Financial Statecraft in the Digital Age: FinTech and the Institutional Shifts in China’s Cross-Border Payment Sector Since 1993
Bibliographic record
Abstract
Cross-border payment (CBP) facilitates international monetary transactions, allowing individuals and businesses in different countries to pay in foreign currencies and exchange funds efficiently. In recent years, CBP systems have grown substantially in the Asia-Pacific, especially in China. Through an in-depth analysis of China’s CBP development over the past three decades, this paper highlights the critical role of financial technology, or so-called FinTech, in shaping a novel governance model, which I define as digital financial statecraft. Combining policy research, analyses of industry data, and interviews with key executives, this research demonstrates three phases of CBP development, each featuring distinct technologies, policy reforms in foreign currency exchange, and the inclusion of the private sector in China’s CBP businesses. More than just a technological advancement, digital financial statecraft has created an emerging set of payment and transaction infrastructure, driving international financial transactions. This new framework contributes to the international political economy and China studies by linking financial statecraft with the social studies of FinTech. Crucially, digital financial statecraft represents a hybrid model anchored in both state authority and technological innovation that empowers states like China to actively reshape global financial flows and reduce strategic vulnerabilities in an era of intensifying great-power competition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".