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Record W4414863333 · doi:10.1007/978-3-032-02983-6_20

Digital Cross-Border Payment Technologies in Fragile, Conflict, and Vulnerable Settings

2025· book-chapter· en· W4414863333 on OpenAlexaboutno aff
Erica Moret

Bibliographic record

VenueFinancial innovation and technology · 2025
Typebook-chapter
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSanctionsResilience (materials science)Humanitarian aidCivil societyInvestment (military)Humanitarian crisisGovernment (linguistics)PaymentFinancial crisis

Abstract

fetched live from OpenAlex

Abstract Financial sector de-risking is a growing problem leading to the financial exclusion of a growing number of countries around the world, especially in fragile, conflict and vulnerable (FCV) contexts deemed to be of high risk from a regulatory compliance perspective. De-risking is marked by the withdrawal of banks and other financial institutions from high-risk jurisdictions and has been termed a “global crisis” by the United Nations (UN) and other international organizations (IOs). Mounting complexity of the global compliance landscape—including the risk of sizeable fines and penalties linked to multilateral and autonomous sanctions, as well as other related regulations—alongside cost and reputational considerations, have led to a rapid decline in global correspondent banking relationships (CBRs) over the past decade (Moret, More civilian pain than political gain (Again?): The demise of targeted sanctions and associated humanitarian impacts. In A. Charron and C. Portela (Eds) Multilateral sanctions revisited: Lessons learned from Margaret Doxey . McGill Queen’s University Press, 2022). A growing number of countries are becoming partially or fully “unbanked”, as a consequence. Ramifications of de-risking include constraints in humanitarian and development assistance; hindered civil society activities; barriers to flows of household remittances; bottlenecks in legitimate trade, investment and wider financial services. Ultimately, these trends diminish resilience and prospects for sustainable peace and tend to hit the underprivileged the hardest—including women, children (Pelter, Teixeira, & Moret, Sanctions and their impact on children . UNICEF, February, 2022), the elderly, those with chronic health conditions and refugees. This chapter provides an overview of the opportunities and challenges associated with digital humanitarian fund transfer technologies and explores whether innovative payment platforms could help stem and mitigate some of the principal constraints associated with the mounting global challenge of financial sector de-risking. It concludes that technology-based solutions for cross-border payments relating to basic human needs are still in their infancy, also representing a major gap in the academic and policy literature. It draws on literature covering several use cases to suggest that payment innovations have potential to play a vital role, particularly in contexts where local entities are able to facilitate liquidity access or establish digital ecosystems for local payments. These innovations can also bring about advantages that include speed, cost-effectiveness, transparency, accountability, choice and heightened compliance, with the right guardrails. It highlights the importance of Human Centred Design (HCD) and finds that, although some IOs and non-governmental organisations (NGOs) have begun adopting innovative payment technologies in certain contexts, widespread adoption remains limited, particularly for cross-border payments. Furthermore, the pace of technological advances is often far ahead of regulatory frameworks, posing potential legal risks to users and hindering effectiveness. It concludes that addressing humanitarian fund transfer challenges associated with de-risking necessitates greater research, use cases and dialogue across stakeholder groups to foster a better understanding the role these technologies could play, alongside capacity building and awareness-raising initiatives, to enable users to take informed choices and contribute to the development of appropriate safety measures, policies and regulations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.266
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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