Introduction. In the context of global crises, military aggression, and decentralization reforms, the transformation of budgetary policy has become a key factor in ensuring the sustainable development of territorial communities in Ukraine. Particular importance is attached to enhancing the adaptability, transparency, and strategic orientation of public finance systems under security and climate challenges. The purpose of the paper is to substantiate the conceptual foundations of budgetary policy transformation, taking into account fiscal decentralization, digitalization, green economy priorities, and the need for financial resilience of territorial communities. Results. The paper systematizes theoretical approaches to understanding the essence and structure of budgetary policy based on classical and modern financial theories. A conceptual model of budget policy is developed, including key structural components: methodological basis, principles, institutional architecture, information infrastructure, and implementation tools. The authors highlight six modernization vectors: institutional-regulatory, financial, managerial, socio-economic, environmental, and security-oriented. Particular attention is paid to digital solutions, open financial data, participatory budgeting, and the integration of environmental indicators into local budget planning. Conclusions. A comprehensive transformation of budgetary policy at the local level, aligned with strategic priorities of sustainable development, will strengthen fiscal autonomy, improve financial governance, and enhance the long-term resilience of territorial communities.
Urban local bodies across India struggle to mobilize adequate revenues despite the informal sector's significant economic contribution, employing 80% of the workforce while contributing minimally to municipal coffers. This research examines how Indian cities can better harness this untapped fiscal resource through an integrated analysis of municipal finance data and urban case studies. The investigation reveals systemic obstacles including outdated property tax systems, complex licensing procedures, excessive transfer dependency, and political interference that collectively constrain revenue potential. Findings demonstrate that strategic interventions - particularly digital governance tools, streamlined regulations, and incentive-based fiscal policies - can dramatically improve collection efficiency while safeguarding vulnerable informal workers. Evidence from pioneering cities highlights successful approaches such as GIS-based property mapping, single-window licensing systems, and performance-linked transfers that have boosted revenues by 20-25%. The study develops a comprehensive policy framework that balances revenue generation with inclusive development, offering scalable solutions for municipal finance reform. These insights provide valuable guidance for urban governance in developing economies facing similar challenges of informality and fiscal decentralization.
With the rapid growth of international trade and cross border capital flows, traditional payment systems such as SWIFT are increasingly challenged by inefficiencies, high fees, and a lack of transparency. As a response, blockchain technology, characterized by decentralization, immutability, and traceability, has emerged as a potential solution for modernizing cross border payments. This paper offers a comprehensive analysis of the current state of blockchain technology in this domain, reviewing its applications, key advantages, and limitations. By examining how distributed ledgers and smart contracts can streamline payment processes, reduce intermediary costs, and enhance transparency and compliance, the study highlights blockchain’s transformative potential. However, challenges such as regulatory fragmentation, limited technological maturity, and accessibility barriers remain significant obstacles to large scale adoption. Relying on secondary data and existing literature, this study also emphasizes the need for future empirical research and case based analysis to evaluate performance, security, and scalability of blockchain based systems in real world cross border payment scenarios.
Irum Matloob, Shoab Ahmed Khan, Bushra Bashir, Rukaiya Rukaiya · 6 authors
Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors' and hospitals' behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide.
The results of this study highlight the effectiveness of the proposed semantic security detection framework, SSB, in identifying a wide range of vulnerabilities in smart contracts tailored for industrial control scenarios. Compared to existing tools like ZEUS, Securify, and VULTRON, SSB demonstrates superior logical coverage across various vulnerability types, as evidenced by its performance on smart contract samples. This suggests that semantic-based approaches, which integrate domain-specific invariants and runtime monitoring, can address the unique challenges of ICS, such as real-time constraints and semantic consistency between code and physical control logic. The framework's ability to model industrial invariants-covering security, functionality, consistency, time-related, and resource consumption aspects-provides a robust mechanism to prevent critical errors like unauthorized access or premature equipment operation. However, the lack of real-world ICS validation due to confidentiality constraints limits the generalizability of these findings. Future research should focus on adapting SSB for real industrial deployments, exploring scalability across diverse ICS architectures, and integrating advanced AI techniques for dynamic invariant adjustment. Additionally, addressing cross-chain interoperability and privacy concerns could further enhance the framework's applicability in complex industrial ecosystems.
Purpose. The purpose of this article is to conduct a philosophical-legal analysis of the determinism of smart contract execution and its impact on fundamental legal categories through the lens of practical philosophy. This analysis aims to elucidate the transformation of the nature of legal certainty in the context of algorithmic automation.Design / Method / Approach. The study employs an interdisciplinary approach, integrating analytical philosophy of law with elements of phenomenological analysis of temporality and critical examination of formal systems. The methodological foundation consists of theories of legal certainty, concepts of practical reason, and approaches from the philosophy of technology to the analysis of algorithmic regulation.Findings. The study identifies a contradiction between the algorithmic logic of procedural justice and human needs for substantive justice. It is established that execution determinism ensures formal predictability at the expense of contextual sensitivity and adaptability. A distinction is substantiated between the certainty of code and legal certainty as qualitatively distinct phenomena. It is demonstrated that the inherent incompleteness of formal systems precludes the complete algorithmic formalization of legal relations.Theoretical implications. The research results advance philosophical-legal theory by analyzing the limits of legal formalization and conceptualizing the temporal transformation of legal processes, thereby enriching the understanding of the relationship between determinism and justice in the digital era.Practical implications. The findings provide a theoretical foundation for developing hybrid legal systems that combine algorithmic efficiency with the preservation of room for human judgment, as well as for formulating principles for the responsible integration of deterministic systems into legal practice.Originality / Value. The article proposes a philosophical-legal analysis of the determinism of smart contract execution through the lens of practical philosophy. It substantiates the transformation of the nature of legal certainty in the context of algorithmic automation as a qualitatively new phenomenon, necessitating a rethinking of traditional legal categories.Research limitations / Future research. Further research is needed on the transformation of legal subjectivity in the context of shifting autonomy from the level of interpretation to the level of designing legal systems, as well as on the analysis of new forms of legal agency in hybrid human-machine systems.Paper type. Theoretical.
Digital banking fraud has grown into a multifaceted threat requiring both strong cryptographic protections and advanced machine learning defenses. This paper proposes a unified framework integrating lattice-based cryptography (for post-quantum resilience and privacy) with federated graph neural networks (for collaborative fraud detection) to address the gap in current financial security architectures. We simulate real-world fraud scenarios –including synthetic identity schemes and transaction laundering – using a mix of publicly reported incidents (e.g., the 2023 Log4Shell exploitation, 2022 SolarWinds-style supply chain compromise, and the Mirai botnet’s IoT spread) as motivating cases. Our methodology leverages threshold homomorphic encryption for privacy-preserving analytics, along with adversarial training and diffusion purification to harden models against poisoning and evasion attacks. Experiments using a mixed dataset of EU banking transactions and simulated breach logs show that our approach achieves over 99% accuracy in detecting novel fraud patterns, while reducing false positives by 35% compared to conventional classifiers. We validate these improvements with statistical significance (p<0.001) and illustrate them via ROC curves and network topology maps. Key findings include the identification of specific trade-offs between cryptographic overhead and detection latency, and the observation that cross-institutional intelligence sharing (using zero-knowledge proofs) can halve the response time to coordinated attacks. These results suggest that combining cryptography with machine learning can close existing vulnerabilities in digital banking and guide future work in robust, privacy-aware fraud prevention.
This review article examines the state of blockchain-enabled identity management in Internet of Things (IoT) networks, focusing on decentralized and secure mechanisms for device identification, authentication, and access control. Traditional centralized identity systems face limitations such as single points of failure, scalability bottlenecks, and vulnerability to breaches. We systematically survey recent literature on blockchain-based frameworks applied to IoT, categorizing approaches by blockchain platform, identity credential models, consensus mechanisms, and smart contract implementations. The analysis highlights key performance metrics such as system latency, throughput, resource overhead, and energy consumption, and compares existing prototypes deployed across diverse IoT scenarios. We assess the security and privacy implications, including resistance to spoofing, Sybil attacks, unauthorized access, data tampering, and insider threats. Additionally, the review identifies open research challenges such as managing identity lifecycle in constrained devices, achieving interoperability across heterogeneous networks, balancing decentralization with scalability, and integrating with emerging technologies like edge computing and zero-knowledge proofs. Finally, we offer recommendations for future research directions and practical deployment strategies to advance blockchain-based identity solutions in IoT ecosystems. Our comprehensive synthesis aims to guide researchers and practitioners in developing robust, scalable, and trustworthy identity frameworks using blockchain for the evolving IoT landscape.
Santa Lusianna Sitorus, Ratna Tri Hari Safariningsih, Aldo Hermaya Aditiya Nur Karsa, Maulana Arif Komara · 5 authors
The digital fashion industry is undergoing a major transformation with the integration of technologies such as blockchain and smart contracts, which offer solutions to key challenges such as product authenticity, intellectual property protection, and supply chain efficiency. This research focuses on the application of blockchain technology and smart contracts in digital fashion entrepreneurship, with the aim of increasing transaction efficiency, product security, and business sustainability. Method used in this study is a quantitative approach with a structural model based on Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the influence between smart contract adoption, digital transaction efficiency, product security, and fashionpreneurship sustainability. Data were collected from digital fashion actors involved in the blockchain-based ecosystem. Research result shows that the adoption of smart contracts has a positive effect on transaction efficiency and product security. In addition, blockchain has been shown to increase supply chain transparency that supports the sustainability of digital fashion businesses. Although this technology faces technical challenges such as high transaction speed and costs, solutions such as layer-2 technology can improve blockchain performance. The conclusion blockchain and smart contract technology can be an effective solution in increasing efficiency, security, and sustainability in the digital fashion industry. Although there are technical challenges that need to be overcome, the application of this technology has great potential to create a more environmentally friendly and transparent digital fashion ecosystem.
Background: The Coronavirus Disease of 2019 (COVID-19) resulted in a global shift in gambling and trading behaviors. At present, a gap exists in understanding the relationship between excessive trading behavior and problem gambling, especially during the COVID-19 period. This narrative review analyzed (1) the changes in trading and gambling activity during the COVID-19 pandemic, (2) whether the pattern of trading activity resembles problem gambling, and (3) whether excessive trading and problem gambling share similar consequences. Methods: We searched databases such as Medline, PsychINFO, Scopus, and Google Scholar using relevant keywords, and included 60 reports for narrative synthesis. Results: During the COVID-19 pandemic, there were major changes to trading behavior, possibly due to market sentiments and psychology, personal financial needs, social media influence, and the behavior of other investors. The progression of the pandemic led to an increase in brokerage account openings and an increase in trading activities among existing investors, likely due to the development of digital trading platforms that enhanced accessibility for technology-adept investors. There was also a shift from gambling at physical destinations to online gambling, with an increase in frequency and spending among individuals who continued gambling. Feelings of boredom, stress, and the need for relaxation may motivate people to engage in gambling. Conclusion: Individuals who engaged in excessive trading and problem gambling shared similar traits and may thus face similar psychiatric consequences. The findings indicate that we can apply the diagnostic criteria for pathological gambling and gambling disorders to excessive trading, given that many of these individuals meet the criteria for an addictive disorder.
From oral agreement to paper to digital format, the contract, which encapsulates and defines the relationships between those who compose it, has undergone multiple revolutions. These days, it's the smart contracts that are interfering between technology and law. A new generation of contracts known as intelligent contracts (smart contracts) has emerged as a result of the remarkable advancements in artificial intelligence coupled with the development of blockchain technology and crypto money. Furthermore, this intelligent contractual model is no longer limited to the creation of traditional agreements alone. Because it can be created and executed automatically, often without external intervention, it tends to expand to other types of legal acts and a wide range of electronic transactions.
Introduction This article presents a method for short-term cryptocurrency price forecasting utilizing news headlines. Methods The study analyzes the impact of news on asset prices within one hour of publication, employing machine learning-based classification with BERT and GPT models, as well as GloVe vector representations. Results The proposed cascade classifier model enhances prediction accuracy by initially assessing the strength of a news item and subsequently forecasting the direction of price movement. Experimental results demonstrate the effectiveness of the developed classification model. Discussion The model achieves an accuracy of 79% in predicting price movements, confirming the potential of leveraging news headlines to improve short-term forecasts in cryptocurrency markets.
Javier David Benítez Fuentes, Rodrigo Lastra del Prado, Miguel Borregon-Rivilla, Alicia de Luna Aguilar · 15 authors
Despite significant advances in oncology, cancer care globally continues to face critical challenges, including stark disparities in access, insufficient preventive focus, fragmented primary health care (PHC) integration, unsustainable financing models, workforce shortages, and inadequate community involvement. This paper revisits the Alma Ata Declaration's principles-health equity, universal access, preventive care, and community participation-as a conceptual framework to address these persistent issues in cancer care. We highlight opportunities to strategically integrate oncology services within strengthened PHC systems, balancing centralized specialist resources with decentralized community-based care. Evidence from diverse settings illustrates how reinforcing PHC infrastructures enhances preventive measures, early detection, and survivorship care, thus mitigating geographic and socioeconomic disparities. Sustainable financing mechanisms and targeted workforce strategies, including task-shifting and multidisciplinary training, are proposed as essential components. Effective community engagement models demonstrate improved care relevance, acceptance, and outcomes. Additionally, we emphasize the critical role of health policy alignment with universal health coverage objectives, robust pharmacoeconomic evaluations, and evidence-based national cancer control plans. Integrating Alma Ata's principles into contemporary oncology provides a viable, scalable model to advance equitable, accessible, and sustainable cancer care globally, laying the theoretical groundwork for future research initiatives and informed policy development.
Open access
Economic and Financial Impacts of Cancer
Health Systems, Economic Evaluations, Quality of Life
Zulfikar Gutama, Suhita Whini Setyahuni, Maria Safitri, Diana Puspitasari
Introduction: This study examines the roles of gold and Bitcoin as safe-haven assets amid global financial uncertainties from 2014 to 2025. While gold is traditionally viewed as a stable asset during market turmoil, Bitcoin's highly volatile and speculative nature challenges its role as a safe-haven. The purpose is to compare the stability and effectiveness of these assets in preserving investment value during crises.Methods: This quantitative research uses 136 monthly return data points of gold and Bitcoin. Analytical methods include independent sample t-tests for comparing average returns and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) models to assess volatility and dynamic correlations.Results: Findings reveal gold as a more stable and reliable safe-haven with consistently lower volatility and steadier returns. Bitcoin shows significantly higher volatility and fluctuating dynamic correlations with gold, indicating its speculative behavior. The t-test confirms a significant difference in average returns, while no strong causal relationship exists between the two assets. These results suggest gold is preferable for conservative investors, whereas Bitcoin serves better as a high-risk diversification instrument. Keywords: Bitcoin, Gold, DCC-GARCH, Safe Haven Asset, Investment Asset
Adnan Sagheer, Ali Raza, Muhammad Rizwan Rashid Rana, Faiza Kiran
The highly stochastic, nonlinear, and volatile nature of Bitcoin prices poses significant challenges for accurate forecasting using traditional statistical models. To address this, we propose a hybrid deep learning architecture that combines the strengths of Convolutional Neural Networks (CNNs) for spatial feature extraction with Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), for capturing long-term temporal dependencies. This integrated framework effectively models both spatial and temporal patterns from historical Bitcoin price data. The model was trained and evaluated using real-world Bitcoin datasets.Experimental results demonstrate that the proposed CNN+LSTM model outperforms traditional machine learning and standalone deep learning approaches. Specifically, it achieves a Root Mean Square Error (RMSE) of 245.76, a Mean Absolute Error (MAE) of 11.45, a Mean Absolute Percentage Error (MAPE) of 15.68%, an R² score of 0.92, and a Mean Bias Error (MBE) of 6.94. These results highlight the effectiveness and reliability of the proposed hybrid model in enhancing the accuracy and stability of financial time series forecasting, providing valuable insights for traders, investors, and financial analysts.
This paper introduces “SmartBLock”, a novel protocol that integrates smart lock management with the Bitcoin blockchain. By employing blockchain technology, the SmartBLock protocol eliminates the need for centralized databases (reducing the risk of data breaches) and ensures accountability for all access events. Authentication is accomplished through Bitcoin's cryptographic signature scheme. Additionally, SmartBLock is an open and manufacturer-agnostic protocol, relying on the open and permissionless Bitcoin blockchain rather than proprietary tools or protocols. Given the limited computational capabilities of Internet of Things (IoT) devices, achieving this integration presents a significant challenge. This paper provides a comprehensive review of the state of the art in smart lock protocols, details the design of the SmartBLock protocol, presents a proof-of-concept prototype to validate its feasibility, and offers a meticulous analysis of its security, privacy, and traceability features. • Literature review on blockchain-integrated smart locks. • Propose a smart lock protocol on Bitcoin for immutable and transparent management. • Provide a demonstration that the protocol is secure, private, and transparent. • Build a PoC for the proposal on an IoT device and gather evidence of its feasibility.
Nur Nisa Humairah Rosdi, Amysha Qistina Amerolazuam, Nur Zafirah Adira Ahmadzamani, Ahmad Anwar Zainuddin
This concise review paper discusses the application of smart contracts to increase blockchain interoperability. The emergence of blockchain has opened many opportunities to explore the advantage of modern technology. Blockchain networks operate as isolated ecosystems, hindering the seamless transfer of assets and data across different platforms. This ecosystem leads to the inability to interact or communicate within the blockchain. Smart contracts present a promising solution for facilitating interoperability between blockchains. In this paper, the potential of smart contracts as bridge technologies between blockchains is explored. The design and implementation of smart contracts to enable secure, trust less communication and asset transfer between disparate blockchain networks are analysed. Several academic papers were reviewed to understand the existing research and development efforts towards smart contract-based interoperability solutions. The ongoing discourse on blockchain interoperability is contributed to by highlighting the potential of smart contracts as bridge technologies, identifying key challenges and research gaps in this domain, and providing insights for further development of secure and efficient cross-chain communication protocols.
Smart contracts and cryptocurrency wallets are foundational components of decentralized applications (dApps) on blockchain platforms such as Ethereum. While these technologies enable secure, transparent, and automated transactions, their integration also introduces complex security challenges. This study presents a security-oriented analysis of smart contract and wallet integration, focusing on BlockScribe—a decentralized Ethereum-based application for digital record certification. We systematically identify and categorize security risks arising from the interaction between wallet interfaces and smart contract logic. In particular, we analyze how user authorization flows, transaction design, and contract modularity affect the security posture of the entire dApp. To support our findings, we conduct an empirical evaluation using static analysis tools and formal verification methods, examining both contract-level vulnerabilities and integration-level flaws. Our results highlight several overlooked attack surfaces in wallet–contract communication patterns, including reentrancy amplification, permission mismanagement, and transaction ordering issues. We further discuss implications for secure dApp development and propose mitigation strategies that improve the robustness of wallet–contract ecosystems. This case study contributes to a deeper understanding of integration-layer vulnerabilities in blockchain-based systems and offers practical guidance for developers and auditors aiming to strengthen smart contract security.
Blockchain-based cryptocurrencies have generated substantially divergent views, opinions, and interpretations about their fundamental nature, sense, and purpose compared to other IT. This study examines what these varying interpretations are and how they influence business decision-makers’ perceptions and acceptance of Bitcoin, the most prominent cryptocurrency, specifically. Drawing on Bijker’s socio-constructivist concepts of “frames”, and using a case study of Bitcoin acceptance, we make two key contributions. First, we identify seven distinct frames used in the cryptocurrency context: currency, asset, novelty, ideological, socio-cultural, business, and IT tool frames of reference. We theorise how these frames emerge from prior frames, drawing on different reference points, foreground or background facets of Bitcoin, and hence lead to constructions of “Bitcoin-as-X” and acceptance or rejection in both conceptual and practical terms. Second, the paper provides an exemplar of how any digital phenomena with ambivalent or conflicting assessments can be studied in general. We conclude by discussing implications for theory and practice.
This study introduces a framework that integrates blockchain, decentralized identity, and zero-knowledge proofs to enhance the trustworthiness and confidentiality of disaster information sharing. A sustainable model is proposed for real-world applications, supported by a prototype developed on the Decentralized Solutions for Humanity (DS4H) blockchain research network.
Data marketplace are rapidly gaining traction as critical components of the modern data economy. However, traditional centralized marketplaces suffer from inherent challenges such as data leakage, lack of user control and single points of failure. To address these limitations, we propose Versatile Peer Network (VePran) – a decentralized data market place built on the Web3 suite of technologies. VePraN is designed to be modular, scalable and aligned with open standards, ensuring broad interoperability and future extensibility. Leveraging the InterPlanetary File System for persistent storage and blockchain for identity and ownership management, the platform offers a robust infrastructure that enhances data security and provenance. Unlike existing buyer centric solutions, VePraN adopts a seller oriented approach, empowering data owners with greater autonomy, fair exchange and control over their data assets. In addition to enabling secure data exchange, the platform facilitates the trading of machine learning models, expanding its utility in AI driven ecosystems. Verification mechanisms such as Merkle roots and Non- Fungible Tokens are employed to ensure data integrity and authenticity. This paper presents the architecture and implementation of VePraN as a foundational step toward a more equitable and resilient data exchange system.
Emanuela Podda, Pol Hölzmer, Alexandre Amard, Johannes Sedlmeir · 5 authors
Zero-knowledge proofs allow the implementation of the data minimisation principle imposed by the GDPR in digital identity wallets and the related personal data transactions, therefore representing a reasonable option to be enforced by lawmakers.
Hugo Massaroli, Leonardo Iara, Emmanuel Iarussi, Viviana Siless
Large language models (LLMs) are increasingly deployed in realworld applications, yet concerns about their fairness persist especially in highstakes domains like criminal justice, education, healthcare, and finance. This paper introduces transparent evaluation protocol for benchmarking the fairness of opensource LLMs using smart contracts on the Internet Computer Protocol (ICP) blockchain (Foundation, 2023). Our method ensures verifiable, immutable, and reproducible evaluations by executing onchain HTTP requests to hosted Hugging Face endpoints and storing datasets, prompts, and metrics directly onchain. We benchmark the Llama, DeepSeek, and Mistral models on the PISA dataset for academic performance prediction (OECD, 2018), a dataset suitable for fairness evaluation using statistical parity and equal opportunity metrics (Hardt et al., 2016). We also evaluate structured Context Association Metrics derived from the StereoSet dataset (Nadeem et al., 2020) to measure social bias in contextual associations. We further extend our analysis with a multilingual evaluation across English, Spanish, and Portuguese using the Kaleidoscope benchmark (Salazar et al., 2025), revealing cross-linguistic disparities. All code and results are open source, enabling community audits and longitudinal fairness tracking across model versions.
Maria Alejandra Ramirez, Rosemarie Nagel, David Wolpert, Jürgen Jost
Commitments play a crucial role in game theory, shaping strategic interactions by either altering a player's own payoffs or influencing the incentives of others through outcome-contingent payments. While most research has focused on using commitments to achieve efficient equilibria, their potential applications beyond this goal remain largely unexplored. In this study, we introduce a non-coercive extortion mechanism that leverages commitments to outcome-contingent payments, demonstrating how a player or external agent can extract profit by offering rewards rather than threatening punishment. At the core of the mechanism is the introduction of sequentiality into a simultaneous-move game, fundamentally reshaping the strategic interaction. We derive the conditions under which extortion is successful, identify the class of games susceptible to this scheme, and determine both the maximum extractable profit and the minimum required payment. To illustrate the extortion mechanism, we apply it to 2x2 games, highlighting how even simple strategic settings can be vulnerable to this form of manipulation. Our results reveal strategic vulnerabilities in competitive settings, with significant implications for economic markets, diplomatic relations, and multi-agent systems operating in blockchain environments. This work broadens our understanding of commitments in game theory and raises critical questions about how to safeguard strategic interactions from exploitation through non-coercive extortion.