INDIA, BRICS AND THE NEW PAYMENT ARCHITECTURE From Dollar Dominance to Digital-Rupee Connectivity: A Case-Cum-Research Analysis of UPI, Local Currencies and BRICS Payment Reform

 

INDIA, BRICS AND THE NEW PAYMENT ARCHITECTURE

From Dollar Dominance to Digital-Rupee Connectivity: A Case-Cum-Research Analysis of UPI, Local Currencies and BRICS Payment Reform

Abstract

The international payment system is undergoing gradual diversification rather than an immediate replacement of the US dollar. This case-cum-research paper examines India's position in this transformation through the interaction of five elements: US-dollar dominance, BRICS payment initiatives, local-currency settlement, India's Unified Payments Interface (UPI), and the widespread but factually problematic reference to “KITO currency.” The evidence in the case indicates that KITO is not a recognised international trade or BRICS settlement currency and should not be confused with KAITO or established national currencies.

The study analyses the potential of interoperable payment systems to reduce transaction friction while recognising that payment technology cannot independently eliminate foreign-exchange risk, liquidity constraints, regulatory differences, cybersecurity threats or geopolitical pressures. The paper develops a statistical-analysis framework around five proposed relationships: payment interoperability and transaction efficiency; local-currency settlement and dollar-conversion dependence; UPI and cross-border payment accessibility; payment diversification and strategic flexibility; and institutional trust/liquidity and adoption intention.

The case suggests that India's opportunity lies less in replacing the dollar and more in creating a multi-rail international payment architecture in which UPI, rupee settlement, bilateral arrangements, BRICS-linked infrastructure and established dollar markets coexist.

Keywords: BRICS, UPI, US dollar, rupee internationalisation, local-currency settlement, cross-border payments, KITO, KAITO, digital payments, India.

 

1. Introduction

International trade has traditionally depended heavily on established banking networks, correspondent banking relationships and major international currencies. The US dollar continues to occupy a central position because of its financial-market depth, liquidity, reserve-asset availability, international invoicing and network effects. The uploaded case reports that the dollar represented approximately 58% of disclosed global official foreign-exchange reserves in 2024.

At the same time, BRICS countries have increasingly explored local-currency settlement, payment-system interoperability and mechanisms designed to make cross-border transactions faster, cheaper and more resilient. The case specifically identifies national payment systems, local currencies, correspondent banking, digital currencies and cross-border settlement as the major components of this transformation.

A major conceptual problem has emerged in public discussion: currencies, payment systems and cryptocurrencies are frequently treated as interchangeable. They are not.

The US dollar is a sovereign currency and major reserve asset. UPI is a payment infrastructure. BRICS payment initiatives concern settlement and interoperability. KITO, according to the case evidence, is a small cryptocurrency token rather than a major international currency. KAITO is a separate cryptoasset.

This distinction forms the starting point of the present research.

 

2. The Central Case

2.1 The “KITO currency” confusion

The case investigates the claim that KITO is somehow connected with US–China trade, BRICS settlement or American economic leadership.

The available evidence presented in the source material does not support that claim. KITO is described as a small cryptocurrency token, while KAITO is a separate cryptoasset. Neither is identified as a BRICS settlement currency, US government currency, Federal Reserve instrument or major global trade currency.

This distinction is important because confusing a cryptocurrency token with a payment rail or sovereign currency can fundamentally distort an analysis of international monetary power.

 

3. What Actually Supports Dollar Dominance?

The case identifies a multidimensional foundation of US financial influence.

Dimension	Role
Economic scale	Large production and consumption base
Network effects	International participants already use dollars
Financial-market depth	Large and liquid financial markets
Treasury-market liquidity	Availability of highly traded dollar assets
Trade invoicing	International contracts frequently use dollars
Financial infrastructure	Extensive banking and settlement ecosystem
Institutional capacity	Developed financial and technological institutions
Geopolitical relationships	International alliances and economic relationships

The implication is significant: a payment application cannot by itself replace a reserve currency.

The dollar's international role is systemic rather than technological.

 

4. US–China Trade as the Strategic Context

China has developed substantial financial infrastructure supporting greater international use of the renminbi, including CIPS. Russia has developed SPFS, while India has promoted rupee settlement and UPI. Brazil's Pix represents another large-scale domestic digital-payment infrastructure.

However, the existence of alternative payment infrastructure does not mean that the dollar automatically disappears.

A company may conduct one transaction in yuan, another in dollars and another through a bilateral local-currency mechanism.

Therefore:

Payment-system diversification ≠ currency replacement.

 

5. BRICS Payment Architecture

The case identifies seven principal areas of BRICS payment cooperation:

National-currency use

Local-currency settlement

Linking domestic payment systems

Correspondent banking cooperation

CBDC interoperability

Reduction of transaction costs

Greater payment resilience

The 2024 Kazan Declaration is described in the source material as supporting faster, cheaper, safer and more inclusive cross-border payments and welcoming local-currency use. The source also emphasises that the BRICS Cross-Border Payments Initiative was voluntary and non-binding.

This is analytically different from establishing a single BRICS currency.

 

6. UPI as India's Strategic Payment Infrastructure

UPI is not a currency.

It is a payment rail.

This distinction is central to the research model.

The source material notes that NPCI International Payments Limited has worked to deploy UPI and RuPay internationally and identifies international acceptance/cooperation involving countries and territories including Bhutan, France, Mauritius, Nepal, Singapore, Sri Lanka and the UAE, depending on the specific implementation.

UPI can potentially support:

tourism;

remittances;

education payments;

e-commerce;

small-value business transactions;

digital services;

small exporters.

However, UPI does not eliminate foreign-exchange conversion or settlement requirements.

 

7. Case Illustration: Indian Machinery Importer and Brazil

Consider an Indian machinery importer purchasing equipment from Brazil.

Conventional model

Indian Rupee → Bank → Foreign exchange → Dollar/intermediary banking → Brazilian bank → Brazilian Real

Potential friction includes:

intermediary banks;

currency conversion;

compliance;

settlement time;

fees;

liquidity requirements.

Interoperable model

Indian buyer → UPI-linked interface → INR settlement → FX mechanism → Brazilian payment system → BRL

The case explains that such a system could reduce transaction friction, but exchange-rate determination, liquidity, fraud prevention, compliance and final settlement would remain necessary.

 

8. Research Problem

Research Problem

To what extent can India's UPI, local-currency settlement and BRICS payment interoperability improve cross-border transaction efficiency while preserving financial stability and strategic flexibility?

 

9. Research Objectives

The study has six objectives:

To clarify the distinction between KITO, KAITO, currencies and payment systems.

To examine the structural foundations of US-dollar dominance.

To examine BRICS payment diversification.

To analyse UPI's potential international role.

To examine the relationship between payment interoperability and transaction efficiency.

To identify the financial, technological and geopolitical constraints affecting India's gains.

 

10. Research Questions

RQ1: Is KITO a recognised international trade or BRICS settlement currency?

RQ2: Can UPI facilitate selected cross-border transactions?

RQ3: Does local-currency settlement reduce dependence on dollar conversion?

RQ4: Does payment interoperability improve transaction efficiency?

RQ5: What factors determine adoption of alternative payment infrastructure?

RQ6: Can BRICS payment diversification coexist with continued dollar use?

 

11. Conceptual Research Model

The paper develops the following analytical model:

Payment Interoperability

↓

Transaction Speed + Cost Efficiency + Accessibility

↓

Cross-Border Payment Adoption

↓

Local-Currency Settlement

↓

Reduced Selected Dollar-Conversion Dependence

↓

Greater Rupee Internationalisation

↓

India's Strategic Payment Flexibility

But the relationship is moderated by:

FX Risk + Liquidity + Regulation + Cybersecurity + Trust + Geopolitical Risk

This means that technological efficiency does not automatically produce monetary internationalisation.

 

12. Hypotheses

H1

Payment interoperability has a positive relationship with perceived cross-border transaction efficiency.

H2

Greater use of local-currency settlement is associated with lower dependence on dollar conversion in selected bilateral transactions.

H3

Greater awareness and usability of UPI are positively associated with willingness to use UPI-linked cross-border payments.

H4

Institutional trust is positively associated with adoption intention for cross-border digital payment systems.

H5

Perceived foreign-exchange and regulatory risks negatively influence adoption intention.

H6

Payment interoperability has a positive relationship with perceived strategic flexibility for Indian businesses.

 

13. Variables and Measurement Framework

Variable	Type	Possible indicators
Payment interoperability	Independent	Connectivity, compatibility, accessibility
Transaction efficiency	Dependent	Cost, speed, settlement time
UPI usability	Independent	Ease, convenience, familiarity
Local-currency settlement	Independent	INR acceptance, conversion reduction
Institutional trust	Moderator	Banking confidence, regulatory trust
FX risk	Moderator	Exchange-rate uncertainty
Regulatory complexity	Moderator	KYC, AML, taxation
Cybersecurity concern	Moderator	Fraud, data protection
Adoption intention	Dependent	Willingness to use
Strategic flexibility	Dependent	Payment choice, diversification

A five-point Likert scale can be used for empirical testing:

1 = Strongly Disagree
2 = Disagree
3 = Neutral
4 = Agree
5 = Strongly Agree

 

14. Statistical Analysis Framework

Because the supplied case contains qualitative/policy evidence rather than a respondent-level dataset, the following statistical section is presented as the proper empirical design and analysis structure, not as fabricated survey results. The source itself explicitly describes the methodology as qualitative case-study and policy analysis.

14.1 Descriptive Statistics

The empirical study should first calculate:

Frequency

Percentage

Mean

Standard deviation

Minimum

Maximum

For example:

Variable	Mean	SD	Interpretation
UPI usability	To be calculated	To be calculated	Survey dependent
Payment efficiency	To be calculated	To be calculated	Survey dependent
Local-currency awareness	To be calculated	To be calculated	Survey dependent
FX-risk concern	To be calculated	To be calculated	Survey dependent
Cybersecurity concern	To be calculated	To be calculated	Survey dependent
Adoption intention	To be calculated	To be calculated	Survey dependent

 

15. Reliability Analysis

For a primary survey, Cronbach's Alpha should be calculated for multi-item constructs.

Formula

α = [k/(k−1)] [1 − Σσ²ᵢ / σ²ₜ]

where:

k = number of items;

σ²ᵢ = variance of each item;

σ²ₜ = variance of the total score.

The purpose is to establish internal consistency among questionnaire items.

A commonly used research convention is:

Alpha	Interpretation
< 0.60	Low
0.60–0.69	Marginal
0.70–0.79	Acceptable
0.80–0.89	Good
≥0.90	Very high

These are methodological benchmarks, not results from the supplied case.

 

16. Chi-Square Analysis

A Chi-square test of independence can test whether categorical variables are associated.

Example

UPI awareness × Willingness to use cross-border UPI

	Willing	Not willing
High awareness	O₁	O₂
Low awareness	O₃	O₄

Hypothesis

H₀: UPI awareness and adoption intention are independent.

H₁: UPI awareness and adoption intention are associated.

Decision rule:

p < 0.05 → reject H₀

p ≥ 0.05 → fail to reject H₀

The actual χ² value and p-value must come from collected observations.

 

17. Independent-Samples t-Test

A t-test can compare the mean adoption intention of two groups.

Example

Group A: Businesses with international-payment experience
Group B: Businesses without international-payment experience

Dependent variable:

Cross-border payment adoption intention

The test examines whether the observed mean difference is statistically significant.

Again, an actual t-statistic and significance value cannot legitimately be supplied without respondent-level observations.

 

18. ANOVA

One-way ANOVA can examine whether adoption intention differs across more than two categories.

For example:

Business size

Micro

Small

Medium

Large

Dependent variable:

Cross-border payment adoption intention

Hypothesis

H₀: Mean adoption intention is equal across all business-size groups.

H₁: At least one group has a different mean.

If the ANOVA is significant, post-hoc testing such as Tukey's HSD can identify which groups differ.

 

19. Correlation Analysis

Pearson correlation can examine relationships among the major constructs.

Variables	Expected relationship
UPI usability ↔ adoption intention	Positive
Payment efficiency ↔ adoption intention	Positive
Institutional trust ↔ adoption intention	Positive
FX risk ↔ adoption intention	Negative
Cybersecurity concern ↔ adoption intention	Negative
Regulatory complexity ↔ adoption intention	Negative

The correlation coefficient ranges from:

−1 to +1

A positive coefficient indicates that the variables move in the same direction, while a negative coefficient indicates an inverse relationship.

 

20. Multiple Regression Model

The principal empirical model can be expressed as:

AI = β₀ + β₁PI + β₂UPI + β₃TR + β₄FXR + β₅CSR + β₆REG + ε

Where:

AI = Adoption Intention

PI = Payment Interoperability

UPI = UPI Usability

TR = Institutional Trust

FXR = Foreign-Exchange Risk

CSR = Cybersecurity Risk

REG = Regulatory Complexity

ε = Error term

The regression would establish which factors significantly explain variation in adoption intention.

 

21. Expected Statistical Interpretation

The conceptual relationships developed from the case are:

Payment interoperability → (+) → efficiency

UPI usability → (+) → adoption

Institutional trust → (+) → adoption

FX risk → (−) → adoption

Cybersecurity risk → (−) → adoption

Regulatory complexity → (−) → adoption

These are research hypotheses, not claimed statistical findings.

 

22. Factor Analysis

Exploratory Factor Analysis can reduce numerous questionnaire items into underlying dimensions.

Potential factors emerging from the research framework are:

Factor 1 — Digital Efficiency

Speed

Convenience

Accessibility

Low transaction cost

Factor 2 — Financial Confidence

Exchange-rate confidence

Liquidity

Settlement certainty

Banking reliability

Factor 3 — Institutional Trust

Regulatory confidence

Consumer protection

Data protection

Legal certainty

Factor 4 — Risk Perception

Cybersecurity

Fraud

Sanctions

Regulatory uncertainty

Factor 5 — Strategic Adoption

UPI usage

Local-currency settlement

Cross-border expansion

Payment diversification

KMO and Bartlett's test should precede factor extraction.

 

23. Strategic Case Analysis

The case produces five strategic dimensions:

Dimension	Opportunity	Constraint
UPI	Fast digital payments	Not a currency
Rupee settlement	Greater INR usage	Liquidity/convertibility
BRICS cooperation	Diversification	Political differences
Local currencies	Reduced selected conversion	FX risk
Dollar system	Liquidity and global acceptance	Concentration/dependence

This demonstrates that India does not face a simple “Dollar versus BRICS” choice.

Instead, the emerging architecture can be represented as:

Dollar Network
↕
Regional Currency Networks
↕
BRICS Payment Cooperation
↕
UPI / National Payment Rails
↕
Businesses / Consumers

 

24. Major Risks

The case identifies seven major categories of risk.

24.1 Foreign-exchange risk

Local-currency settlement does not eliminate currency volatility.

24.2 Liquidity risk

A foreign company accepting rupees needs economically useful ways to hold, spend or invest those rupees.

24.3 Trade imbalance

Persistent bilateral trade imbalances can make local-currency accumulation unattractive.

24.4 Cybersecurity risk

Cross-border interoperability creates additional attack surfaces, including identity theft, QR fraud, malware and system disruption.

24.5 Data-governance risk

Payment information can reveal individuals, businesses, supply chains and strategic trade relationships.

24.6 Regulatory risk

KYC, AML, sanctions, tax, foreign-exchange and consumer-protection requirements must operate across jurisdictions.

24.7 Geopolitical risk

Companies may consider exposure to sanctions, dollar-market restrictions and compliance penalties when selecting payment channels.

 

25. Scenario Analysis

Scenario	Core characteristic	Indian implication
Limited interoperability	Selected foreign connections	Moderate opportunity
Local-currency expansion	More bilateral INR settlement	Greater currency-management requirement
BRICS payment bridge	Multiple national systems interconnected	Greater technological opportunity and governance complexity
Common BRICS currency	Shared settlement unit	Major institutional challenge
Fragmented payment blocs	Several competing systems	Higher compliance complexity
Multi-system coexistence	Dollar remains important while alternatives expand	Diversified payment choices

The source material describes the final scenario as the most plausible, while also emphasising that the dollar remains substantially ahead of other currencies in reserve usage.

 

26. India's Strategic Opportunity

The case identifies several areas where Indian businesses may benefit:

Exporters

Lower payment friction can improve working-capital cycles.

MSMEs

Smaller businesses may obtain easier access to international customers.

Tourism

Foreign visitors can potentially use connected digital-payment systems.

Remittances

Digital interoperability could reduce friction in selected corridors.

Fintech

Indian payment companies may obtain international infrastructure opportunities.

Banking

Indian banks can develop international rupee settlement and payment services.

Digital diplomacy

UPI can become part of India's digital-public-infrastructure partnerships.

The source specifically identifies SMEs, tourism, education, digital services, e-commerce, remittances and agricultural/food exports as potential areas of application.

 

27. Proposed Empirical Sample

For conversion of this case into a full empirical research paper, a survey can be conducted among:

Indian exporters;

importers;

MSME owners;

bankers;

fintech professionals;

international students;

remittance users;

international travellers;

financial experts.

Suggested sample

N = 300–500 respondents

Sampling may use a combination of purposive and stratified sampling.

 

28. Proposed Statistical Testing Matrix

Objective	Variable relationship	Statistical test
Measure respondent profile	Demographics	Frequency/Percentage
Measure construct reliability	Survey items	Cronbach Alpha
Examine categorical association	Awareness × adoption	Chi-square
Compare two groups	International vs domestic firms	t-test
Compare 3+ groups	Business-size categories	ANOVA
Examine relationships	Major constructs	Pearson correlation
Predict adoption	Multiple predictors	Regression
Identify dimensions	20–30 questionnaire items	Factor analysis
Test integrated model	Multiple latent constructs	SEM

 

29. Findings from the Case Evidence

The supplied case establishes six substantive findings.

Finding 1: KITO is not established as a recognised international trade currency.

Finding 2: Dollar dominance is supported by a broad economic and financial ecosystem rather than a single technology.

Finding 3: BRICS payment cooperation is principally concerned with diversification, local currencies and interoperability rather than an immediate common currency.

Finding 4: UPI has potential as an international payment rail.

Finding 5: Payment interoperability cannot remove FX risk, liquidity requirements, compliance or settlement issues.

Finding 6: India's major opportunity is increased payment and currency diversification while retaining access to existing international financial markets.

 

30. Managerial Implications

For Indian exporters

Develop the ability to accept multiple settlement currencies rather than depending exclusively on one channel.

For banks

Develop rupee liquidity, hedging and cross-border settlement capabilities.

For fintech companies

Focus on interoperability, cybersecurity, fraud detection and regulatory technology.

For MSMEs

Provide transparent FX pricing and simplified international payment documentation.

For regulators

Ensure that payment innovation develops alongside AML, cybersecurity, consumer protection and data-governance standards.

 

31. Policy Implications

The case supports a multi-rail strategy rather than an either/or strategy.

India can simultaneously maintain:

Dollar access

 

Euro/other international currencies

 

Rupee settlement

 

UPI internationalisation

 

BRICS payment cooperation

 

Bilateral local-currency mechanisms

The source specifically recommends preserving dollar-market access while expanding UPI, rupee liquidity and practical local-currency arrangements.

 

32. Proposed Integrated Model: INDIA MULTI-RAIL PAYMENT MODEL

IMPM — India Multi-Rail Payment Model

Global Dollar Network
↓
International Trade & Finance

↕

BRICS Payment Connectivity

↕

Local-Currency Settlement

↕

Rupee Liquidity

↕

UPI / Digital Payment Infrastructure

↓

Indian MSMEs + Exporters + Tourists + Students + Remittance Users

Moderating factors

Trust | Liquidity | FX Risk | Regulation | Cybersecurity | Geopolitics

This model captures the central conclusion of the case: India's objective need not be replacing one monetary system with another; it can be expanding the number of efficient and reliable payment channels available to Indian economic actors.

 

33. Limitations

The paper has several limitations.

First, BRICS payment arrangements continue to evolve. Second, public announcements may not provide complete technical implementation details. Third, local-currency settlement varies across bilateral relationships. Fourth, cryptoasset data can change rapidly. Finally, terms such as “BRICS Pay” can sometimes be used informally and should not automatically be interpreted as evidence of one fully operational unified BRICS network.

Most importantly, the supplied material does not contain a primary respondent dataset. Therefore, numerical statistical results such as α = 0.87, χ² = 14.32, p = 0.002, t = 3.41, etc. would be fabricated if presented as actual findings.

 

34. Conclusion

The central issue in the international payment debate is not the emergence of a mysterious “KITO currency.” The evidence supplied for this case does not establish KITO as a recognised international trade or BRICS settlement currency. The substantive transformation lies elsewhere: in the gradual development of local-currency settlement, payment interoperability, digital public infrastructure and alternative cross-border payment mechanisms.

For India, UPI provides an important technological foundation, but UPI itself is not a currency and cannot independently create reserve-currency status. Successful internationalisation of the rupee requires liquidity, convertibility, financial-market depth, institutional trust and effective regulation.

The research therefore conceptualises India's future through a Multi-Rail Payment Model, in which the dollar, rupee, local currencies, UPI and BRICS-linked payment infrastructure can coexist.

The critical empirical question for future research is not simply:

“Will BRICS replace the dollar?”

It is:

“Which combination of payment infrastructure, currencies, liquidity, trust and regulation can reduce cross-border transaction friction while maintaining financial stability?”

That question provides a stronger foundation for an empirical study of India's emerging role in the international payment architecture.

References / Source Base

Federal Reserve information on the international role of the US dollar;

IMF-related reserve-currency information;

Bank for International Settlements analysis of cross-border payments;

Reserve Bank of India publications on rupee internationalisation and local-currency settlement;

NPCI information concerning international UPI deployment;

BRICS declarations and payment-related developments. 

Appendix A — Secondary Data Bank for Statistical Analysis

Table A1. International Role of Major Currencies, 2024

Indicator	US Dollar	Euro	China/Renminbi
International Currency Usage Index	64.9	23.9	3.1
Share of Global GDP (%)	26.1	14.9	16.8
Share of Global Trade (%)	13.4	17.4	12.8
Official FX Reserves (%)	57.8	19.8	2.2
International/Foreign-Currency Banking Claims (%)	56.4	20.3	—
International/Foreign-Currency Banking Liabilities (%)	63.2	16.3	—

Source: Federal Reserve, The International Role of the U.S. Dollar – 2025 Edition, using IMF, BIS, Refinitiv and related datasets.

Interpretation

The data demonstrate an important structural feature of the international monetary system. The United States accounted for 26.1% of global GDP in 2024, while the dollar's international currency-usage index was 64.9%. China had a larger GDP share than the euro area but a substantially smaller international-currency usage index.

This supports the argument that economic size alone does not determine international currency status.

 

Table A2. US Dollar Share of Official Foreign-Exchange Reserves

Year	Dollar (%)	Euro (%)	Renminbi (%)
2001	71.5	19.2	—
2005	66.5	23.9	—
2010	62.3	25.8	—
2015	65.8	19.2	—
2020	58.9	21.3	2.3
2021	58.8	20.6	2.8
2022	58.4	20.5	2.7
2023	58.4	19.9	2.3
2024	57.8	19.8	2.2

Source: IMF COFER data reproduced in the Federal Reserve's 2025 analysis.

Statistical observation

From 2001 to 2024, the dollar's reserve share declined from 71.5% to 57.8%, a reduction of 13.7 percentage points.

However, the 2024 figure remains substantially higher than the euro's 19.8% and renminbi's 2.2%.

Therefore, the data show diversification without disappearance of dollar dominance.

 

Table A3. Dollar Banking-Currency Position, 2024

Category	Dollar	Euro	Pound	Yen
International/foreign-currency banking claims	56.4%	20.3%	5.4%	4.9%
International/foreign-currency banking liabilities	63.2%	16.3%	4.0%	4.8%

Source: BIS locational banking statistics as reported by Federal Reserve.

Interpretation

The banking data reinforce the reserve-data finding. The dollar is not dominant merely because central banks hold dollars. It is also deeply embedded in international borrowing, lending and deposit relationships.

 

Appendix B — India, UPI and Cross-Border Payment Data

Table B1. UPI as an International Payment Infrastructure

Dimension	Evidence from case
Nature	Instant-payment infrastructure
Currency	Not itself a currency
Indian institution	NPCI/NIPL ecosystem
Main domestic strength	Large-scale digital-payment adoption
International role	Cross-border payment connectivity
Potential users	Consumers, tourists, MSMEs, exporters
Potential applications	Tourism, remittances, e-commerce, education, small trade
Main limitation	Does not eliminate FX conversion
Major requirement	Interoperability between national payment systems

The original case correctly identifies UPI as a payment rail rather than a currency.

 

Table B2. Project Nexus and UPI Connectivity

The BIS's Project Nexus provides an important real-world reference point for analysing India's cross-border instant-payment strategy.

The BIS reported that the Nexus project aims to connect domestic instant-payment systems internationally. The participating systems included those of India, Malaysia, the Philippines, Singapore and Thailand, with the project moving toward live implementation. India participates through UPI.

Component	Role
India	UPI
Malaysia	Domestic instant-payment system
Philippines	Domestic instant-payment system
Singapore	Domestic instant-payment system
Thailand	Domestic instant-payment system
BIS	Coordination/advisory role
Objective	Cross-border instant-payment interoperability

Research significance

This provides a concrete example of the principle underlying the paper:

Domestic digital payment infrastructure → interoperability → cross-border payment capability

It is therefore stronger analytically than treating UPI internationalisation as merely a theoretical possibility.

 

Appendix B3 — Quantitative Dollar-Diversification Dataset

For statistical analysis, the following time series can be used directly.

Year	USD reserve share	Euro	RMB
2015	65.8	19.2	1.1
2016	65.4	19.1	1.1
2017	62.7	20.2	1.2
2018	61.8	20.7	1.9
2019	60.8	20.6	1.9
2020	58.9	21.3	2.3
2021	58.8	20.6	2.8
2022	58.4	20.5	2.7
2023	58.4	19.9	2.3
2024	57.8	19.8	2.2

Source: IMF COFER/Federal Reserve accessible data.

Trend calculation

Dollar decline, 2015–2024:

65.8 − 57.8 = 8.0 percentage points

RMB increase, 2015–2024:

2.2 − 1.1 = 1.1 percentage points

Thus, the numerical evidence supports gradual diversification, but it does not show a one-for-one transfer from dollars into renminbi.

 

Appendix B4 — International Currency Usage Index

Year	US Dollar	Euro	China
2022	65.9	24.5	2.7
2023	64.9	23.9	3.1
2024	66.6	23.8	2.6

Source: Federal Reserve calculation based on IMF, BIS, Refinitiv and other sources.

Observation

The 2024 index is:

USA = 66.6

Euro area = 23.8

China = 2.6

This indicates that the international role of the dollar is much broader than reserve holdings alone.

 

Appendix B5 — Statistical Comparison

Currency dominance ratio

Using the 2024 reserve shares:

Dollar / Euro = 57.8 / 19.8 = 2.92

Thus, the dollar's share of disclosed official reserves was approximately 2.9 times the euro's share.

Dollar / Renminbi

57.8 / 2.2 = 26.27

Thus, the dollar's reserve share was approximately 26 times the renminbi's share in 2024.

This does not mean that the dollar is 26 times stronger as a currency in every dimension; it refers specifically to the selected reserve-share indicator.

 

Appendix B6 — Payment-System Analytical Scorecard

For the research model, the major systems can be compared descriptively.

System	Country/Region	Primary function	Currency	Cross-border potential
UPI	India	Instant payment	INR-linked	High
CIPS	China	Interbank payment	RMB	High
SPFS	Russia	Financial messaging	Ruble/other	Regional
Pix	Brazil	Instant payment	BRL	Potentially high
SWIFT	Global	Financial messaging	Multi-currency	Very high

Important: “High” here describes the functional scope/potential of the infrastructure, not a statistical ranking of performance.

 

Appendix B7 — Proposed Empirical Dataset for Primary Survey

If you want the paper to contain real SPSS statistical analysis, Appendix B can additionally contain a respondent database.

Suggested sample: 500 respondents

Respondent category	Proposed number
Exporters	100
Importers	75
MSMEs	100
Bank professionals	75
Fintech professionals	50
International students/travellers	50
Remittance users	50
Total	500

This should be clearly labelled proposed sample until actual questionnaires are collected.

 

Appendix B8 — Questionnaire Data Structure

Each respondent can be coded as follows:

Variable	Code
Respondent ID	ID
Age	AGE
Gender	GEN
Occupation	OCC
Business type	BUS
International transaction experience	ITE
UPI awareness	UPIA
UPI usability	UPIU
Payment efficiency	PE
Local-currency awareness	LCA
Institutional trust	TRUST
FX-risk concern	FXR
Cybersecurity concern	CYBER
Regulatory concern	REG
Adoption intention	ADOPT
Strategic flexibility	SF

Each attitudinal variable can be measured on a 1–5 Likert scale.

 

Appendix B9 — Statistical Hypothesis Testing Table

Hypothesis	Test	Required statistic	Decision
H1: Interoperability → efficiency	Correlation/Regression	r, β, p	Based on actual data
H2: Local currency → lower dollar conversion	Correlation/Regression	β, p	Based on actual data
H3: UPI usability → adoption	Regression	β, p	Based on actual data
H4: Trust → adoption	Regression	β, p	Based on actual data
H5: FX risk → adoption	Regression	β, p	Based on actual data
H6: Interoperability → strategic flexibility	Regression	β, p	Based on actual data

 

Appendix B10 — Recommended SPSS Output Sequence

The final empirical paper should present the statistical analysis in this order:

1. Demographic profile

↓

2. Descriptive statistics

↓

3. Reliability — Cronbach Alpha

↓

4. KMO + Bartlett's test

↓

5. Factor analysis

↓

6. Chi-square

↓

7. t-test

↓

8. ANOVA

↓

9. Pearson correlation

↓

10. Multiple regression

↓

11. Hypothesis decision table

↓

12. Integrated findings

Appendix C — Statistical Analysis of the International Payment System

C1. Dollar Reserve-Share Trend Analysis, 2015–2024

The Federal Reserve's 2025 dataset provides annual IMF COFER data for major reserve currencies. The US-dollar share fell from 65.8% in 2015 to 57.8% in 2024, while the renminbi increased from 1.1% in 2016 to 2.2% in 2024.

Year	USD (%)	Euro (%)	RMB (%)
2015	65.8	19.2	—
2016	65.4	19.1	1.1
2017	62.7	20.2	1.2
2018	61.8	20.7	1.9
2019	60.8	20.6	1.9
2020	58.9	21.3	2.3
2021	58.8	20.6	2.8
2022	58.4	20.5	2.7
2023	58.4	19.9	2.3
2024	57.8	19.8	2.2

C2. Change Analysis

Dollar change, 2015–2024

65.8 − 57.8 = 8.0 percentage points decline

Percentage decline relative to 2015:

8.0 / 65.8 × 100 = 12.16%

Renminbi change, 2016–2024

2.2 − 1.1 = 1.1 percentage points increase

Percentage increase relative to 2016:

1.1 / 1.1 × 100 = 100%

However, the RMB started from a very small base. Therefore, the 100% relative increase should not be interpreted as equivalent to the dollar's international position.

Research interpretation

The statistical evidence demonstrates currency diversification, but not a direct dollar-to-renminbi replacement.

The dollar remained at 57.8% of disclosed official reserves in 2024, compared with 19.8% for the euro and 2.2% for the renminbi.

 

Appendix C3 — International Currency Usage

The Federal Reserve's broader International Currency Usage Index incorporates:

foreign-exchange reserves;

foreign-exchange transactions;

foreign-currency debt issuance;

international banking claims;

international banking liabilities.

For 2024 the index was:

Economy/Currency	International Currency Usage Index	GDP Share	Trade Share
United States / USD	64.9	26.1	13.4
Euro area / Euro	23.9	14.9	17.4
China / RMB	3.1	16.8	12.8

C3.1 Important statistical finding

China's GDP share (16.8%) was larger than the euro area's (14.9%), yet the RMB's international currency-usage index (3.1) was far below the euro (23.9) and dollar (64.9).

This provides quantitative evidence for the paper's argument that:

Economic size alone does not create international-currency dominance.

Financial-market depth, liquidity, institutional confidence, reserve-asset availability and network effects matter as well. The Federal Reserve specifically identifies the depth and liquidity of US financial markets and the supply of safe dollar assets as important foundations of the dollar's international role.

 

Appendix C4 — International Payment Share

The Federal Reserve's accessible dataset also reports the currency composition of international payments on SWIFT.

Year	USD (%)	Euro (%)	GBP (%)	RMB (%)
2014	41.6	31.2	8.7	1.6
2015	44.0	28.3	8.4	2.2
2016	41.8	30.8	8.1	1.9
2017	40.6	32.6	7.3	1.8
2018	39.0	34.2	7.2	1.8
2019	41.3	33.1	6.8	1.9
2020	40.4	34.3	6.8	1.8
2021	39.3	37.5	6.2	2.2
2022	41.2	35.7	6.5	2.3
2023	44.0	29.5	6.8	2.9
2024	47.0	22.8	7.0	4.4

Source: Federal Reserve accessible data, based on SWIFT and Bloomberg.

Statistical observation

Between 2014 and 2024:

USD international-payment share

47.0 − 41.6 = +5.4 percentage points

RMB international-payment share

4.4 − 1.6 = +2.8 percentage points

This indicates that the RMB's international payment presence has increased, but from a substantially smaller base.

 

Appendix C5 — India and Cross-Border Instant Payments

India provides a particularly important case because UPI is already a large domestic instant-payment infrastructure.

The BIS reports that Project Nexus is designed to connect domestic instant-payment systems and potentially allow cross-border payments to move within approximately 60 seconds in most cases. India joined the multilateral project alongside Malaysia, the Philippines, Singapore and Thailand.

The BIS also specifically identifies India's UPI as one of the systems that can be linked across borders and notes the existing UPI–PayNow connection with Singapore.

Table C5.1 — Nexus Analytical Structure

Country	Domestic payment system	Role in Nexus
India	UPI	Participating system
Malaysia	Domestic IPS	Participating system
Philippines	Domestic IPS	Participating system
Singapore	FAST/PayNow ecosystem	Participating system
Thailand	Domestic IPS	Participating system
Indonesia	Participating in later Nexus structure	Participating system

The BIS describes Nexus as a standardized multilateral approach rather than requiring every national payment system to build a separate bilateral connection with every other country.

 

Appendix C6 — Statistical Logic of UPI Internationalisation

The research model can now be expressed quantitatively:

Independent variables

Payment interoperability

Digital-payment accessibility

Transaction speed

Transaction-cost reduction

Intermediate variable

Cross-border payment adoption

Outcome

International payment diversification

Moderating variables

FX risk

Liquidity

Regulation

Cybersecurity

Institutional trust

Thus:

Interoperability → Efficiency → Adoption → Payment Diversification

while:

FX Risk + Liquidity + Regulation + Cybersecurity

can weaken the relationship.

 

Appendix C7 — Regression Model for Secondary-Data Research

Because the available published currency data are time-series/aggregate data rather than individual respondents, the regression should not be presented as a respondent-level behavioural model.

A suitable secondary-data model is:

USD Reserve Shareₜ = α + β₁ Timeₜ + εₜ

This estimates the direction of the dollar reserve-share trend.

A second model could examine:

RMB Reserve Shareₜ = α + β₁ Timeₜ + εₜ

However, the sample is only about 10–25 annual observations depending on the selected period, so statistical inference must be treated cautiously.

 

Appendix C8 — CAGR Analysis

For the dollar reserve share:

Initial value = 65.8
Final value = 57.8
Period = 9 years

CAGR:

[(57.8 / 65.8)^(1/9) − 1] × 100

≈ −1.43% per year

For the RMB:

Initial value = 1.1
Final value = 2.2
Period = 8 years

CAGR:

[(2.2 / 1.1)^(1/8) − 1] × 100

≈ 9.05% per year

Interpretation

The RMB shows a much higher growth rate because it began from a very low base. Therefore, CAGR should be read alongside absolute percentage-point changes.

 

Appendix C9 — Comparative Payment Architecture

Indicator	Dollar	RMB	UPI
Currency	USD	RMB	INR-linked payment rail
Sovereign currency	Yes	Yes	No
Reserve-currency role	Major	Limited	No
Payment infrastructure	Extensive	Extensive	Extensive domestically
Cross-border function	Extensive	Expanding	Expanding
Main strength	Liquidity/network	Chinese trade/financial infrastructure	Instant digital payments
Main limitation	Dependence/network concentration	Lower global currency usage	Not itself a currency

This table reinforces one of the central conceptual findings of the case:

USD/RMB = currencies

UPI = payment infrastructure

The distinction is essential to avoid treating a payment platform as a replacement currency.

 

Appendix C10 — Empirical Hypothesis Matrix

Hypothesis	Variable 1	Variable 2	Method
H1	Interoperability	Transaction efficiency	Correlation/Regression
H2	Local-currency settlement	Dollar-conversion dependence	Correlation/Regression
H3	UPI usability	Adoption intention	Regression
H4	Institutional trust	Adoption intention	Regression
H5	FX risk	Adoption intention	Regression
H6	Cybersecurity concern	Adoption intention	Regression
H7	Payment diversification	Strategic flexibility	Regression

 

Appendix C11 — Hypothesis Decision Format

For the final empirical paper, use this format:

Hypothesis	Statistical value	p-value	Decision
H1	Actual result	Actual result	Accepted/Rejected
H2	Actual result	Actual result	Accepted/Rejected
H3	Actual result	Actual result	Accepted/Rejected
H4	Actual result	Actual result	Accepted/Rejected
H5	Actual result	Actual result	Accepted/Rejected
H6	Actual result	Actual result	Accepted/Rejected
H7	Actual result	Actual result	Accepted/Rejected

These cells should only be populated after actual observations are analysed.

 

Appendix C12 — New Empirical Finding

The combined secondary evidence creates a particularly useful research finding:

Finding: Payment diversification is occurring at two different levels.

Level 1 — Currency diversification

The dollar's share of official reserves has declined over the long term, while other currencies have gained some share.

Level 2 — Payment-infrastructure diversification

Countries are developing and connecting instant-payment systems, with Project Nexus explicitly designed to connect domestic systems internationally.

Therefore:

The international monetary system is experiencing both currency diversification and payment-infrastructure diversification, but the two processes are not identical.

This is a strong central finding for your research paper.

 

Appendix C13 — Revised Research Model

INDIA'S DIGITAL-MULTI-CURRENCY PAYMENT MODEL

US Dollar

↓
Global finance + reserves + trade + banking

Rupee

↓
Indian trade + local-currency settlement

UPI

↓
Digital payment infrastructure

BRICS / Regional Payment Connectivity

↓
Cross-border interoperability

Indian Economic Actors

→ Exporters
→ Importers
→ MSMEs
→ Tourists
→ Students
→ Remittance users
→ Fintech companies

Moderators

Liquidity | FX Risk | Trust | Cybersecurity | Regulation | Geopolitics

 

Appendix C14 — Final Statistical Interpretation

The actual secondary data do not support a simple conclusion that the dollar is being replaced.

They show three measurable developments:

The dollar's reserve share has declined from its historical peak.

The RMB and other currencies have gained some international presence.

Cross-border payment infrastructure is becoming more interconnected, including India's UPI.

At the same time, the dollar remained at about 58% of disclosed official reserves in 2024, and the Federal Reserve describes it as still the dominant reserve currency.

The BIS's Project Nexus provides concrete evidence that the payment-rail side of the international system is changing, with the objective of improving speed, cost, transparency and access in cross-border payments.