Recent large language models (LLMs) incorporate reasoning capabilities that allow them to perform well in predicting whether a smart contract respects a certain property, suggesting a complementary approach to traditional formal-methods-based techniques for smart contract verification. However, the application of LLMs in such context has two major issues: 1) properties expressed in natural language are intrinsically ambiguous, and 2) answers returned by LLMs have no guarantee of correctness. In this paper, we address both issues simultaneously by: 1) introducing a new formal specification language that extends Solidity with abstract types, and 2) designing a workflow that combines LLMs with type checking and concrete execution to generate and validate violation witnesses (i.e., counterexamples). The key idea is to represent a specification as a Solidity test with (existentially quantified) variables of abstract type; finding an instantiation of these variables to concrete values (of the correct type) concretizes the test into an executable counterexample (PoC) for the target property. We implemented our procedure in the tool Neuroforger, experimentally evaluating it on a smart-contract verification dataset drawn from literature, obtaining promising results that demonstrate its potential applicability in the wild.
Quantum computing poses significant challenges to traditional zero-knowledge proof schemes based on number-theoretic assumptions. As a result, code-based cryptography has attracted increasing attention for its resistance against quantum computing. In this paper, we study the Rank Syndrome Decoding problem (RSD) and investigate its ZK proof formulation within the MPC-in-the-Head framework. To prove the possession of a secret witness, we reformulate the secret witness as a mixed-field matrix multiplication preserving the rank constraint, and then obtain a representation that aligns naturally with the local-view paradigm of MPC-in-the-Head. Utilizing this value-to-calculation technique, we introduce the RSD relation into a ZKBoo-style (2, 3)-secret-sharing MPC-in-the-Head framework and obtain an RSD-based zero-knowledge proof scheme via mixed-field secret sharing. The resulting scheme reduces the proof size relative to generic formulations while preserving completeness, soundness, and zero-knowledge for the interactive protocol. The Fiat–Shamir non-interactive extension is analyzed only in the classical random oracle model; we do not claim QROM security for this variant.
У статті досліджено вплив цифрової трансформації на розвиток креативної економіки та формування нових парадигм монетизації творчих продуктів. Показано, що цифровізація суттєво змінює традиційні підходи до маркетингу, відкриваючи можливості для персоналізованої взаємодії між креатором і аудиторією завдяки використанню аналітичних інструментів, алгоритмічного таргетингу та платформ цифрової комунікації. Особлива увага приділяється ролі інфлюенсер-маркетингу, платформ прямої підтримки авторів і розвитку моделей підписки, донатів та краудфандингу як альтернативи класичній моделі продажу продукту. Обґрунтовано, що цифрові платформи сприяють демократизації доступу до ринку, зниженню бар’єрів входу та формуванню моделі «довгого хвоста», яка забезпечує стабільну монетизацію навіть нішевих продуктів. У роботі розкрито трансформацію економічних механізмів у креативних індустріях, де ключовим ресурсом стають дані про поведінку аудиторії, а не лише сам контент. Доведено, що ефективна монетизація базується на управлінні цифровими активами, зокрема аудиторією, каналами комунікації та аналітикою, що дозволяє підвищити рівень персоналізації та конверсії. Окремо висвітлено роль новітніх технологій, таких як штучний інтелект, NFT та Web3, які розширюють можливості створення, дистрибуції та капіталізації творчих продуктів, формуючи нові типи взаємодії з користувачами та моделі отримання доходу. Підкреслено зростання значення мікроспільнот, ексклюзивних форматів доступу та економіки дефіциту як інструментів підвищення цінності продукту та формування емоційної прихильності аудиторії. Також проаналізовано нерівномірність цифровізації окремих секторів креативної економіки, зокрема традиційного мистецтва, виконавських практик, музейної справи та культурної спадщини, де цифрові технології мають обмеження через специфіку споживчого досвіду. Визначено ключові бар’єри цифрової трансформації, серед яких недостатній рівень цифрової компетентності, обмежені ресурси та складність передачі емоційної та сенсорної складової творчого продукту у цифровому середовищі. Обґрунтовано необхідність формування гнучких стратегій цифрової інтеграції, що поєднують технологічні можливості з культурною специфікою галузей. У підсумку доведено, що цифрова монетизація трансформується у самостійну форму креативності, а сучасний креатор виступає не лише виробником контенту, а архітектором цифрових екосистем і емоційних економік, здатним самостійно управляти процесами створення, просування та капіталізації власного продукту
Teknologi informasi dan komunikasi kini sebagai media yang memfasilitasi berbagai kegiatan di GKPS Cililitan. Salah satunya adalah untuk proses pemilihan personel pada masa periodisasi Gereja. Pada saat ini proses pemilihan personel di GKPS Cililitan masih dilakukan secara manual, dimana masih menggunakan kertas untuk menulis nama kandidat pilihan dan papan untuk proses penghitungan yang juga secara manual di saksikan oleh seluruh anggota rapat yang hadir. Tujuan penelitian ini adalah untuk mempermudah proses periodisasi di GKPS Cililitan, baik secara proses pemilihan dan proses penghitungannya yang dapat di pantau secara realtime sehingga proses pemilihan bisa diselesaikan lebih cepat. Metode penelitian yang digunakan yaitu metode penelitian action research dengan pendekatan kualitatif serta pengembangan sistem yang digunakan yaitu metode prototyping. Dalam pembuatan sistem ini menggunakan bahasa pemrograman Typescript, Javascript, Solidity, CSS serta menggunakan framework Next.JS 14 dan Hardhat yang dikemas dalam 1 bundle development kit Scaffold.eth. Hasil penelitian ini adalah sebuah sistem pemilihan elektronik berbasis web3 dan teknologi blockchain yang diharapkan dapat membantu mengelola proses pemilihan serta penghitungan suara secara realtime di GKPS Cililitan.
University autonomy is not merely a matter of expanding institutional powers; it raises a fundamental governance question about whether the internal organizational structure is capable of exercising those powers effectively, transparently, and accountably. This article argues that organizational structure is the critical condition through which university autonomy transforms from a granted right into actual governance capacity. Drawing on cost-benefit theory, the article analyzes organizational structure in autonomous universities as a mechanism for allocating internal authority, controlling agency costs, coordination costs, compliance costs, and decision-making costs, while generating benefits in educational quality, financial transparency, and accountability. The article examines the current Vietnamese legal framework governing organizational structure in autonomous universities, evaluates the limitations of both centralized and decentralized extremes, and proposes reform directions toward a balanced model ensuring clear delegation of authority, substantive oversight, and protection of academic freedom.
S. Tamilselvi, Ravikumar R. N., Duggirala Aravind, Satheesh Kumar A. · 6 authors
The integration of Distributed Ledger Technologies (DLTs) with Digital Twin (DT) systems is transforming smart hospital infrastructures by facilitating secure, transparent, and real-time operational intelligence. As healthcare settings rely more on IoMT devices, AI analytics, and automated processes, it's crucial to ensure that the data shared between real and virtual systems is reliable and accurate. DLT provides decentralized validation, immutable storage, and automated smart-contract governance, ensuring trustworthy Digital Twin updates for predictive maintenance, patient-flow optimization, and resource management. By strengthening interoperability, enhancing cybersecurity resilience, and supporting transparent data-sharing mechanisms, DLT-enabled Digital Twins offer a robust foundation for next-generation intelligent hospital ecosystems. This chapter looks at different design models, real-life examples, performance details, and future research directions that are important for making healthcare changes that are scalable, secure, and ethical.
Yevheniia Malyshko, Pavlo IVAKHNO, Roman KOLONTAIEVSKYI
Abstract. The article examines the theoretical and methodological foundations for assessing the market value of business projects in the decentralized finance (DeFi) sector using a risk-oriented approach. The relevance of the study is driven by the rapid expansion of decentralized financial ecosystems, the increasing capitalization of blockchain-based projects, and the high volatility and uncertainty inherent in DeFi markets. Traditional valuation methods are insufficiently adapted to the specific characteristics of decentralized financial platforms, including tokenomics, smart contract architecture, liquidity instability, governance decentralization, and heightened cyber and regulatory risks. The purpose of the study is to improve methodological approaches to assessing the market value of business projects in the DeFi sector through the integration of risk-oriented analytical tools into the valuation process. The study systematizes key risk factors affecting the market value of decentralized financial projects, including technological, financial, operational, market, liquidity, and regulatory risks. Particular attention is devoted to the influence of Total Value Locked (TVL), token volatility, protocol revenue stability, governance decentralization, and smart contract security on investment attractiveness and capitalization dynamics. The methodological basis of the research includes comparative analysis, systematization, risk-oriented valuation methods, scenario analysis, and elements of financial modeling. The study proposes an integrated approach to business project valuation that combines traditional discounted cash flow methods with DeFi-specific indicators and risk coefficients. A comparative assessment of valuation models used in traditional finance and decentralized finance ecosystems is conducted. The obtained results demonstrate that the implementation of a risk-oriented approach significantly improves the accuracy and adaptability of business project valuation in decentralized financial markets. The proposed methodological framework enables a more objective assessment of project sustainability, investment attractiveness, and market capitalization under conditions of high market turbulence. The scientific novelty of the research lies in the development of a comprehensive valuation model that incorporates decentralized governance parameters, blockchain ecosystem indicators, and dynamic risk factors into the market valuation process. The practical significance of the study is associated with the possibility of applying the proposed methodological approach by investors, financial analysts, venture funds, and DeFi platform developers in the process of evaluating investment decisions and managing financial risks within decentralized digital ecosystems. Keywords: valuation, decentralized finance, market value, risk-oriented approach, smart contract risk, business projects, digital assets.
Decentralized Finance (DeFi) has introduced new security challenges due to its open, permissionless, and pseudonymous nature, which has increased the risk of fraud and phishing activities. This thesis first presents a comprehensive study of 284 DeFi platforms to examine their architectural, functional, and security-related characteristics. Building on this ecosystem-level analysis, the thesis develops a behavior-centric multiclass detection framework using Ethereum transaction data. The framework integrates legitimate, fraud, and phishing activities into a unified dataset and evaluates several traditional and deep tabular learning models, including TabNet, GANDALF, and NODE. The results show that deep tabular models outperform conventional baselines, with NODE achieving the strongest overall performance. Feature importance analysis highlights gas usage, nonce behavior, transaction frequency, and wallet activity as key indicators of malicious behavior. Overall, this thesis demonstrates that behavior-based Ethereum transaction features combined with deep tabular learning can support more effective and scalable DeFi threat detection.
Cryptocurrencies are increasingly adopted as investment assets, making their interactions with traditional financial markets central to cross-asset diversification and systemic risk. This paper studies the integration of cryptocurrencies, fiat currencies, and S&P500 equities using a balanced panel of 381 assets from October 2017 to February 2024. We combine rolling correlation networks, community structure, market-specific and system-wide Turbulence Indices, and VAR-based connectedness analysis to examine how market stress, network structure, and shock transmission vary across financial regimes. The results show that cross-asset integration is episodic. In calm periods, the three asset classes remain relatively segmented, whereas under stress, local clustering increases, modular separation weakens, and communities become more compositionally mixed across asset classes. Connectedness analysis further shows that regime shifts alter the structure of transmission rather than simply increasing spillover magnitudes. In high-turbulence states, fiat-market turbulence becomes the dominant propagation channel, while network clustering and modularity play a greater role in transmitting forecast uncertainty. These findings support the interpretation of network structure as an emergent, state-dependent transmission layer rather than a persistent exogenous driver of turbulence. The results highlight the need for regime-aware risk monitoring, since full-sample connectedness estimates can understate the cross-asset coupling that emerges precisely when diversification benefits are most fragile.
We describe a verification pipeline that takes production Rust cryptographic code and produces machine-checked correctness proofs in Lean 4. The pipeline combines three components: symbolic extraction tools (Charon and Aeneas, or Hax) that lift Rust into Lean 4; formal cryptographic specification libraries (ArkLib and CompPoly, from the Verified zkEVM project) that provide the mathematical targets; and AI provers (Aristotle from Harmonic AI and Aleph from Logical Intelligence) that close the resulting proof obligations. Every proof is checked by the Lean kernel, so AI output cannot compromise soundness. Within the scope of the Ethereum Foundation's zkEVM Verification Project, we applied the pipeline to cryptographic primitives in Plonky3 (FRI folding, Mersenne31 and KoalaBear field arithmetic, Horner polynomial evaluation) and RISC Zero (Merkle inclusion verification). In addition, Aleph authored proofs of two bounds-style theorems in Plonky3's compute_log_arity_for_round that previously stood as sorry. The paper describes the architecture, walks through a running example based on Aleph's two proofs, reports which classes of proof obligations AI closed and which required manual work, and discusses the engineering gaps we encountered: Lean 4 toolchain drift across tools and specific Aeneas/Hax extraction limits. We also document concrete missing lemmas, tactic gaps, and code-generation friction points discovered during proof development. We hope this contribution lowers the barrier to adoption of formal verification and facilitates more effective use of AI in this pipeline. The result is a working pipeline for formal verification of Rust, with kernel-checked proofs and reproducible artefacts.
Xiang Liu, Sa Song, Zhaowei Zhang, Huiying Lan · 9 authors
Consensus protocols form the backbone of distributed systems and blockchains, where implementation bugs can cause data corruption and financial losses. While LLM-based approaches show promise in code analysis, they struggle with deep protocol-level logic bugs involving complex state-dependent behaviors across multiple execution stages. We present Agora, a domain-aware multi-agent framework that integrates hypothesis-driven testing with LLM capabilities for systematic protocol verification. Agora employs specialized agents that collaboratively explore protocol state spaces, synthesize attack scenarios using domain-specific constraints, and validate findings through iterative refinement. This explicit role separation enables reasoning about global protocol invariants beyond single-function code analysis. We evaluate Agora on four consensus implementations (Raft, EPaxos, HotStuff, BullShark) using four state-of-the-art LLMs. Agora discovers 15 previously unknown protocol-level logic bugs that violate safety properties, while existing LLM-based agents fail to detect any such protocol-level logic bugs. Our results demonstrate that domain-aware multi-agent collaboration is essential for detecting deep logic bugs in complex protocols.
Ankit Kanaiyalal Prajapati, Shahzad Memon, Mohammed Mahir Rahman, Ameer Al-Nemrat
Centralised biometric identity systems expose users to single points of failure, opaque verification processes, and irreversible biometric compromise. Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) offer stronger privacy guarantees, yet their integration with biometric authentication and distributed verification remains insufficiently explored. This paper presents Ciphera, a decentralised biometric identity framework combining privacy-preserving facial recognition, multi-node verification, IPFS-based credential metadata storage, and blockchain-anchored revocation. Evaluated across functional, performance, security, and distributed consistency dimensions, Ciphera achieved an 81% functional success rate, with stable enrolment and authentication but measurable revocation propagation delays and occasional audit-log inconsistencies. Performance testing demonstrated sub-second p95 verification latency of approximately 820ms under concurrent multi-node conditions. Security analysis confirmed strong confidentiality and integrity guarantees, though incomplete liveness detection leaves susceptibility to deepfake and replay attacks. The results demonstrate the feasibility of decentralised biometric identity while identifying key engineering challenges for production-grade deployment.
Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: \textit{adversarial pattern evolution by malicious actors} and \textit{the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains}. To address these challenges, we propose a novel framework termed \textbf{TE}mporal \textbf{M}otif-aware \textbf{G}raph \textbf{T}est-\textbf{T}ime \textbf{A}daptation (\textbf{TEMG-TTA}). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed \textbf{TEMG-TTA} outperforms \textit{state-of-the-art} GAD approaches by an average of 54.88\%. A further case study on interpretable motif patterns reveals that \textbf{TEMG-TTA} explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.
The proliferation of AI-generated synthetic media poses a critical threat to the integrity of digital evidence in legal and forensic contexts. Existing deepfake detection systems typically address a single modality and provide no mechanism for tamper-proof evidence preservation. We present DeepFake Forensics AI, a unified platform that detects synthetic media across image, video, and audio modalities, identifies generative architecture fingerprints, and anchors forensic evidence immutably on the Ethereum blockchain. Our system trains four independent neural networks from scratch: an EfficientNet-B4 image detector (AUC = 0.9868), a Bidirectional LSTM video detector (AUC= 0.9628), an ECAPA-TDNN audio detector (EER = 18.63%), and a novel GAN fingerprinting module (accuracy = 99.88%) that identifies the generative architecture behind a fake image. Evidence files are hashed with SHA-256, stored on IPFS via Pinata, and registered on-chain via a Solidity smart contract with role-based access control. The platform provides a React frontend and FastAPI backend suitable for deployment in forensic and legal workflows. To our knowledge, this is the first system to unify multi-modal deepfake detection with blockchain-based chain-of custody management.
This study aims to examine the effect of Bitcoin risk perception on stock investment decisions by considering the mediating roles of financial literacy and investor trust. The rapid development of digital financial technology, particularly cryptocurrencies, has introduced new dimensions of risk that influence investor behavior across financial instruments. This research adopts a quantitative approach with an explanatory design, involving 120 respondents selected through purposive sampling. Data were collected using structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Bitcoin risk perception has a positive and significant effect on financial literacy, investor trust, and stock investment decisions. Furthermore, financial literacy and investor trust significantly influence stock investment decisions and serve as partial mediators in the relationship between Bitcoin risk perception and investment decisions. These findings suggest that higher awareness of cryptocurrency risk encourages individuals to enhance their financial understanding and develop rational trust, ultimately leading to more informed investment decisions in the stock market. This study contributes to the integration of behavioral finance and financial technology by highlighting the indirect mechanisms through which risk perception shapes investment behavior. Practically, the results emphasize the importance of financial education and transparent information in improving investor decision-making in the digital era.
Why are mathematical conjectures—the Riemann Hypothesis, the Kakeya Conjecture, P vs NP—so extraordinarily difficult to solve? For centuries, countless mathematicians have tried to dismantle them using “manual deduction”, only to hit a wall. The author argues that the root cause is: these conjectures are inherently not “manual” but “automatic”. Behind them lies the same dynamical structure—the self‑organising evolution of an information field. Traditional mathematical tools attempt to capture a dynamic, closed‑loop feedback process with static logical chains, much like trying to drive an automatic car with a manual gearbox. This paper proposes a new cross‑disciplinary framework: Information Dynamics. Its core is the generalised Ginzburg–Landau equation, whose four operations (diffusion, anti‑diffusion, nonlinear compression, logarithmic potential) form the atomic instruction set of universal self‑organisation. By faithfully embedding this equation into the category of nonlinear automatic control, we translate the three great conjectures into standard control‑theoretic properties: Riemann Hypothesis ⇔ passivity (positive realness) of a control system; Kakeya Conjecture ⇔ zero measure of the reachable set; P vs NP ⇔ polynomial stabilisability. Significance for Physical AI:This work not only provides a new language for mathematical conjectures, but also directly gives birth to a new paradigm: Physical AI. Traditional AI (including deep learning) requires massive labelled data and backpropagation—it is “manual driving”. Physical AI, in contrast, lets the information field evolve autonomously under the GL equation toward a target state, without any training—it is “autonomous driving”. Prototype experiments, such as the prime density generator, the five‑dimensional single‑point Kakeya set, and linear‑time DNA assembly, have already validated the feasibility of this paradigm. Physical AI promises to become a general problem solver, directly handling images, video, sequences, and beyond, initiating a revolution from “computation” to “generation”. Traditional algorithms adopt a search paradigm, often with exponential complexity. Physical AI provides a control paradigm: encode the problem’s state space as an initial distribution of the information field, then let the GL equation automatically evolve as a closed‑loop feedback system towards a steady state. Information Dynamics defines the physical dynamics of information — that is, how the information field itself, as a physical entity, driven by specific laws (the generalized Ginzburg–Landau equation), spontaneously evolves from disorder to order, generating complex patterns, structures, and knowledge. It answers the question: How can orderly structures and mathematical truths emerge from the quantum vacuum? This paper is not a final proof, but a research programme that can be made rigorous. All assumptions (Hilbert–Pólya conjecture, existence of a continuous limit, etc.) are explicitly stated. Code and experimental data:The numerical experiments (prime density generation, five‑dimensional Kakeya set, DNA assembly) are distributed across several GitHub repositories of the author: Riemann Hypothesis information‑dynamics proof: https://github.com/hkaiopen/Riemann-ID Kakeya set GL construction: https://github.com/hkaiopen/Kakeya-ID DNA assembly: https://github.com/hkaiopen/ComputationalBiology-ID Because the code is scattered across multiple actively developed sub‑projects, no single archive is provided on Zenodo. Please visit the links above for the latest versions.
ABSTRACT This paper examines the relationship between skewness and kurtosis in Bitcoin spot and futures markets using high‐frequency data. We document a strong convex skewness–kurtosis relationship consistent with theoretical moment restrictions. Trading activity is positively associated with realized kurtosis, particularly in futures markets, though sensitive to specification and driven by extreme‐return episodes. Allowing the relationship to evolve over time reveals substantial curvature variation, indicating state‐dependent higher‐moment dynamics. The close co‐movement of results across markets suggests patterns reflect broad market‐wide conditions. The empirical framework is reduced‐form and results should be interpreted as conditional associations rather than causal effects.
Abstract As AI agents evolve into autonomous economic actors, verifiable and legally binding identity frameworks become critical. This paper presents Ricardian-TEA , a novel architecture combining Triple-Entry Accounting (TEA), Ricardian Contracts, and Distributed Ledger Technology to assign “Legal-Technical Identities” to AI agents. We provide rigorous mathematical foundations: a Ricardian-TEA Integrity Theorem proving that constraint enforcement, non-disputability, and identity binding hold with overwhelming probability under standard cryptographic assumptions, and a Cyber-Chama Convergence Proposition characterising reputation-based trust dynamics. The framework ensures GDPR compliance via Zero-Knowledge Architecture and Crypto-Shredding. Proof-of-concept implementations on Ethereum Sepolia and Bitcoin SV testnets demonstrate chain-agnostic applicability, achieving at worst 1.4 s latency per transaction while maintaining 100% auditability of AI transactions.
This study presents a comprehensive bibliometric analysis of 1,567 scholarly publications to map the evolution of Fintech from 2005 to 2025. Leveraging the SPAR4SLR framework and advanced tools like Biblioshiny, three critical paradigm shifts are identified: the transition from foundational digital banking (2005–2014) to AI-driven financial systems (2015–2019), pandemic-accelerated digitization (2020–2022), and the current era of ethical AI and decentralized finance (2023–2025). The analysis reveals that post-2020 research predominantly focuses on technological innovations such as blockchain and algorithmic trading, while ethical implications remain underrepresented—highlighting a significant governance gap. Geographical disparities are also evident, with most publications originating from North America and Europe, despite Sub-Saharan Africa and South Asia emerging as key regions for fintech adoption. This research provides actionable policy recommendations to bridge these gaps, including a framework for AI ethics in credit scoring and strategies for fintech integration.
THE SS138 PROTOCOL: A DETERMINISTIC INGRESS ARCHITECTURE FOR DISTRIBUTED DATA DRIFT ISOLATION Abstract — This paper introduces the SS138 protocol, an invariant edge gateway architecture designed to isolate and eliminate calculation variance, packet time-series anomalies, and systemic tracking drift before data reaches downstream processing components. The architecture provides an application-layer structural filter, mapping incoming data vectors onto a fixed coordinate system bound by a multi-phase temporal macro framework. By evaluating metrics across structured phase intervals, the protocol achieves deterministic input sanitization and real-time entropy tracking with minimum algorithmic overhead, reducing the attack surface and protecting downstream distributed ledgers from processing failures or data propagation errors. Owner and Developer SquirrelSniper138 from YouTube
Zokir Mamadiyarov, R. N. Ravikumar, S. Aarthi, E. Padma · 5 authors
The integration of ESG (Environmental, Social, and Governance) principles with FinTech is reshaping modern finance through technologies like blockchain, digital wallets, robo-advisors, and DeFi. This work explores how computational tools such as machine learning, data analytics, and simulation modelling enhance ESG risk management in digital finance. It addresses cybersecurity, regulatory compliance, AI ethics, and financial resilience, linking ESG issues to practical risk control strategies. Real-time tracking of ESG indicators and transparent reporting via blockchain are emphasized. Techniques like ESG scoring models, heatmaps, and dashboards assist in monitoring sustainability and performance. It also examines emerging risks such as greenwashing, data ethics, and decentralized finance vulnerabilities. The focus is on combining innovation, ethics, and sustainability to build a robust, risk-aware financial ecosystem. This approach is essential for aligning FinTech growth with responsible, long-term ESG goals.
Risk management along with sustainable wealth generation are being revolutionized by the combination of Fintech with Environmental, Social, and Governance (ESG) standards. Fintech platforms enable financial institutions to detect and reduce ESG risks, improve transparency, and avoid greenwashing by utilizing blockchain, big-data analytics, artificial intelligence (AI), and decentralized finance (DeFi). Risk assessment techniques are improved by blockchain-enabled ethically finance protocols as well as AI-driven ESG evaluations, which support regulatory oversight and transparency. Research indicates that companies that include ESG factors into Fintech products see improved financial outcomes and attract impact-oriented funding. But there are still a lot of barriers, like disjointed legal regimes, difficulties harmonizing data, and high compliance costs.