代做FINM081 The Role of AI in Enhancing Financial Inclusion in Chinese Banking Task 2代做Prolog

The Role of AI in Enhancing Financial Inclusion in Chinese Banking

Task 2: Research Design

Research Questions

1. What are the specific barriers to the adoption of AI solutions in Chinese banks, and how can these be mitigated?

2. How does the implementation of AI in banking impact financial inclusion in different socio-economic regions within China?

3. What specific AI innovations have proven most effective in enhancing financial inclusion in Chinese banks, and what factors contribute to their success?

Research Philosophy

The research philosophy used in this study is pragmatism. There is always flexibility in pragmatism, and this makes it appropriate in the mixed methods research that involves use of both qualitative as well as quantitative data. According to the pragmatic perspective, ideas should be put into practical use as actions which prove whether those ideas are true or not thereby connecting theory and practice (James, 2020; Kaushik & Walsh, 2019). This philosophy is especially helpful when the problem under investigation is still evolving and possesses features that call for multiple and flexible approaches. Pragmaticism is rather interested in solutions to research problems and interested in freedom in methods of solving these problems (Nørrekli & Mitchell, 2010). This makes it an ideal approach for studying modern phenomenon such as FinTech, which occurs in dynamically changing conditions.

This philosophy aligns with the study objectives by facilitating the analysis of AI's impact on financial inclusion within Chinese banks using both quantitative data and qualitative feedback from stakeholders. Pragmatism enhances research relevance by concentrating on the practical application of theories and addressing emergent phenomena within socio-economic and regulatory landscapes (Ormerod, 2021). By embracing methodological pluralism, pragmatism not only supports flexibility in research methods but also increases the practical value of the results obtained.

Research Methodology

This research uses mixed methods, which emphasize the use of both qualitative and quantitative methods to gather sufficient data for the purposes of this analysis of FinTech for financial inclusion. The mixed-method approach guarantees that every aspect is covered since both methods have their advantages. The factual data, like the number of bank accounts, mobile money transactions, offer objective and statistically significant information about the penetration and effectiveness of FinTech services (Bryman, 2016). At the same time, qualitative information from case studies and reports provides context and human aspects of the users and regulations within Chinese banks.

The combined use of these methods facilitates the assessment of the control variables, while the qualitative data should expand upon and complement the findings of the quantitative analysis. This mixed approach not only provides data analytics of trends and patterns identified through a quantitative approach, but it also gives the researcher a qualitative feel of the clients in the financial inclusive process with the help of technological innovations in FinTech domain (Shannon-Baker, 2016). The use of mixed-methodology allows for a thorough and comprehensive approach to the research topic, identifying many-scaled effects of FinTech on financial inclusion.

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Research Design

The research design for this study is a cross-sectional mixed-method approach. A cross-sectional study is carried out at one time hence can be effective in establishing the current situation of events like financial inclusion. This design is beneficial because it captures the current situation and enables the assessment of the specified variables and their interactions at a particular time (Johnson & Onwuegbuzie, 2004). To achieve the above objectives, the study utilizes quantitative data sourced from the Bank of China’s annual reports, rural credit cooperatives' data, and other relevant Chinese financial institutions. These sources offer reliable financial access quantitative measures that help to analyze trends, assets and liabilities, opportunities and threats of financial inclusion.

At the same time, the qualitative aspect aims at reviewing case studies and reports to consider avenues like user experiences, regulation, or socio-economics. This supplements the quantitative analysis because it allows for adding context to the quantitative results representing the existing conditions of financial inclusion. The mixed-methods approach allows for triangulation of data thus making the results more valid and reliable as information from various sources and different methods are evaluated. The strength of this system is the holistic approach to the study of factors that facilitate or hinder financial inclusion.

Methods for Data Collection

The collection of data for this study will be mainly concerned with the acquisition of secondary data from reputable sources. This means an objective and focused search of specific studies, reports, and datasets that help in understanding FinTech and financial inclusion. The sources will be searched from the scholarly publications and other search networks using keywords such as ‘FinTech’, ‘financial inclusion’, ‘mobile money’, ‘digital payments’, ‘regulatory issues’. These search terms will be used to gather comprehensive information specifically related to AI in Chinese banking. Among the key constituents of the given approach, the selection criterion will play the most crucial role because in the given task, only the most recent, relevant, and credible sources authored by only relevant contributors will be considered. This reduces the probability of using inaccurate data and information which in turn increases the reliability of the used data. In order to provide the necessary information and address specific topics, the data will be collected from reputable sources such as the Bank of China, Chinese regulatory bodies, and relevant datasets from the World Bank and IMF focused on China. These sources are important for being powerful and highly accumulative data sets on main indicators of the global financial system.

Search Table

Basic Search Terms	Combined Boolean Search Terms
"FinTech"	("FinTech" OR "financial technology") AND ("financial inclusion" OR "banking access") AND ("China" OR "Chinese banks")
"financial inclusion"	("financial inclusion" AND "AI adoption") AND ("China" OR "Chinese banking sector")
"AI in banking"	("AI in banking" AND "financial services") AND ("China" OR "Chinese banks")
"digital payments"	("digital payments" AND "AI technology") AND ("policy impact" OR "regulatory challenges") AND ("China" OR "Chinese banking sector")
"regulatory issues"	("regulatory issues" AND "FinTech adoption") AND ("AI implementation" OR "banking sector") AND ("China" OR "Chinese regulations")
"Bank of China reports"	("Bank of China reports" AND "financial inclusion") AND ("AI technology" OR "FinTech")
"rural credit cooperatives"	("rural credit cooperatives" AND "AI adoption") AND ("financial services" OR "FinTech") AND ("China")

Research Methods

The primary sources of data used are the Bank of China’s annual reports, rural credit cooperatives' data, and other relevant Chinese financial institutions. These sources incorporate quantitative data and highlight specific details on financial access, including the number of existing bank accounts, digital transactions, and AI adoption metrics in Chinese banks. Qualitative data are necessary to achieve qualitative co-relation/characteristics, which enables a systematic approach towards trends and characteristics on financial accessibility.

Moreover, the study will incorporate qualitative data available in the form. of AI user experience surveys, case studies, and reports within Chinese banking. As secondary sources, academic journals, publications from Chinese regulatory bodies, and other relevant organizations will be consulted. The quantitative results will complement the qualitative results, which aim to gain a better understanding of the user experience and initial impression of AI applications in Chinese banks.

Criteria for Data Accuracy

The aspects to be considered regarding data accuracy are the validity and reliability of the sources of the data. The two components of validity include content validity based on adequate information collection concerning the perceptions concerning the financial inclusion and the use of FinTech (Sireci & Faulkner-Bond, 2014). Criterion validity affirms the quality of the collected data when compared to other sources of data independent of the collected data while construct validity confirms that the collected data reflects concepts that are more theoretical in relation to financial inclusion (Bedford & Speklé, 2018). Reliability is extended to the nature of the measures as the test-retest reliability applies to measures taken at different points in the same year, and source credibility, whereby the data is sourced from World Bank or IMF only (Enkavi et al., 2019).

Further, the credibility and quality of collected data will be enhanced using triangulation in the study. Triangulation involves the use of different sources and techniques of data collection with an aim of establishing the validity of the findings. For example, the contrast between the quantitative data collected from the Bank of China and the qualitative information of using AI services in Chinese banks offers a more encompassing perspective on the phenomena under study. In addition, peer reviews and consultations with experts will be conducted to confirm the interpretation of the results and relate the conclusions made to the actual trends in the case of FinTech and financial inclusion. Thus, by applying those detailed methods, the study strives for the high level of validity and reliability of the results which, in turn, would guarantee the study’s credibility.

Ethical Considerations

As this study only sources secondary data, ethical practices are therefore critical in this research. Data must be protected from unauthorized persons meaning that the data used has to be compliant with data privacy and keepers ethical standards of data suppliers (Bryman, 2016). This entails using statistical data and ensuring that all the necessary permissions are granted on data usage. Data use also calls for appropriate attribution of data to its source and which one has to be cited effectively. While maintaining ethical integrity of data collection and manipulation is a ‘must’ aspect of research that would guarantee adherence to set academic standards, it is also significant to respect the rights of the data providers and participants.

This structured approach is well-coordinated, systematic, and effective to offer adequate information on the impacts of AI in banking on financial inclusion within China. Hereby, the study combines the analysis of multiple data sources and qualitative and quantitative research methodologies to present a comprehensive approach to understanding how FinTech can enhance financial inclusion and address gaps in different areas and demographics within China.

References

Bedford, D. S., & Speklé, R. F. (2018). Construct validity in survey-based management accounting and control research. Journal of Management Accounting Research, 30(2), 23-58. https://doi.org/10.2308/jmar-51995

Bryman, A. (2016). Social research methods. Oxford university press.

Creswell, J. W. (2008). The selection of a research design. Research Design-Qualitative, Quantitative, and Mixed Method Approaches-, 3-22.

Enkavi, A. Z., Eisenberg, I. W., Bissett, P. G., Mazza, G. L., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019). Large-scale analysis of test–retest reliabilities of self-regulation measures. Proceedings of the National Academy of Sciences, 116(12), 5472-5477. https://doi.org/10.1073/pnas.1818430116

James, W. (2020). Pragmatism. In Pragmatism (pp. 53-75). Routledge.

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational researcher, 33(7), 14-26.

Kaushik, V., & Walsh, C. A. (2019). Pragmatism as a research paradigm and its implications for social work research. Social sciences, 8(9), 255. https://doi.org/10.3390/socsci8090255

Nørreklit, H., Nørreklit, L., & Mitchell, F. (2010). Towards a paradigmatic foundation for accounting practice. Accounting, Auditing & Accountability Journal, 23(6), 733-758.

Ormerod, R. J. (2021). Pragmatism in professional practice. Systems Research and Behavioral Science, 38(6), 797-816.

Shannon-Baker, P. (2016). Making paradigms meaningful in mixed methods research. Journal of mixed methods research, 10(4), 319-334.

Sireci, S. G., & Faulkner-Bond, M. (2014). Validity evidence based on test content. Psicothema. doi: 10.7334/psicothema2013.256