1. Introduction

Conflicts of interest between national agendas and global economic governance are fundamental issues which undermine the decisions made by the International Monetary Fund (IMF) and the World Trade Organization (WTO). These organisations, which are designed to ensure stability and fairness, often host disputes between members due to the conflicting interests of their member countries. The IMF, a macroeconomic institution dealing with international financial stability and the coordination of balance of payments, creates tension when its imposed measures clash with domestic social welfare and political stability priorities. The WTO, an institution governing world trade regulations, is subject to ongoing tension between national sovereignty and international regulations.

This paper will argue that one important lens to view these institutional conflicts through is how they address unfair trade practices, namely dumping and non-tariff barriers (NTBs). These practices are pressure points in the global economic order. Therefore, this research shifts its focus to analyse how WTO and IMF work, and often struggle, to mediate the competing interests between dumping and NTBs, and how digital trade may be the modern solution.

2. Dumping and Non-Tariff Barriers

Dumping is the export of goods at prices below their normal value, often enabled by state subsidies, which directly attack the competitive landscape of a targeted country’s industry. The logical response is the imposition of anti-dumping duties, which, should they be used to stop the exporting countries’ influence, can be weaponised for protectionism. For instance, the 2009 US-China Steel Dispute highlights a cycle of accusation and retaliation, of dumping and anti-dumping. In this case, China was accused of subsidising its steel industry and dumping surplus steel pipes, leading the US and others to impose duties. China then challenged this at the WTO Dispute Settlement Court. This case is testament to how the WTO’s rule-based system is vulnerable to be challenged by powerful member nations.

Similarly, non-tariff barriers, such as import quotas, represent a source of conflict. While often justified in their use, they can be easily employed as a protectionist measure. The WTO’s role is to distinguish between legitimate and unfair NTBs, particularly through its Sanitary and Phytosanitary (SPS) Agreement (WTO, n.d.). The SPS mandates that measures must be based on scientific evidence. In contrast, the IMF engages with NTBs indirectly through policy recommendations and lending conditions that encourage trade liberalisation and the removal of trade barriers (IMF, n.d.).

There are clear differences in institutional responses: the WTO takes a direct, rules-based approach to settling disputes on dumping and NTBs; the IMF employs a more indirect approach aimed at removing the underlying financial issues that could lead to conflicts. However, both approaches are restricted by political influence, unclear information and a lack of transparency, delaying any effective conflict resolution.

The WTO and IMF both play important roles in stopping trade disputes caused by dumping and NTBs: the WTO provides countries with a clear set of rules, while the IMF helps by promoting fair trade when lending. However, countries do not always stick to the WTO’s rules, especially when national interests are at stake. The 2009 US-China Steel Dispute shows that while the WTO provides clear rules, governments often prioritise national interests. This shows the difficulty of keeping global trade fair when politics and economics clash, especially with more powerful countries. However, these issues and conflicts can be resolved through the growth of digital trade.

3. Digital Trade

The digital transformation of global trade is based on three interconnected technological pillars: blockchain, smart contracts and artificial intelligence (AI). These technologies jointly address long-standing challenges such as trade transparency, process efficiency and decision-making automation: blockchain provides an immutable, distributed ledger for recording transactions; smart contracts enable self-executing contracts based on predetermined parameters; and AI extracts big data to make inferences, predict outcomes and optimise processes. Together, they form a strong digital trade infrastructure capable of reducing costs, minimising frictions and enabling more inclusive global trade ecosystems. The intersection of these technologies is particularly significant for international institutions like the IMF and the WTO, which must retool trade governance and arbitrate conflicts of interest among members via technology neutrality and data-driven governance.

3.1 Blockchain

A blockchain is a distributed database or ledger shared among nodes in a computer network. They are known for their important role in cryptocurrency systems, maintaining a secure, decentralised record of transactions, but are not limited to cryptocurrency applications. Blockchains can be used to make data immutable across any industry (Hayes, 2025).

The operation of blockchain involves a sequential process that ensures security and consensus:

• Transaction Recording: A blockchain transaction records the movement of physical or digital assets between parties, containing details such as participants, timing, location, conditions and asset quantity (Hayes, 2025).
• Consensus Mechanism: Most participants on the distributed network must agree that a transaction is valid. The rules of agreement vary by network type but are established at the network’s inception (Hayes, 2025).
• Block Formation: Verified transactions are written into blocks equivalent to pages in a ledger book. Each block contains a cryptographic hash that links it to the previous block, creating a secure chain where modifying any block would require altering all subsequent blocks (Hayes, 2025).
• Ledger Distribution: The system distributes the latest copy of the central ledger to all participants, ensuring everyone has access to the same information (Hayes, 2025).

Blockchain has found applications across numerous industries relevant to trade, including supply chain management, financial services and the energy sector. Retail companies use blockchain to track the movement of goods between suppliers and buyers. Amazon, for instance, has patented a distributed ledger system to verify authentic goods on its platform by allowing manufacturers, couriers, distributors and users to add events to the ledger after registration. Similarly, traditional financial systems use blockchain to manage online payments, accounts and market trading. Singapore Exchange Limited uses blockchain technology to build more efficient interbank payment accounts, solving challenges like batch processing and manual reconciliation of thousands of financial transactions. Meanwhile, energy companies use blockchain to create peer-to-peer energy trading platforms and streamline renewable energy access. Blockchain enables homeowners with solar panels to sell excess energy to neighbours, with smart metres creating transactions and blockchain recording them.

3.2 Smart Contracts

Like blockchain, smart contracts follow a sequential process:

• Contract Creation: Parties define terms and conditions, which are translated into code. The contract is then deployed to the blockchain network (IBM, n.d.).
• Contract Deployment: The smart contract is distributed across the blockchain, with each node in the network possessing a copy. This decentralisation ensures consistent execution and maintains transparency.
• Execution Trigger: External inputs, such as user transactions or outputs from other contracts, trigger contract execution when predefined conditions are met.
• Automatic Execution: The contract processes input data, executes encoded instructions and records results on the blockchain. This process is immutable and transparent: once executed and recorded, conditions cannot be changed or deleted. 
• Network Verification: All nodes in the network verify the execution, ensuring consensus and validity of the transaction (PWC, n.d.).

Smart contracts have numerous applications in trade finance, supply chain management, and customs and compliance. Smart contracts can automate payment releases upon verification of shipment delivery, reducing processing time from days to minutes. Blockchain and smart contracts are used together to create ecosystems of trust for global trade, reducing friction and risk while easing trading processes (IBM, n.d.). Companies like Home Depot use smart contracts on blockchain to quickly resolve disputes with vendors. Through real-time communication and increased supply chain visibility, they build stronger supplier relationships while saving time for critical work and innovation (IBM, n.d.). Additionally, smart contracts can automatically verify regulatory compliance, calculate tariffs and process payments by accessing trusted data sources, significantly reducing border delays and administrative burdens.

3.3 Artificial Intelligence

Artificial intelligence can be used to analyse large datasets, simplify and scale trends, and uncover insights for data analysts. AI data analytics encompasses various techniques, including: machine learning algorithms that extract patterns or make predictions on large datasets; deep learning that uses neural networks for complex tasks like image recognition and time-series analysis; and natural language processing (NLP) that derives insights from unstructured text data (Murphy, 2025). AI data analytics is designed to support, automate and simplify each stage of the data analysis journey, from collection and preparation to interpretation and decision-making (Murphy, 2025). In the context of digital trade, AI enables the processing of vast amounts of transaction data, market information and regulatory content to generate actionable insights that humans alone could not efficiently derive.

AI enhances each stage of the data analysis process:

• Data Collection: AI tools help with ingesting data from multiple sources, including structured and unstructured data relevant to trade patterns, market conditions and regulatory changes. Tools like virtual phone numbers streamline data collection from various communication channels, ensuring AI algorithms have sufficient input to learn effectively (Murphy, 2025).
• Data Preparation: AI automates the cleaning and organising of data for analysis, handling tasks like identifying outliers, managing empty values and normalising datasets. This is particularly valuable as data analysts traditionally spend 70-90% of their time cleaning and preparing data (Murphy, 2025).
• Pattern Recognition and Analysis: Machine learning models trained on prepared data extract insights and patterns. These models can identify correlations, anomalies and trends across massive datasets that would be imperceptible to human analysts.
• Insight Interpretation: AI helps analysts interpret trends and insights for more informed decision-making, often through natural language generation that explains findings in plain language.

Several AI capabilities are particularly valuable for digital trade applications. For instance, AI can build predictive and forecasting models that anticipate market movements, demand fluctuations and supply chain disruptions. Businesses can leverage SQL to build, train and deploy batch predictive models directly within data warehouses (Nweje & Taiwo, 2025). Moreover, AI is able to run sentiment analysis on datasets to parse positive, negative and neutral scores. This is valuable for understanding customer feedback on social media or product reviews, developing market research through competitor analysis and assessing campaign effectiveness (Nweje & Taiwo, 2025). AI is also valuable in analysing unstructured data in images and videos to extract valuable information, streamline processes and enhance decision-making. This is particularly useful for verifying shipment contents, monitoring port activity and inspecting goods quality without requiring a physical presence.

The implementation of AI for data analytics in trade contexts offers significant benefits, including cost reduction and time savings. 54% of businesses report that implementing AI led to cost savings by automating repetitive tasks that previously required human effort. Similarly, AI analyses large volumes of data far quicker than humans, enabling real-time insights critical for time-sensitive trade decisions, and identifies complex patterns and relationships in data that humans might miss, leading to more informed strategic decisions. Moreover, AI systems can handle exponentially increasing data volumes without proportional increases in processing time or costs.

However, AI integration also poses unique challenges surrounding data quality, bias, security and privacy. AI analytics tools are only as good as the data they process, making data cleaning and formatting essential prerequisites. AI is also frequently trained on homogenous datasets, meaning if source data contains biases, these can be replicated in AI algorithms and outputs. Finally, using AI often involves sharing data with third-party platforms, creating potential vulnerabilities for sensitive trade information.

4. Applying the Fix: A Digital Solution to Conflict Resolution

Conflicts of interest in the WTO’s dispute settlement and the IMF’s terms for conditional borrowing highlight the lack of uniformity in institutional decision-making. Blockchain technology, smart contracts and artificial intelligence have the potential to revolutionise the business digital concept by enabling trade technology solutions that are devoid of regional jurisdiction or techno-bias.

4.1 Dumping Disputes: The US-China Steel Case Reimagined

The 2009 US-China Steel Dispute exemplifies the flaws in the current anti-dumping process: lengthy investigations based on potentially contested data, subjective calculations of dumping margins and measures that often escalate rather than resolve trade tensions. This section reimagines the case using a digitally transformed process.

Instead of the US Department of Commerce conducting its own investigation using a methodology China could challenge, the WTO could pilot a voluntary, neutral analytics service under its committees (e.g., AD, SPS/TBT), where parties submit agreed datasets for a third-party algorithmic check of dumping margins or SPS evidence; any remedy remains a member decision under the DSU. Customs execution continues nationally; automation, if used, follows domestic law. Both parties would submit encrypted data – including Chinese production costs, domestic prices and US import prices – to this neutral system. The AI would run a transparent, pre-agreed algorithm to calculate an objective “normal value” and dumping margin. This removes the immediate conflict over methodological bias, centring the debate on the accuracy of the input data rather than the calculation itself.

To prevent conflicts over data accuracy, China could incentivise its steel producers to record key data (e.g., raw material input costs, energy consumption and government subsidy receipts) on a permissioned blockchain. This creates an immutable, time-stamped record. During a dispute, the US investigation authority could be granted temporary, auditable access to verify specific data points without seeing competitively sensitive full ledgers. This provides a trusted, verifiable foundation for the AI’s analysis, drastically reducing the investigation period from years to months.

If a violation is found, the WTO’s Dispute Settlement Body would not just authorise tariffs; it would approve the deployment of a smart contract. This contract would be integrated with US customs databases and automatically apply the precise anti-dumping duty to the specific Harmonized System codes for Chinese steel pipes. Most importantly, the WTO’s Sunset Clause is hardcoded. The smart contract would be programmed to automatically self-destruct after five years, eliminating the political inertia that often keeps protectionist duties in place long after they are necessary. A new duty would require a new, AI-verified investigation.

This system transforms anti-dumping from a political weapon into a precise, surgical and temporary regulatory tool. It protects domestic industries from genuine unfair trade while ensuring measures are based on neutral data and are not permanent barriers, thereby reducing the incentive for retaliatory trade wars.

4.2 Non-Tariff Barriers: The EU Beef Hormone Ban Revisited

The 1998 US-EU Beef Hormone Dispute highlights the conflict between national sovereignty over health standards and the WTO’s requirement for science-based SPS measures. The dispute centred on the credibility and interpretation of scientific evidence, aiming to hold countries accountable for conducting an unbiased investigation. This section revisits the case to highlight the benefits of a digitally-transformed process.

The WTO’s SPS Committee would maintain an AI model trained on global scientific literature, including studies from the Codex Alimentarius, WHO and peer-reviewed journals. When the EU proposes a new measure, it must submit its scientific justification to this AI. The tool would generate a neutral report analysing the strength of the evidence, identifying consensus and highlighting any gaps or outlier studies. This provides an objective baseline for discussion, moving the debate from “we feel it’s dangerous” vs. “you’re being protectionist” to “the current scientific evidence suggests X, so measure Y is proportionate/disproportionate”.

Instead of a blanket ban on all US beef, the solution becomes precision regulation. US beef producers who do not use hormones can have their cattle certified and their hormone-free status immutably recorded on a blockchain at every step (e.g., farm, feedlot and slaughterhouse). EU importers and customs officials can instantly verify this compliance by scanning a QR code, allowing safe beef to enter while blocking non-compliant shipments. This transforms an absolute NTB into a targeted, transparent and less trade-distortive measure.

This approach respects national sovereignty and the precautionary principle while ensuring it is not used as a disguised restriction on trade. It creates a win-win: EU consumers get their desired safety guarantees and compliant US producers maintain market access, resolving the core conflict that led to decades of litigation.

4.3 IMF Conditionality: Beyond Austerity Conflicts

The conflict between IMF-imposed austerity and domestic social stability arises from a lack of trust in how funds are used and the perception of punitive, one-size-fits-all conditions. The following evidences how a digital approach could transform the process.

As a condition for a loan, a recipient government agrees to run its core public finances on a permissioned blockchain. Budget allocations, tax revenues and procurement contracts are recorded immutably. The IMF (and a country’s citizens) can see in real-time that funds are being spent on agreed-upon social programmes and infrastructure, not misappropriated. This builds trust that the loan is being used effectively.

Similarly, loan conditions are codified into smart contracts. Instead of a subjective review by IMF officials, the release of funds is triggered automatically by achieving transparent, verifiable milestones recorded on the blockchain. For example: “release $500M when the national budget deficit, as verified by the National Statistics Office’s blockchain-recorded data, falls below 5% of GDP” or “release funds for public works when the blockchain system shows the competitive tender process for a contract is complete”.

This system depoliticises the conditionality process. The government has complete clarity on how to unlock funding and the IMF gains confidence that its objectives are being met without micromanagement. It shifts the IMF’s role from a punitive overseer to a facilitator of verifiable, good governance, directly mitigating the conflict between international lenders and domestic populations.

4.4 Digital Solutions Summary

For the WTO, this digital toolkit does not change the rules but enforces them with unprecedented transparency and objectivity. It advances the institution from a reactive arbitrator of disputes to a proactive platform for preventing them through verifiable data sharing. For the IMF, the toolkit transforms its relationship with borrowers. It replaces opaque conditionality with a transparent, technical framework that builds trust and aligns the interests of the Fund, governments and citizens around accountable governance and measurable economic outcomes. By adopting this digital framework, both institutions can transition to being neutral administrators of a rules-based, data-driven global economic system.

In implementing a digital process, tools must respect data sovereignty, confidential business information and due-process rights. Participation should be voluntary, algorithms published and audited, and outputs advisory, not binding. For low-capacity members, any pilot must include capacity-building and fallbacks, so digitalisation does not become a new barrier.

5. Conclusion

This research has demonstrated that the recurring conflicts of interest in the IMF and WTO are embedded in the structure of their operations. The analysis of anti-dumping measures and non-tariff barriers reveals a critical flaw: the reliance on subjective, state-provided data and the interpretation of rules through a unilateral lens. This inevitably leads to disputes, retaliation and an eroding trust in the multilateral system. The US-China Steel Dispute and the US-EU Beef Hormone Dispute symbolises case studies of this systemic weakness. The processes designed to ensure fairness end up in geopolitical contests.

However, this paper has argued that the digital revolution offers a paradigm shift. The integrated toolkit of blockchain, smart contracts and artificial intelligence (AI) presents an opportunity to diminish these root conflicts. By adapting key processes for transparency, objectivity and automation, we can optimise the institutions. For the WTO, this means moving from dispute settlement to dispute prevention. An AI-powered platform for calculating dumping margins, backed by blockchain-verified supply chain data, can help de-escalation with an evidence-based databank; smart contracts can ensure that retaliatory duties are applied precisely and temporarily. With NTBs, AI can objectively assess the scientific basis of investigations, while blockchain replaces blunt, trade-disrupting bans. For the IMF, the digital toolkit enables a transition from overseer to facilitator of accountable governance. Blockchain-based transparency in public financial management builds trust by allowing the Fund and citizens to verify the effect of bailout funds. 

Implementing this technology will be challenging. Data sovereignty, the global digital divide, algorithmic bias and the governance of new digital platforms are substantial problems that need to be addressed. These hurdles are parameters for proper implementation. Therefore, this paper recommends the following:

1. Initiate Pilot Programmes: The WTO should launch a voluntary, sector-specific pilot (e.g., for agricultural products or green technology) to test the effect of AI. 
2. Establish Multi-Stakeholder Governance: The IMF and WTO must establish ethical guidelines, data privacy and legal frameworks for digital trade tools.
3. Prioritise Capacity Building: This transition needs major investments in digital infrastructure for developing nations, funded through IMF and WTO technical assistance programmes, to ensure the digital trade system is inclusive and fair.

The strategic adoption of blockchain, smart contracts and artificial intelligence is not just a technical upgrade; it is a necessary evolution for these institutions to administer a 21st-century global economy.

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