Author
Listed:
- Kui YU
- Alessandro Lo Schiavo
Abstract
The ongoing digital transformation is being undertaken by the financial institutions on the upgrade in China. The risks are accumulated synchronously in the middle of establishing differentiated competitive advantages through information technology innovations by the new fintech institutions. In this study, a fuzzy analytical hierarchy process is adopted to figure out the risk evaluation of the new fintech institutions in China, identifying those at risk as early as possible. Firstly, several level 1 indicators of the risk evaluation system of the new fintech institutions and corresponding subordinate level 2 indicators are determined, followed by rating the level 2 indicators of each new fintech institution ready for risk evaluation ranking, which leads to the risk evaluation matrix of each level 1 indicator. Secondly, the new fintech institutions are classified into the theoretically ideal “optimal,†“medium,†and “worst†categories by establishing the membership matrix of each level 1 indicator in the application of linear transformation formula. Thirdly, the degree of proximity is exploited in comparison of the fuzzy sets in pairs to form the fuzzy recognition model of each level 1 indicator in pursuit of the new fintech institutions least risky regarding each level 1 indicator. Finally, the fuzzy recognition models of each level 1 indicator are integrated into the construction of the fuzzy recognition model regarding the whole risk evaluation system to achieve the risk ranking of the new fintech institutions. This study aimed to provide a theoretical ground and an applied method for national regulators to monitor the fintech risks, which are prone to be avoided by the enterprises and individuals.
Suggested Citation
Kui YU & Alessandro Lo Schiavo, 2022.
"Risk Evaluation of the New Fintech Institutions in China Based on Fuzzy Analytical Hierarchy Process,"
Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, July.
Handle:
RePEc:hin:jnlmpe:9338032
DOI: 10.1155/2022/9338032
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