This part delineates the fundamental principles of financial aid, fintech, and political risk, establishing the theoretical framework for the ensuing research.

Financial aid encompasses diverse forms, including international economic assistance and development support, which involve grants, concessional financing, and material contributions. China’s foreign aid predominantly comprises official development assistance (ODA) and other official funds (OOF), emphasizing monetizable resources ( OECD, 2020 ; Neumayer, 2003 ). Aid classification can be based on origin, type, aim, etc., including bilateral or multilateral aid. The efficacy of assistance is assessed based on the attainment of established objectives ( Sachs, 2005 ).

Financial technology enhances financial services by technological innovation, increasing efficiency, inclusivity, and fostering economic development. Originating in the 1990s, it initially denoted the utilization of technology by financial organizations to minimize expenses, subsequently broadening to encompass AI, big data, and block chain in enhancing financial sector capabilities ( Arner et al., 2017 ). Financial technology is characterized by technology-driven progress, inclusion, and global collaboration, while also encountering issues related to regulation and social equity ( Zohar, 2019 ).

Political risk is categorized into narrow and broad meanings. The restricted definition emphasizes the influence of political occurrences or policy modifications on corporate interests within the host nation. The expansive term includes dangers such as warfare, labor strikes, or coups that could adversely impact a nation’s economy ( Henisz, 2000 ). This research largely examines macro-level political hazards and their impact on financial technology development.

The concept of foreign financial aid emerged from development economics, focusing on the economic roles of help. The efficacy of aid is assessed by its success in fulfilling the objectives of both donor and recipient nations, and can be examined from multiple viewpoints: donor, recipient, or shared interests ( Riddell, 2007 ).

The inclusive finance theory, endorsed by the UN and international experts, advocates for the provision of comprehensive financial services to alleviate poverty and foster economic progress ( United Nations, 2015 ). China’s financial assistance, including support for digital payment systems and mobile banking, has improved financial accessibility in Africa, fostering regional economic development ( Gao, 2019 ). The human capital theory, introduced by economist Theodore Schultz, asserts that technical advancement and economic progress are propelled by the enhancement of human capital ( Schultz, 1961 ). China’s assistance includes financial support, technology transfer, and talent development, thereby significantly bolstering Africa’s financial technology advancement ( Li & Liu, 2020 ).

The financial technology development idea, grounded in Joseph Schumpeter’s innovation theory, asserts that technological advancement is the primary catalyst for growth in the financial sector ( Schumpeter, 1942 ). China has enhanced its worldwide competitiveness through financial technology aid initiatives in Africa, simultaneously assisting recipient nations in modernizing their financial systems ( Chen et al., 2018 ). The mercantilism theory, formulated by John A. Hobson, suggested that aid can facilitate the internationalization of the donor nation’s industries ( Hobson, 1902 ). By promoting financial technology in Africa, Chinese firms such as Alipay and Tencent have enhanced their market presence and cultivated trade and investment relationships with the continent, yielding reciprocal economic advantages ( Bai & Zhang, 2020 ).

The theory of inclusive finance and the notion of interdependence highlight the reciprocal dynamics of aid relationships. China’s financial technology assistance to Africa not only bolsters the advancement of African financial systems but also amplifies China’s economic and political might ( Chin, 2007 ). China’s financial assistance to Africa’s financial technology advancement exemplifies a seamless integration of theory and practice, establishing a multi-tiered cooperation model advantageous to both entities.

In recent years, Africa, recognized for its significant developmental potential, has garnered considerable attention from prominent bilateral aid nations, like the United States and the European Union. Although both organizations dedicated to fostering Africa’s development, they exhibit considerable disparities in approach, emphasis, and historical trajectories. U.S. aid concentrates on health, education, food security, and humanitarian assistance. The most emblematic initiative is the President’s Emergency Plan for Assistance (PEPFAR), which has significantly enhanced the response to AIDS in Africa. The United States has enacted the African Growth and Opportunity Act (AGOA) to enhance African nations’ access to the U.S. market and fortify economic collaboration. U.S. aid frequently includes stipulations, such as mandating recipient nations to implement democratic changes, anti-corruption initiatives, and enhancements in governance. These criteria, although fostering responsibility, may also restrict African nations’ flexibility in utilizing aid resources ( Figure 1 ).

In contrast, the EU’s aid approach focuses on multilateral collaboration. The EU has formed ties with African, Caribbean, and Pacific countries (ACP countries) through frameworks like the Cotonou Agreement, emphasizing environmental preservation, renewable energy, education, and equitable social development. The EU prioritizes the fight against climate change, the advancement of green energy initiatives, and the promotion of gender equality. Its support seeks to tackle structural issues and foster enduring resilience and self-sufficiency in African nations. The progression of EU aid to Africa distinctly illustrates its transformation. During the mid-20th century, EU assistance was predominantly shaped by post-colonial relations and aimed at preserving economic connections with former colonies. By the conclusion of the 20th century, the EU prioritized democracy, human rights, and governance in its aid program. In the 21st century, as issues like globalization and climate change escalate, the EU has redirected its emphasis towards economic development, renewable energy, and digital transformation.

Notwithstanding their disparities, both the United States and the European Union have significantly contributed to Africa’s growth, with the distinct merits of their separate aid programs providing varied methodologies for advancement in African nations. The United States prioritizes immediate outcomes via strategies like healthcare and market access, but the European Union focuses on long-term fintech development aimed at systemic enhancement and social inclusion. This multifaceted aid strategy provides African nations with various avenues for collaboration with international partners to facilitate economic development and social advancement.

China and the United States differ in their foreign financial aid features and policy formulation tendencies. The following manifestations are present: Initially, there exist varying inclinations in the selection of beneficiaries and aid initiatives. In contrast, the United States has selected a wider array of honorees. The United States posits that open and democratic social governance is crucial for economic development, which relies on the collaborative involvement and concerted efforts of several stakeholders. Consequently, the aid recipients designated by the United States encompass the government, private institutions, corporations, and philanthropic organizations. Statistics from the Brookings Institution indicate that in 2018, 34% of U.S. development assistance was allocated to multilateral institutions, representing the largest share, followed by other sectors at 25%, national governments at 21%, and non-governmental organizations at the lowest proportion. Conversely, China mostly directs its assistance towards foreign governments and multilateral organizations, seldom selecting private institutions and firms as recipients of aid. The United States aims to propagate its principles through aid initiatives, frequently selecting humanitarian and public health projects. China is more predisposed to select initiatives that enhance the welfare of the populace and foster economic advancement in recipient countries, particularly infrastructure projects. From 2000 to 2017, the aid funds provided by China to Africa can be broadly divided into three categories according to their purposes: commercial, development and mixed purposes. Specifically, commercial aid accounted for 13% of the total aid funds, occupied purposes accounted for 36.7%, and the remaining 50.2% was used for mixed purposes ( Figure 2 ).

China has extended help to Africa through debt restructuring, debt forgiveness, technical support, and loan provisions. Between 2000 and 2017, loans were the predominant part at 51.4 percent, succeeded by export buyer’s credit at 31.4 percent. The proportion of loans reached its zenith in 2011 at 98 percent. This clearly illustrates the versatility and adaptability of China’s support to Africa in both its modalities and substance, while also highlighting the deep-rooted collaboration between China and African nations ( Figure 3 ).

In recent years, fintech has emerged as a significant inventive force in the financial sector, seeing rapid global development, particularly in emerging nations. It is essential for fostering financial inclusion, enhancing capital allocation efficiency, and stimulating economic growth ( Barberis & Buckley, 2016 ). The advancement of fintech in Africa is primarily propelled by several factors, including the prevalence of mobile payments, the rise in Internet access, and supportive government policies ( Gomber et al., 2017 ). Kenya’s M-Pesa, a successful mobile payment system, has emerged as a significant topic in fintech research and has garnered extensive discussion ( Jack & Suri, 2011 ). Simultaneously, several African nations are progressively enhancing their legislative frameworks to foster a market climate favorable to digital financial innovation ( Zetzsche et al., 2017 ; Evans & Pirchio, 2015 ). In recent years, Africa has seen mobile phone subscribers surpass 495 million, with connected SIM cards totaling 930 million, expanding at an average annual rate of 4.8%. In Africa, mobile technology has emerged as the most efficient instrument for fintech companies to engage users. In 2007, the African mobile payment sector was catalyzed by the introduction of M-Pesa, an electronic wallet in Kenya that facilitates money transfers and payments via text messages, without the necessity of Internet access. M-Pesa garnered 1 million customers in its initial nine months of existence, then increasing to 4 million within 18 months. In that year, mobile phone transactions in Kenya amounted to $50 billion. Kenyans conduct an average of $140 million in daily mobile phone transactions, with mobile payments constituting 87 percent of the nation’s yearly gross domestic product (GDP). North African nations possess 66.59 million mobile payment accounts and 74.1 million mobile payment agents, respectively ( Boston Consulting Group, 2018 ). M-Pesa has undeniably initiated a new era by providing affordable and effective banking services to rural Kenya ( Figure 4 ).

In prior research, academics frequently considered political risk, institutional quality, and infrastructure as mediating variables in examining the influence of aid on economic growth ( Chen & Li, 2021 ; Garcia et al., 2023 ). Some Western media characterize China’s foreign aid to African nations as “strategic expansion”, as previously mentioned. Consequently, political risk will serve as an intermediary variable in this study to examine the influence of China’s foreign aid on the advancement of the fintech sector, while scholars juxtapose it with the United States’ aid model, which is contingent upon political conditions. This section will present three distinct perspectives to examine the aforementioned challenges. This research will examine the indirect mechanism of American foreign aid and assess its impact on the advancement of African fintech development, considering the mediation of political risk ( Figure 5 ).

This study formulates the primary regression equations to analyze China’s impact in the growth of fintech in Africa.

Fintech_it = αAid+ βc_it + φ_i+ μ_it (1)

In this research, we employ various analytical techniques, including stepwise detection regression parameter method, parameter product test method, and parameter difference test method, to thoroughly investigate whether political risk, as an intermediary variable, influences the relationship between aid and fintech development. This study discusses the gradual detection regression parameter method employed by certain researchers in intermediary system detection to formulate two operational equations:

Risk_it = γAid_it + β₁𝐶_it+ φ_i1 + μ_it1 (2)

Fintech_it = α₁Aid_it+ 𝜔𝑅𝑖𝑠𝑘_it+ β₂𝐶𝑖𝑡+ φ_i2 + μ_it2 (3)

The equations of the aforementioned operation 𝑖 represent several African countries, whereas 𝑡 denotes different years. The financial technology development indicators (Fintech), market trade openness (FDI), Africa’s market trade dependence on China (GDP), socio-economic development (GDP), and investment (GDP) will influence the economic development level of African countries over a span of years. This influence encompasses capital intensity, trade openness, foreign aid, import and export dynamics, and investment, particularly regarding foreign direct investment from China, the significance of trade liberalization, the interdependence of Africa and China in market trade, the impact of socio-economic development, reliance on grants, and the role of natural resources.

This project aims to utilize control panel data from 45 African nations receiving foreign aid between 2000 and 2017, employing least squares, fixed effects, and other methodologies, with China’s total foreign investment in Africa as the independent variable and the level of fintech development in recipient countries as the dependent variable. Utilizing metrics such as foreign direct investment (FDI), trade openness, Africa’s trade reliance on China, economic development (GDP), financial input, and natural resource input, among others. Establishing the correlation between African economic growth and foreign direct investment (FDI) in Africa.

China’s financial aid to Africa, this research conducts a thorough comparison analysis using two metrics: Aid Data and CARI (China-Africa Research Initiative). This study utilized the total aid amount from the Aid Data 2.0 database as the primary dependent variable, adjusting the relevant data to the 2017 fixed dollar exchange rate to mitigate the effects of inflation, and employed paired numbers to ensure comparability with other variables. Firstly, Aid Data database inaccurately categorizes foreign investments by private enterprises, such as Huawei, as official bailouts, and loans from Chinese-African joint ventures as loans from the host nation’s government, hence inflating the estimation of China’s concealed debt to African nations. Moreover, several scholars have focused their research on the foreign help provided by NGOs. Consequently, we utilized data from the Johns Hopkins China-Africa Research Initiative to assess its stability. This study gathers data on nations, sectors, and financial service entities related to China’s foreign direct investment, financial aid assistance, and credit; nonetheless, the overall scope of total money and investment projects in Africa is much inferior than that of Aid Data.

Secondly, considering the diversification of China’s non-aid models and the existing literature on the funding methods of the Official Development Assistance “ODA”, along with the characteristics of funding scales across various countries, this analysis will delve into the action mechanism of the “poverty alleviation” policy on China’s development and investigate the role of this policy in advancing economic development. Consequently, the volume of American aid to Africa is incorporated into the fundamental regression analysis, and its impact is examined.

The Financial Technology Development Index (Fintech) examines the extent of financial technology advancement in African nations. The analysis encompassed of data from 2000 to 2017. Findexable releases a globally comparable Fintech development metric, which is assessed annually within the same nation. The findexable neural network offers a scientific analytical approach to establish a comprehensive indexing system for the advancement of financial technology. Initially, to mitigate the impact of anomalous data, we exclude the lowest 2.5% of observational data. Secondly, the scores for each indicator are derived separately based on distinct data types. In the absence of an absolute ideal value, the weighted average score is utilized. The score of each indicator is normalized to a range of [0, 100], and subsequently, the arithmetic mean is employed to get the overall score of the countries, resulting in their ranking. This, in conjunction with the World Bank’s Global Financial Inclusion Database, which offers comprehensive data on financial inclusion, digital payments, and mobile banking, robustly supports the analysis of fintech trends and their impacts across various nations.

Political Risk, by evaluating various alternative factors, the “arithmetic average” of the four indicators of “Control of Violence” and “Social Stability”—namely “Government Effectiveness,” “Regulatory Quality,” and “Anti-Corruption”—in the African Governance Index, is utilized to assess the political risks encountered by African nations. The evaluation outcomes were graded from -2.5 (bad) to 2.5 (poor), with lower scores signifying more political peril. In the regression model, we standardized the political risk index, indicating that a greater value corresponds to an increased level of political risk.

Nonetheless, due to the challenges in quantifying political risk, researchers from various nations select disparate alternative indicators. Prior research has utilized the Global Governance Index (WGI), the Index of Economic Freedom, and the International Country Risk Guide (ICRG). In 2018, Jing Zhou and Jing Hong conducted a quantitative evaluation of the political risk in African nations by utilizing the average coefficients of the four World Governance Indicators. In 2017, Chen Si and Ma Yeqing, followed by Zhu Jiaqi in 2021, utilized the national regulatory economic risk level from the International Country Risk Guide to assess the political risk of different nations. In 2014, utilizing ICRG statistical data, Tan Yingying and Fang Zhaoyun (2019) categorized intergovernmental political risk into two tiers: unilateral and bilateral, and established distinct scoring standards for each category.

The control variable for market openness to trade is determined by the proportion of total imports and exports relative to GDP, as sourced from the World Bank WDI database. In African nations, the openness of market commerce significantly influences the quality of international trade. This study established this indicator as a control variable, based on the research conclusions of Moustier and Teboul in 2001. Outward direct investment is quantified as the ratio of a nation’s foreign direct investment inflows to its GDP for the current year, and this metric is transformed into logarithmic form for analytical purposes. The sample data for this indicator was sourced from the United Nations database, using selection criteria derived on the study findings of Yan Bing, Xie Xindi, and other experts in 2021. Grant reliance serves as a proxy variable for the volume of grants within government revenue, encompassing payments from foreign government departments and international organizations. This study based its variable selection on the research conducted by Yan Hongrong and Zhang Xiaolu et al. in 2020. The degree of socio-economic development is assessed using real GDP per capita, estimated at constant 2017 US dollar prices. The index data was sourced from the United Nations database, and the selection referenced the research findings of Yan Bing and Xie Xindi from 2021, as well as those of Zhou Jing and Hong Jing from 2018. Total natural resources serve as a proxy variable. Data sourced from the World Bank Database. This work adopts a novel perspective based on the research of Chen Weibing and Wu Jin. The exchange rate denotes the annual average rate of China’s currency relative to that of the recipient country. Information from the United Nations Conference on Trade and Development (UNCTAD). Consult the decisions made by Renwick et al. (2018) . The debt ratio is the total debt repaid throughout the year expressed as a percentage of Gross National Income (GNI). Data was acquired from the United Nations information database ( Table 2 ).

Note: The data was collected and classified by the author.

Table 3 illustrates that the Variance Inflation Factor (VIF) for all variables is below 10, with the average VIF significantly under 10, indicating the absence of substantial multicollinearity. This study conducted correlation tests for each relevant element. The test findings indicate that the majority of correlation coefficients are below 0.50, suggesting an absence of significant collinearity issues.

Note: The data was collected and classified by the author.

Table 4 presents the formula for ordinary least squares (OLS) regression (1), the formula for random effects generalized least squares (GLS) regression (2), and the results of fixed effects regression (3). The findings of the OLS regression analysis in (1) indicate a lack of a strong correlation between China’s outbound investment and the advancement of Fintech in Africa. Model (2) indicates that China’s financial assistance to Africa may be augmented by 6.5% at a significance level of 5%. Model (3) indicates that China’s fintech advancement in Africa, at a 5% importance level, will enhance its economic growth by 6.7%. Despite the Hiseman test results endorsing the hypothesis of random effects, a fixed effect model is employed to illustrate the hypothesis below, as the presumed conditions of the random hypothesis are excessively idealistic and there exists a degree of consistency across the estimates. The empirical analysis demonstrates that China’s financial assistance to African nations can significantly enhance their progress in financial technology. The rationale may lie in the close correlation between China’s aid strategy to Africa and its secondary objectives in fintech growth, which successfully fosters fintech advancement in Africa.

Note: *, **, *** in turn indicates that the reflection is obvious at the level of 10.0%, 5.0%, 1.0%, and the stable reference standard error is in parentheses.

Table 5 demonstrates that (1) represents the fixed effect post-removal of political risk, revealing that China’s financial support to Africa can enhance the fintech development of African nations by 6.9% at a 5% significance level, reflecting China’s contribution to the “fintech development” of these countries. The rationale may be because China’s aid strategy for Africa is closely aligned with its secondary objectives in fintech advancement, hence facilitating the growth of fintech in Africa. Analysis of the moderating impacts of (1), (2), and (3) reveals that the Chinese government’s “Aid-Africa” initiative in African nations remains unaffected. Specifically, in (2), there is no evident positive correlation between the volume of China’s aid to Africa and the political risks encountered by the recipient nations; thus, the financial assistance from China to Africa is not contingent upon the political risks of the recipient countries, aligning with China’s established stance and practice regarding foreign aid. The correlation between the extent of fintech development in Africa, the magnitude of China’s aid to Africa, and domestic policy risks is statistically significant at the 5% level. Specifically, a 1% increase in China’s foreign investment in Africa corresponds to a 6.7% enhancement in fintech development. Nonetheless, a 1% increase in the political risk of the recipient country results in an 82.6% decline in its fintech development; thus, greater political and social stability correlates with enhanced fintech development.

Note: *, **, *** in turn indicates that the reflection is obvious at the level of 10.0%, 5.0%, 1.0%, and the stable reference standard error is in parentheses.

1) Data Information Set Submission Method

This study utilized samples from the Johns Hopkins China-Africa Research Initiative for stability testing, and data regarding China’s OFDI, aid and loan recipient countries, industries, and financial institutions was acquired. The aggregate sum and quantity of money addressed by the China-Africa Institute are significantly less than those reported by Aid Data. The regression analysis presented in Table 6 indicates that the correlation between China’s fintech development in Africa and China’s foreign investment fails to achieve 1% significance, and the government has no discernible regulatory influence on fintech development in Africa.

Note: *, **, *** in turn indicates that the reflection is obvious at the level of 10.0%, 5.0%, 1.0%, and the stable reference standard error is in parentheses.

2) Data Information Set Submission Method

Note: *, **, *** in turn indicates that the reflection is obvious at the level of 10.0%, 5.0%, 1.0%, and the stable reference standard error is in parentheses.

The United States has significantly contributed to the advancement of fintech in Africa. Table 7 demonstrates that model (1) predicts a 1% growth rate in US aid to Africa, correlating with a 0.289% increase in Africa’s fintech development score, suggesting that US funding facilitates fintech advancement in Africa. By synthesizing (2) and (3), we ascertain a specific association between American assistance to the United States and the political risk of the recipient nation. The United States intervenes in the internal matters of the recipient nation under the guise of assistance, or it engages the recipient nation due to its own political vulnerabilities.

Table 8 indicates that, despite the limited sample size of ODA and the suboptimal results of regression analysis, ODA significantly contributes to the advancement of fintech in Africa at the 10% significance level. This finding aligns with our prior research hypothesis that China has facilitated the development of fintech in Africa.

Note: *, **, *** indicate significant at the level of 10%, 5%, 1% respectively, and the brackets are robust standard error.

This study categorizes aid purposes into four classifications: classical, economic, development, and mixed reasons. Nonetheless, the quantity of help designated for development purposes totals 310, whilst the quantity of aid not related to development goals is merely 28. Consequently, we concentrate on the impact of developmental aid on the advancement of fintech. This conclusion aligns well with the findings of the fundamental regression analysis in this study ( Table 9 ).

Note: *, **, *** indicate significant at the level of 10%, 5%, 1% respectively, and the brackets are robust standard error.

Note: The joint identity test uses Klebergen-Papp rk LM statistical analysis, and the weight recognition test uses Klebergen-Papp Wald F statistics. The Stock-Yogo weighted ID located in “{}” tests for key values in the 15% confidence range. Represents the corresponding P value in “[]”. DWH was tested by Dubinwu-Hausmann test.

We identified two instrumental variables: one pertains to China’s steel output and its help to Africa; the other is derived from the works of Sun et al. (2024) , and Dreher et al. (2008) . Consequently, the two employed tools were utilized to modify the quantity. To ascertain the relationship between the volume of China’s aid and support to Africa and the social and economic development of the recipient nations. The statistical Table 10 below indicates that employing IV1 (China’s steel production and processing aid to China’s supply) as an application tool to modify the quantity is justifiable, and the positive correlation between poverty alleviation and fintech development remains largely unchanged between these two factors. The recipient country did not successfully complete a series of internal assessments, including the identification flaw test, endogeneity test, and identification test, about China’s voting outcomes at the UN.

Note: *, **, *** in turn indicates that the reflection is obvious at the level of 10.0%, 5.0%, 1.0%, and the stable reference standard error is in parentheses.

The heterogeneity of Africa is analyzed from two viewpoints: firstly, the continent is segmented into North Africa and sub-Saharan Africa; according to the African Economic Prospects 2022 classification, countries are categorized into three groups: oil-exporting, resource-intensive, and non-resource-intensive. The models (1) and (2) in Table 11 below demonstrate a substantial positive association between the aid and support from sub-Saharan African countries to China and its fintech growth, although no such correlation exists in North Africa. This is primarily due to the scarcity of statistical data in North Africa, with just 67 papers available, resulting in an insufficiently evident correlation degree. Secondly, North Africa, being more economically advanced than the continent of Africa overall, has received comparatively limited money and, in many instances, insufficient assistance. Models 3, 4, and 5 in Table 11 below indicate that China plays an insignificant role in fostering fintech development in Africa, although its contribution to other resource-intensive sectors in Africa’s fintech development is 10%. The causes may stem from the limited data on African crude oil exporting nations and the intricate industrial framework of non-resource-based countries. This circumstance has led to Chins involvement in fostering fintech development in these nations to a degree.