Blockchain Papers

Yulong Wang, Yufeng Zhang, Zhuyu Zhang, Liting Gao · 5 authors

No abstract is available for this record.

Temitope Akinwunmi

Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AI’s long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AI’s potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape

Praveen Kumar Sabbineni

Federated learning enables financial institutions to collaboratively develop credit risk models while maintaining data privacy, yet existing implementations prioritize accuracy and confidentiality over transparency and regulatory compliance requirements. Current federated approaches treat explainability as a secondary concern addressed through separate post-processing workflows, creating significant gaps in auditability and stakeholder trust that limit adoption in regulated environments. This article introduces the Explainable Update Auditing framework, which embeds transparency mechanisms directly into federated training protocols through local explanation bundles and privacy-preserving audit trails. The framework generates standardized, model-agnostic explanations that characterize how institutional updates influence global model behavior without exposing proprietary data or competitive information. Cryptographic attestation mechanisms verify compliance with fairness, stability, and governance constraints throughout training processes using zero-knowledge proof systems that maintain institutional confidentiality while providing mathematical assurance of appropriate collaborative behavior. The dual-layer trust mechanism addresses distinct information needs across multiple stakeholder groups, including participating institutions, regulatory authorities, internal governance bodies, and affected borrowers. Implementation considerations reveal computational overhead challenges, privacy-utility trade-offs, and cryptographic protocol efficiency requirements that must be addressed for practical deployment. The framework transforms federated learning from an opaque collaboration protocol into a transparent, auditable ecosystem that satisfies regulatory requirements while preserving privacy guarantees essential for cross-institutional partnerships in credit risk modeling applications.

Aedah Alrehaili, Maher Wasl Alharby

No abstract is available for this record.

Aravinda S. Rao, Babu Pillai, Marimuthu Palaniswami, Vallipuram Muthukkumarasamy

Global financial integrity is fundamentally challenged by cryptocurrency mixers such as Tornado Cash, which facilitate billions in illicit fund flows. Low detection rates, reliance on labeled training data that is unavailable for novel attacks, and failure to analyze temporal coordination patterns are all impediments to the effectiveness of existing forensic tools. We introduce CONSENSUS, a self-supervised heterogeneous ensemble framework that addresses the challenge of attribution in mixed transaction streams. Our system requires no pre-existing labels, and it generates supervision signals directly from on-chain behavioral patterns. It synthesizes evidence by orchestrating nine analytical modalities—including deterministic clustering, behavioral analysis, and multiple graph neural network architectures—through a formal consensus mechanism. This multi-modal approach produces transparent, auditable risk scores from a 111-dimensional behavioral fingerprint. We validated the framework on five major decentralized finance (DeFi) exploits, including the Ronin Bridge and Poly Network hacks. Using raw transaction data, it detected all known primary attackers at 100% accuracy without training. Crucially, the framework's self-supervised components successfully identified the novel attack pattern of the Poly Network exploit, thereby demonstrating robustness to out-of-distribution threats that defeat supervised methods. By providing a transparent, zero-label solution, CONSENSUS establishes a new paradigm for flexible, effective risk profiling and forensic investigation.

S. VISHNU VARDHAN GOUD, CHANDU E., SIVAKUMAR D., MANJUNADH K. · 5 authors

The system establishes a privacy-preserving credit evaluation framework that eliminates exposure of user financial data during creditworthiness verification. Conventional credit scoring requires complete visibility into a borrower’s income records, liabilities, repayment patterns, and transaction histories, creating structural risks related to data theft, unauthorized sharing, profiling, and large-scale breaches. The proposed architecture replaces disclosure-based assessment with Non-Interactive Zero-Knowledge (NIZK) proofs. Users locally compute cryptographic attestations that assert compliance with predetermined financial thresholds—such as debt-to-income ratio, repayment consistency, or minimum balance stability—without revealing source data. Verifiers check the proof deterministically, without bidirectional communication or access to underlying financial artifacts. The model reduces attack surface, removes centralized exposure of sensitive records, and aligns credit scoring processes with modern expectations of confidentiality, verifiability, and regulatory trust. It offers an adaptable foundation for digital lending, embedded finance, decentralized platforms, and cross-institution credit portability.

Tomer Doitshman, Gilad Katz, Asaf Shabtai

Smart contracts (SCs) implemented on blockchain represent a breakthrough in decentralized applications, enabling a range of functions such as managing supply chains and handling elections. As the adoption of SCs increases, the need to detect flaws and vulnerabilities in their execution grows. To address this challenge, we present Branch Reinforcement Learning Fuzzer (BRLF), a deep reinforcement learning-based solution for the detection of vulnerabilities in SCs. The novelty of our method is threefold: first, our deep model uses text-based embeddings of conditional branches to enhance its adaptability and flexibility. Secondly, we propose a reward function that considers multiple aspects of fuzzing, such as opcode analysis and gas usage. Finally, we incorporate evolutionary algorithms into our approach, which significantly bolsters its ability to produce varied outputs. Extensive evaluation on three datasets of Ethereum-based SCs shows that BRLF outperforms state-of-the-art methods, detecting more vulnerabilities and achieving higher code coverage than existing solutions. Our code and data are available at: https://zenodo.org/records/15022152

Hengjie Song, Yangkai Wang, Han Yu, Le Ma · 5 authors

Smart contracts are crucial for managing sensitive financial transactions. However, these contracts are inherently vulnerable which can cause significant security risks. Traditional methods for detecting smart contract vulnerabilities depend on expert-defined rules, which are often complicated and limited by human experts' individual experience. In contrast, deep learning-based approaches automatically extract intricate feature representations, greatly improving detection efficiency and accuracy. Nevertheless, these approaches rely on access to large amounts of high-quality labeled data. They are less effective when facing new types of vulnerabilities where labeled data are scarce. To this end, we propose the Semi-supervised Tuning (SST) approach for smart contract vulnerability detection. It first leverages a source model trained on labeled source data to extract features for new vulnerabilities. Subsequently, it performs semi-supervised learning to explore the feature structure of unlabeled data. In particular, SST groups contract code features and constructs a shared feature queue containing labeled and unlabeled contracts to explore the complete feature structure and guide model training. Extensive experimental evaluations based on two real-world datasets containing eleven smart contract vulnerabilities demonstrate that SST is significantly more advantageous compared to eight state-of-the-art baseline methods, outperforming them by 24.12% in terms of F1 scores.

Mohan Sankaran, Nagaraju Jooluri, Srimaan Yarram, Balasundaram Subbusundaram · 5 authors

Stablecoins are becoming more common in the Fin-Tech (Financial Technology) ecosystem as they keep their value stable and combines easily with decentralized finance inherent in the financial technology ecosystem due to their price stability and simplicity of integration in decentralized finance (DeFi), cross-border payments, and automated trading systems. However, the same characteristics that propel utility transaction speed, pseudonymity, and automation through smart contracts have also made them vulnerable to financial manipulation. Tactics such as wash trading, spoofing, and pump-and-dump schemes have become more prevalent, compromising market integrity significantly. However, major technical challenge in detecting these fraudulent activities and behaviors, especially under conditions of extreme class imbalance even the legitimate transactions vastly outnumber fraudulent ones.This paper introduces SC-GAN, a conditional Generative Adversarial Network that addresses the scarcity of fraudulent samples by synthesizing realistic blockchain-based fraud instances. The model conditions on key financial and transactional features native to blockchain systems, enabling the generation of high-fidelity synthetic data. We then compare SC-GAN with traditional oversampling techniques like SMOTE and Borderline-SMOTE on a variety of supervised classification models. Our experiments on a real-world stablecoin transaction dataset show with the help of SC-GAN improves both the F1 Score and overall accuracy that is resulting in more efficient detection of rare but crucial fraudulent transactions. This approach also provides the possibility for stronger fraud prevention methods and risk management policies within FinTech platforms.

Mehdi Asgari, Seyyed Mohsen Hashemi

Fraud detection within the Ethereum network remains a major research challenge due to the strong statistical resemblance between legitimate and fraudulent transaction patterns, severe class imbalance, and the multiscale complexity of temporal-interaction dependencies. Proposing and evaluating a multi-branch attention-based system with automated architecture optimization, which can detect fraudulent Ethereum accounts with high accuracy, is the aim of this study. The experimental evaluation was performed on a dataset with 9,841 samples and 17 extracted features. The proposed system employed a hybrid multi-branch architecture combining CNN, Bi-LSTM, and LSTM with a Gated Fusion mechanism along with multiscale attention layers. The Artificial Bee Colony (ABC) algorithm was applied to automatically optimize sixteen key structural and learning parameters. The results indicate that the proposed system achieved an accuracy of 99.84 %, F1 score of 98.94 %, sensitivity of 98.76 percent, and precision of 99.12 percent. These results notably outperform eight algorithms, such as Random Forest, XGBoost, LGBM, and GADL. According to the confusion matrix analysis, there is a reduction in false negatives, confirming that the system produced only five such cases in the sample set. These findings show that the proposed system is an effective and efficient approach for detecting fraud in blockchain systems and enables deployment in exchanges, DeFi platforms, and regulatory institutions.

Allen Lin

Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.

Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He

Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.

Ting Chen, Lingfeng Bao, Ting Chen

No abstract is available for this record.

DR.N.K.SHIJIN, MR.NAZIM AHAMED.P.H, MR.LOHITH.V

The financial sector is experiencing rapid transformation due to emerging technologies. Blockchain offers a decentralized, transparent, and immutable framework for secure transactions, while Artificial Intelligence (AI) enables advanced data analytics, predictive modeling, and intelligent automation. When combined, these technologies create a powerful synergy that is reshaping finance by enhancing fraud detection, improving credit evaluation, optimizing decentralized finance (DeFi) platforms, and automating compliance processes. This paper explores the combined benefits of blockchain and AI, highlighting practical applications such as AI-enabled fraud detection within blockchain networks, adaptive smart contracts, and blockchain-secured digital identity verification. It also addresses challenges in merging these technologies, including scalability limitations, regulatory ambiguity, interoperability concerns, and ethical considerations. The study underscores the potential future of autonomous financial systems, decentralized autonomous organizations (DAOs), and AI-driven sustainable finance solutions. Ultimately, the integration of blockchain and AI is seen as a transformative force capable of significantly improving transparency, efficiency, and inclusiveness in global financial systems.

Walter Kurz, Michel Malara, Wojtek Stricker, Eva Albrecht

The objective of this study is to define a compliance-first, conceptually generalisable architecture for a multi-agent artificial intelligence platform integrated with distributed ledger technology, designed to be domain-, deployment-, and vendor-agnostic. It addresses a persistent shortcoming in current AI deployments, where compliance is often treated as a secondary concern, applied retroactively through prompt engineering rather than embedded within the foundational design. The proposed model encodes regulatory, governance, and ESG requirements into an objective-under-constraints framework, ensuring that all specialised agents operate within legally admissible and verifiably auditable parameters prior to any domain-specific implementation. A DAG-based verification layer is incorporated to enable scalable, low-latency, and cost-efficient operation while preserving evidentiary integrity. The analysis evaluates the feasibility of this conceptual model to support sustainable, rapid-deployment vertical applications without inducing vendor lock-in, preserving operational neutrality, and ensuring environmental accountability. The findings suggest that integrating compliance, ESG metrics, and agent specialisation at the architectural level provides a transferable foundation for cross-domain AI–DLT infrastructures.

Shengwei You, Aditya Joshi, Andrey Kuehlkamp, Jarek Nabrzyski

Cross-chain bridges constitute the single largest vector of systemic risk in Decentralized Finance (DeFi), accounting for over \$2.8 billion in losses since 2021. The fundamental vulnerability lies in the binary nature of existing bridge security models: a bridge is either fully operational or catastrophically compromised, with no intermediate state to contain partial failures. We present ASAS-BridgeAMM, a bridge-coupled automated market maker that introduces Contained Degradation: a formally specified operational state where the system gracefully degrades functionality in response to adversarial signals. By treating cross-chain message latency as a quantifiable execution risk, the protocol dynamically adjusts collateral haircuts, slippage bounds, and withdrawal limits. Across 18 months of historical replay on Ethereum and two auxiliary chains, ASAS-BridgeAMM reduces worst-case bridge-induced insolvency by 73% relative to baseline mint-and-burn architectures, while preserving 104.5% of transaction volume during stress periods. In rigorous adversarial simulations involving delayed finality, oracle manipulation, and liquidity griefing, the protocol maintains solvency with probability $>0.9999$ and bounds per-epoch bad debt to $<0.2%$ of total collateral. We provide a reference implementation in Solidity and formally prove safety (bounded debt), liveness (settlement completion), and manipulation resistance under a Byzantine relayer model.

Dongcheng Li, W. Eric Wong, Xiaodan Wang, Sean Pan · 5 authors

No abstract is available for this record.

Gihan M. Ali

Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R2 = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments.

Ziqian Lin, Qining Luo, Dongze Wu, Jie Shen · 7 authors

Anti-money laundering (AML) remains a critical challenge in cryptocurrency ecosystems, where blockchain’s transparency paradoxically coexists with pseudonymity. Traditional methods often fall short in modeling the temporal and structural complexity of transaction networks. This paper introduces ChronoWave-GNN, a graph neural framework designed from the theoretical perspective of time-frequency representation learning. By combining wavelet-based frequency decomposition with temporal encoding, our model captures nonstationary and multi-scale patterns inherent in illicit financial activity. This dual-domain perspective enhances the expressive capacity of graph representations without relying on modular patching. We validate our approach on the Elliptic dataset, where ChronoWave-GNN achieves a test accuracy of 0.9802 and F1-score of 0.9799, surpassing prior state-of-the-art results. These findings suggest that unifying temporal dynamics and spectral compression offers a principled and effective pathway for robust AML in decentralized financial systems.

Rino Thomas, Savitha K.K

Decentralized Finance (DeFi) has changed the financial ecosystem but is extremely vulnerable to complex schemes such as rug pulls, honeypots, and flash loan attacks, which cause massive losses for platforms and users. This study responds to the critical necessity of early and actionable warning for fraud by suggesting a real-time system predicting and justifying the risk level of freshly launched DeFi tokens prior to their engagement with the users. Drawing on a hybrid methodology, integrating smart contract code analysis, on-chain behavioral data, and social metrics, the system utilizes cutting, edge machine learning models, including Graph Neural Networks (GNNs) and ensemble methods (XGBoost, FT-Transformer), to provide sophisticated risk scoring. For user and regulatory trust assurance, explainable AI methods such as SHAP and LIME are utilized to clearly identify important risk drivers, including unsafe contract functions, wallet concentration, and liquidity lock patterns. The solution provides stage-aware and cross-chain surveillance, coupling functionality like anomaly detection, federated model training, and governance analysis. With large-scale literature synthesis and empirical validation, this framework shows that proactive, pre-transaction fraud identification and open risk valuation are possible, and it achieves a scalable defense for DeFi players and infrastructure.

Prem Anand Rathina Sabapathy

Detecting fraud in digital banking is a recognized challenge given the increasing sophistication of perpetrators as well as the limitations of traditional security models. Rules-based systems produce interpretability but cannot be adapted to emerging fraud threats. Advanced machine learning models require complex systems for training and serving, which are often not practical in lightweight and real-time environments. In this work, a Java-based Hybrid Framework for Fraud-Resilient Banking Systems is built that combines rule-based compliance, lightweight AI modeling, anomaly detection, and the application of cryptographic concept of Zero Knowledge Proof (ZKP) authentication in a decision layer. A synthetic dataset of 10,000 banking transactions representing realistic imbalances has been developed, with approximately 0.6% flagging transactions as fraudulent. The rules for interpretable transparency are applied in the event of high-value transactions or merchant transactions that are shown to be suspicious, the fraud detection is tackled through a logistic regression classifier to apply a probabilistic approach to fraud detection and z-scores have been used to identify anomalous outliers from an expected normal distribution as fraud. The framework included Schnorr’s ZKP protocol to authenticate the user without disclosing the secret credential. The consolidated scoring system incorporates the outputs of rules, AI probabilities, anomalies, and ZKP verification for sorting transactions into High, Medium, and Low risk. The experimental results on the Java implementation shows an achievable ROC AUC of 0.984. The system produces a balanced risk distribution, for 1.8% of transactions classified as high risk, 20% medium risk and 78% low risk. This research suggests that a lightweight Java-based fraud detection system can be made efficient, interpretable, and cryptographically augmented, and thus usable in practice for a banking platform where performance and security are key.

Linlin Zhang, Nianlu Liu, Ke Li, Wenbo Fang · 5 authors

No abstract is available for this record.

Siyu Jiang, Teng Ouyang, Yue Wang, Yifan Luo · 6 authors

No abstract is available for this record.

Muhammad Sannan Khaliq, Love Allen Chijioke Ahakonye, Jae‐Min Lee, Dong‐Seong Kim

No abstract is available for this record.