-2660.
Cheng M, Qu Y. Does bank FinTech reduce credit risk? Evidence from China[J]. Pacific-Basin Finance Journal, 2020, 63: 101398.
Hu D, Zhao S, Yang F. Will fintech development increase commercial banks risk-taking? Evidence from China[J]. Electronic Commerce Research, 2022: 1-31.
Zhang A, Wang S, Liu B, et al. How fintech impacts pre‐and post‐loan risk in Chinese commercial banks[J]. International Journal of Finance & Economics, 2022, 27(2): 2514-2529.
Xie Z S. A Risk Feature Recognition Method of Cross-Border Financial Derivatives’ Transaction Based on Fuzzy Support Vector Machine[J]. Mobile Information Systems, 2022, 2022.
Ma Y, Liu H, Zhai G, et al. Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model[J]. Wireless Communications and Mobile Computing, 2021, 2021.
Sun Q, Wu H, Zhao B. Artificial intelligence technology in internet financial edge computing and analysis of security risk[J]. International Journal of Ad Hoc and Ubiquitous Computing, 2022, 39(4): 201-210.
Wang Y. Research on supply chain financial risk assessment based on blockchain and fuzzy neural networks[J]. Wireless Communications and Mobile Computing, 2021, 2021.
Zhang H, Khurshid A, Xinyu W, et al. Corporate financial risk assessment and role of big data; New perspective using fuzzy analytic hierarchy process[J]. Journal for Economic Forecasting, 2021 (2): 181-199.
Sun S, Wu W, Wu S, et al. FINANCIAL RISK ASSESSMENT BASED ON AN IMPROVED EFFICACY COEFFICIENT METHOD[J]. Transformations in Business & Economics, 2021, 20.
Zong Q. RESEARCH ON FUZZY EVALUATION MODEL OF ENTERPRISE FINANCIAL RISK BASED ON LOW-CARBON ECONOMIC ENVIRONMENT[J]. FRESENIUS ENVIRONMENTAL BULLETIN, 2020, 29(11): 9872-9879.
Wang K, Yan F, Zhang Y, et al. Supply chain financial risk evaluation of small-and medium-sized enterprises under smart city[J]. Journal of advanced transportation, 2020, 2020.
Kou G, Olgu Akdeniz Ö, Dinçer H, et al. Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach[J]. Financial Innovation, 2021, 7(1): 1-28.
Zhong Y. Financial risk investment decision based on fuzzy logic theory model[J]. Journal of Discrete Mathematical Sciences and Cryptography, 2018, 21(6): 1419-1424.
Wang C, Wei Y. Simulation of financial risk spillover effect based on ARMA-GARCH and fuzzy calculation model[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40(4): 6555-6566.
Zhao T R. Models for evaluating the benefit risk and performance of internet financial product with triangular fuzzy information[J]. Journal of Intelligent & Fuzzy Systems, 2019, 37(2): 1819-1826.
Demir E, KARAMAŞA Ç. Analysis of Experts' Psychological Behaviors Under Risk with Pythagorean Fuzzy Sets and Todim Method in Terms of Balanced Scorecard: An Example of Factoring and Financial Leasing Companies[J]. Journal of Multiple-Valued Logic & Soft Computing, 2020, 35.
Hu K H, Chen F H, Hsu M F, et al. Construction of an AI-driven risk management framework for financial service firms using the MRDM approach[J]. International Journal of Information Technology & Decision Making, 2021, 20(03): 1037-1069.
Ding Q. Risk early warning management and intelligent real-time system of financial enterprises based on fuzzy theory[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40(4): 6017-6027.
Kunitsyna N, Britchenko I, Kunitsyn I. Reputation risks, value of losses and financial sustainability of commercial banks[J]. Entrepreneurship and Sustainability Issues, 2018, 5(4).
Erol I, Ar I M, Peker I, et al. Alleviating the impact of the Barriers to circular economy adoption through blockchain: An investigation using an integrated MCDM-based QFD with hesitant fuzzy linguistic term sets[J]. Computers & Industrial Engineering, 2022, 165: 107962.
Wu T, Liu X, Qin J, et al. An interval type-2 fuzzy Kano-prospect-TOPSIS based QFD model: Application to Chinese e-commerce service design[J]. Applied Soft Computing, 2021, 111: 107665.
Liu P, Gao H, Ma J. Novel green supplier selection method by combining quality function deployment with partitioned Bonferroni mean operator in interval type-2 fuzzy environment[J]. Information Sciences, 2019, 490: 292-316.
Li W, Yüksel S, Dinçer H. Understanding the financial innovation priorities for renewable energy investors via QFD-based picture fuzzy and rough numbers[J]. Financial Innovation, 2022, 8(1): 1-30.
Yan H B, Meng X S, Ma T, et al. An uncertain target-oriented QFD approach to service design based on service standardization with an application to bank window service[J]. IISE Transactions, 2019, 51(11): 1167-1189.
Huang S T, Chang K Y, Su I, et al. Service quality assessment of free trade port zone using multilayer quality function deployment: an empirical study in Taiwan[J]. Journal of Marine Science and Technology, 2020, 28(1): 1.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Shuo Shen (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
