Formal verification is essential for ensuring the safety of smart contracts in decentralized finance (DeFi), but scaling these techniques across diverse blockchain ecosystems remains a challenge. In this talk, we present our experience making formal verification practical across multiple platforms, including the EVM, Solana, Stellar, and Sui. We discuss how automated reasoning techniques can be adapted to different execution models and programming paradigms while still providing strong correctness guarantees. We focus on what it takes to apply verification in real-world settings: handling complex DeFi primitives, integrating with development workflows, and maintaining usability for engineers. Drawing from verification projects with production protocols, we highlight key challenges and lessons learned in bringing formal methods from theory into practice.
Decentralized finance and stablecoin systems rely Stablecoins increasingly incorporate freeze, pause, and blacklist mechanisms to satisfy regulatory, compliance, and risk-management requirements. However, these controls introduce a critical temporal vulnerability when enforcement actions compete with transaction finality. This paper defines <b><i>Stablecoin Freeze Race Conditions</i></b> as a class of failures in which transfers, redemptions, or collateral movements execute successfully during the latency window between risk detection and freeze enforcement. We analyze how asynchronous control paths enable value escape even in fully permissioned stablecoins and demonstrate why governance authority alone is insufficient. A validator-level, logic-layer enforcement model is proposed to ensure atomicity between risk triggers and monetary state transitions under MiCA-aligned frameworks.
Blockchain technology, in simple words, is an innovative force that democratizes the methodologies of financial transactions by creating safe, traceable, and unalterable digital data. This research investigates how blockchain increases financial transparency in banking, government, and supply chain management for different sectors. It identifies block chain’s core features: decentralized ledgers, real-time auditing, and transparent data sharing, in total reducing information asymmetry and thus fraud, increasing public trust. This study will focus on the role of block chain in financial reporting, as immutable transaction records ensure audit-free error-free error checks and compliance with regulatory standards. The primary use cases for this are anticorruption government procurement systems, banking networks improving fraud detection, and supply chain platforms ensuring product traceability. Smart contracts integrated into financial processes help reduce intermediaries and promote accountability. The paper concludes with an overview of emerging trends in zero-knowledge proofs, decentralized finance, and blockchain-based governance systems that may transform the standards of transparency. Some policy recommendations for leaders are investment in blockchain research, the development of regulatory frameworks, and fostering cross-industry collaboration. Blockchain technology is expected to redefine financial transparency through accountability, fraud reduction, and increased public trust in digital economies.
Decentralized finance (DeFi) platforms have experienced a rapid increase in fraudulent activities such as price manipulation, wash trading, and anomalous transaction behavior, while traditional centralized fraud detection methods remain unsuitable due to privacy and regulatory constraints. This study proposes a privacy-preserving fraud detection framework using federated learning, enabling multiple decentralized entities to collaboratively train a machine learning model without sharing raw transaction data. A real-world decentralized exchange (DEX) dataset containing over 100,000 transactions is preprocessed and enhanced through feature engineering techniques capturing swap rate deviations, transaction volume anomalies, and temporal patterns. In the absence of labeled fraud data, a heuristic-based labeling approach is employed to simulate realistic fraud scenarios. A Logistic Regression model is trained across multiple distributed client nodes, with model parameters aggregated using the Federated Averaging (FedAvg) algorithm over several communication rounds. The experimental findings show that the federated model delivers results similar to centralized methods while preserving data privacy, proving it to be an efficient solution for secure and scalable fraud detection in decentralized financial environments.
Decentralized Finance (DeFi) lending and borrowing protocols enable investors to take leveraged long and short positions on digital assets without centralized intermediaries, but expose them to a distinctive form of risk: on-chain liquidation triggered by debt and collateral value fluctuations. In this work, we provide a detailed formalization of Aave's lending, borrowing, and liquidation mechanisms, grounded in the protocol's open-source implementation. In doing so, we propose a mathematical modeling of the risk of liquidation, including some stochastic approximations with the purpose of efficient analysis, with different applications. Among them, portfolio optimization problem.
This whitepaper introduces a new scientific framework for risk analysis in decentralized finance (DeFi) and cryptocurrency systems through the integration of artificial intelligence and robotic technologies. The research proposes an autonomous risk intelligence architecture that combines machine learning prediction models with robotic execution agents for real-time monitoring and mitigation of systemic financial risks. Key risk domains including market volatility, smart contract vulnerabilities, liquidity instability, oracle manipulation, governance threats, and regulatory uncertainty are formally classified and mapped to AI-driven detection and response mechanisms. The paper further introduces the Autonomous Risk Response Protocol (ARRP), a novel model for automated intervention using smart contracts, positioning AI-driven robotics as a foundational layer for next-generation decentralized financial security infrastructure.
<b><i>Bribe-Based Oracle Echoes</i></b> describe a class of incentive-layer attacks in which economically rational adversaries influence oracle participants to repeatedly reinforce distorted data outputs without directly compromising oracle infrastructure. Unlike overt oracle manipulation, these attacks exploit incentive alignment and tolerance-band logic to create feedback loops in which biased signals are echoed, amplified, and normalized across decentralized finance (DeFi) and stablecoin systems. This paper formalizes the threat model, identifies structural enablers, and demonstrates why decentralization alone is insufficient to prevent economically induced oracle drift.
Why does industrialization in some cases generate social consolidation and in other cases political conflict? This paper argues that the answer depends on how industrial finance is allocated. I develop a dynamic political-economy model in which the government channels external liquidity into industry under either centralized or decentralized finance. Under decentralization, adverse shocks harden budget constraints and permit replacement of inefficient incumbents by new entrepreneurs. Under centralization, by contrast, the government is more likely to refinance inefficient incumbents, soften budget constraints, and block entry. Industrialization then generates concentrated rents and a higher risk of conflict. I interpret late imperial Russia as a historically revealing case of this mechanism. Rather than treating Russia as the sole object of interest, the paper uses it to motivate a general theory of industrialization under monopoly.
This article is devoted to the contribution of cryptocurrencies and blockchain to the transformation of the world economy. It describes the basic principles of blockchain functioning, the evolution of major cryptocurrencies such as Bitcoin and Ethereum, as well as their practical application in the areas of decentralized finance, cross-border payments, and supply chain management. The study also analyzes the advantages of these technologies and their current limitations.
Open access
Security, Politics, and Digital Transformation
Digitalization and Economic Development in Agriculture
Nigeria continues to face one of the highest maternal mortality burdens globally, a situation that is closely linked to the low utilization of essential maternal and child health services. To address this challenge, the Nigeria State Health Investment Project (NSHIP) introduced two innovative financing mechanisms Performance-Based Financing (PBF) and Decentralized Facility Financing (DFF) aimed at improving the performance of primary healthcare facilities. This study compared the effectiveness of these financing approaches in enhancing service utilization while accounting for the selection bias commonly associated with non-experimental research designs. The study adopted a retrospective quantitative approach and analyzed data from 216 Primary Health Care (PHC) facilities located in Adamawa, Nasarawa, and Ondo States between 2022 and 2025. Propensity Score Matching (PSM) was used to create comparable groups of facilities based on important characteristics such as staffing levels and bed capacity. Thereafter, Analysis of Covariance (ANCOVA) and Welch’s ANOVA were employed to examine differences in service utilization across key maternal and child health indicators, including Outpatient Department (OPD) attendance, Antenatal Care (ANC) visits, Skilled Deliveries, Family Planning (FP) uptake, and Complete Vaccination Coverage (CVC). The results revealed that facilities operating under the PBF model consistently recorded higher utilization rates than those supported through DFF in several critical service areas. Specifically, PBF facilities achieved significantly better outcomes in OPD attendance (Mean Difference = 3,276; p < .01; Partial Eta Squared = 0.19), Skilled Deliveries (Mean Difference = 322; p < .001; Partial Eta Squared = 0.34), and Family Planning uptake (Mean Difference = 1,180; p < .001; Partial Eta Squared = 0.36). These findings indicate that PBF had a substantial positive influence on services that require active provider engagement and community mobilization. In contrast, no statistically significant difference was found between PBF and DFF in Complete Vaccination Coverage (p = .70), suggesting that both financing approaches were equally effective in supporting routine immunization services. The study concludes that PBF offers a clear advantage for demand-driven maternal and reproductive health services, whereas DFF provides comparable results for supply-driven programmes such as routine immunization. These findings highlight the importance of adopting a differentiated financing strategy within Nigeria’s primary healthcare system. Rather than relying on a single financing model, policymakers should consider a hybrid approach that combines the strengths of both PBF and DFF to maximize health outcomes while ensuring efficient use of available resources. However, as this study focused solely on service volumes, future research is recommended to evaluate the impact of these financing models on clinical quality of care, maternal mortality outcomes, and cost-effectiveness across broader geographic settings.