Blockchain Papers

Diego Vallarino

No abstract is available for this record.

Dan Lin, Ziye Zheng, Jiajing Wu, Jingjing Yang · 8 authors

Cross-chain technology enables seamless asset transfer and message-passing within decentralized finance (DeFi) ecosystems, facilitating multi-chain coexistence in the current blockchain environment. However, this development also raises security concerns, as malicious actors exploit cross-chain asset flows to conceal the provenance and destination of assets, thereby facilitating illegal activities such as money laundering. Consequently, the need for cross-chain transaction traceability has become increasingly urgent. Prior research on transaction traceability has predominantly focused on single-chain and centralized finance (CeFi) cross-chain scenarios, overlooking DeFispecific considerations. This paper proposes ABCTRACER, an automated, bi-directional cross-chain transaction tracing tool, specifically designed for DeFi ecosystems. By harnessing transaction event log mining and named entity recognition techniques, ABCTRACER automatically extracts explicit cross-chain cues. These cues are then combined with information retrieval techniques to encode implicit cues. ABCTRACER facilitates the autonomous learning of latent associated information and achieves bidirectional, generalized cross-chain transaction tracing. Our experiments on 12 mainstream cross-chain bridges demonstrate that ABCTRACER attains 91.75% bi-directional traceability (F1 metrics) with self-adaptive capability. Furthermore, we apply ABCTRACER to real-world cross-chain attack transactions and money laundering traceability, thereby bolstering the traceability and blockchain ecological security of DeFi bridging applications.

Papa Rao Maddala

By combining blockchain technology, machine learning, and artificial intelligence (AI), the banking sector has witnessed a revolution in credit risk reduction in recent years. With an emphasis on predictive analytics and decentralized frameworks, this paper explores the real-world applications of these technologies in the discovery, evaluation, and management of credit risk. The study demonstrates how machine learning models, blockchain's transparent and unchangeable ledger systems, and AI-powered algorithms have greatly increased the precision and effectiveness of credit risk assessments through thorough literature analysis and case studies. The report also examines how financial institutions implement these technologies to improve operational risk management, lower fraud, and create more accurate credit scoring systems. Notwithstanding their promise, there are still significant obstacles to overcome, including data privacy, regulatory compliance, and implementation costs. In order to effectively utilize the advantages of AI, blockchain, and machine learning in reducing credit risk, the article ends with ideas for overcoming these obstacles. Keywords: Artificial Intelligence; Blockchain; Machine Learning; Credit Risk Mitigation; Predictive Analytics; Financial Technology; Credit Scoring; Risk Management; Decentralized Finance; Operational Risk

Anthony Chidi Nzomiwu, Franca Okoye, Michael Ndubisi Nwobodo

No abstract is available for this record.

Rafique Adi Putra Mohd Nasir

No abstract is available for this record.

Ahammed Haris Yoosuf, Sukrith Lal P S, K M Sheena

Decentralized Finance (DeFi) lending protocols currently rely on fixed collateralization ratios, leading to inefficiencies such as over-collateralization, frequent liquidations, and suboptimal capital utilization. This paper proposes a novel framework integrating machine learning (ML) with DeFi lending protocols to dynamically adjust collateral requirements in realtime based on borrower behavior, market volatility, and on-chain data. By analyzing historical loan performance, social sentiment, and macroeconomic indicators, the ML model optimizes collateral ratios to minimize liquidations while maintaining protocol security. We simulate the model using data from major DeFi platforms (e.g., Aave, Compound) and demonstrate a 30-50

Muhammad Shoaib Farooq, Shahzada Fahad Munir, Muhammad Faraz Manzoor, Momina Shaheen

The increasing complexity of fraudulent activities requires advanced fraud detection systems, as existing solutions lack effectiveness due to two challenges. First, privacy concerns prevent financial institutions from sharing sensitive transaction data. Second, data imbalance causes biased models, as fraudulent transactions represent a small fraction of total transactions, leading to poor fraud detection performance. To address these challenges, we propose an AI‐driven adaptive federated learning (AFL) framework for credit card fraud detection (CCFD). AFL enables decentralized learning, allowing financial institutions to train a global fraud detection model collaboratively without sharing raw transaction data. The model aggregation is performance‐adaptive, weighting client contributions based on detection accuracy to ensure a robust global model. To overcome data imbalance, we introduce a multistep data balancing framework integrating Tomek links for undersampling, borderline‐SMOTE for oversampling, and cognitive sample pruning to remove misleading samples. To evaluate the robustness and generalizability of the proposed framework, we conducted experiments on both the widely used 2013 Kaggle dataset and the Sparkov simulated dataset (2019‐2020). The Sparkov dataset, which contains interpretable demographic and merchant‐level features, allowed us to test the model’s adaptability to diverse data sources. The results demonstrate that the proposed AFL framework, combined with advanced data balancing, significantly outperforms traditional models, achieving 99% accuracy, 99.5% precision, 99.4% recall, and 99% F1‐score on the Kaggle dataset, and 97.4% accuracy, 99.5% precision, 97.5% recall, and 98.4% F1‐score on the Sparkov dataset. This research highlights AI’s transformative role in finance, particularly in enhancing fraud detection systems with improved accuracy, robustness, security, and scalability.

Sandeep Singh, Ashish Nag, Pooja Kaushik, Utkarsha Tomar

No abstract is available for this record.

Trong Hoan Dao, Tuan-Dat Trinh, Viet-Bang Pham

No abstract is available for this record.

Naresh Kumar Satish, Mathieu Mercadier, Cristina Hava Muntean, Anderson Augusto Simiscuka

The cryptocurrency market is widely regarded as one of the most volatile financial markets due to inconsistencies in its pricing factors. Despite this volatility, it continues to attract a large population of investors, many of whom incur significant losses. To address this challenge and support risk assessment for investors, users, and other stakeholders, this paper focuses on forecasting Ethereum prices by analyzing social media sentiment. The study gathers data from sources such as global news headlines and Reddit discussion forums, enhancing it with hybrid sentiment features derived from the VADER, BERT and TextBlob models. These sentiment insights are then correlated with Ethereums financial parameters to establish meaningful relationships within the data, which are used to train machine learning models. The study evaluates the predictive performance of Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Among these, Extreme Gradient Boosting demonstrated superior performance, effectively capturing complex relationships within the data and achieving an R-squared value of 0.982115. To further enhance the studys risk assessment capabilities, the concept of Explainable Artificial Intelligence (XAI) is employed to improve transparency and accountability in the model outcomes. Specifically, Shapley Additive Explanations (SHAP) are used to interpret the feature interactions within the Extreme Gradient Boosting model, thereby increasing its reliability and providing deeper insights into its decision-making process.

Nathan Burton

No abstract is available for this record.

Xinyue Huang, Chen Zhao, Xiang Li, Chengwei Feng · 5 authors

Illicit transaction detection on blockchain networks presents a critical challenge due to the pseudonymous, decentralized, and high-volume nature of decentralized finance (DeFi) ecosystems. Traditional machine learning models struggle to effectively capture the temporal dynamics and irregular patterns of illicit behavior, while graph-based methods often incur high computational costs and rely on static relational structures. In this paper, we propose a novel dual-attention framework—GAM-CoT Transformer—for robust transaction-level anomaly detection. The proposed model integrates two key components: a Global Attention Module (GAM) that adaptively reweights feature channels and temporal steps to emphasize salient patterns, and a Contextual Transformer (CoT) block that efficiently models short-range dependencies using grouped convolutions instead of full self-attention. This design enables the model to simultaneously achieve computational efficiency, temporal expressiveness, and improved detection sensitivity. We evaluate our approach on a real-world blockchain transaction dataset and demonstrate its superiority over conventional classifiers including Random Forest, XGBoost, and LSTM-based models. The GAM-CoT Transformer achieves higher recall and F1 scores, particularly for the minority illicit class, while maintaining fast convergence and deployment scalability. Our method offers a practical and effective solution for enhancing the security of blockchain systems through intelligent transaction behavior modeling.

Ra-Yeon Choi, Yeji Song, Minsoo Jang, Tae-Kyung Kim · 6 authors

Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and integrity. However, their immutability after deployment makes programming errors particularly critical, as su... | Find, read and cite all the research you need on Tech Science Press

Vineeth Kumar Reddy Chandravathi

As decentralized finance (DeFi) expands, Ethereum’s role as the backbone for digital asset exchange, smart contracts, and financial protocols has grown—but so has its exposure to fraud. Phishing, money laundering, and malicious contracts exploit its openness. Existing ML and deep learning models often lack the balance between speed, accuracy, and explainability needed for real-time blockchain analysis. High-performing models like transformers are accurate but too resource-heavy and opaque. This study leverages TabNet—a sparse-attention deep learning model optimized for tabular Ethereum transaction data. It dynamically selects relevant features during training, enhancing both interpretability and efficiency. With an accuracy of 0.86, precision of 0.80, and F1-score of 0.79, TabNet outperforms traditional models in fraud detection while remaining lightweight and transparent. Its feature-level insights make it ideal for environments where trust, latency, and transparency are crucial. The results position TabNet as a scalable, practical alternative for fraud detection in blockchain ecosystems.

Akaash Vishal Hazarika, Mahak Shah, Swapnil Patil, Pradyumna Shukla

Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniques namely in memory caching, distributed caching, and proxy caching and their functions in enhancing performance in decentralized settings. Through literature review we examine the utilization of AI techniques for alleviating risks related to market volatility, liquidity challenges, operational failures, regulatory compliance, and security threats. This comparison research evaluates various case studies from prominent DeFi technologies, emphasizing critical performance metrics like latency reduction, load balancing, and system resilience. Additionally, we examine the problems and trade offs associated with these technologies, emphasizing their effects on consistency, scalability, and fault tolerance. By meticulously analyzing real world applications, specifically centering on the Aave platform as our principal case study, we illustrate how the purposeful amalgamation of AI with contemporary caching methodologies has revolutionized risk management in distributed arbitrage systems.

Damanpreet Kaur, A. P. Siva Kumar, Prabhdeep Singh

No abstract is available for this record.

Saeid Ataei, Saeid Ataei, Shervan Ataei, Shervan Ataei · 5 authors

This study presents a comprehensive systematic review of Artificial Intelligence (AI) applications in DecentralizedFinance (DeFi), emphasizing AI’s pivotal role in mitigating the vulnerabilities and operational complexities inherentin permissionless financial systems. By systematically analyzing 39 peer-reviewed studies from major scholarlydatabases, the review identifies five dominant application domains: fraud detection, smart contract security, marketprediction, credit risk assessment, and decentralized governance. It examines the diverse range of AI methodsspanning machine learning, deep learning, graph neural networks, and reinforcement learning—and evaluates theircomparative performance and limitations. The findings reveal that AI not only enhances DeFi’s transparency, trust,and efficiency but also underpins emerging capabilities such as autonomous governance and adaptive marketmechanisms. Persistent challenges including data scarcity, cross-chain generalization, interpretability, andscalability—underscore the need for robust, explainable, and ethical AI solutions. The review concludes that AIconstitutes a foundational enabler for secure, transparent, and resilient decentralized financial ecosystems, andoutlines critical future research directions for integrating trustworthy intelligence into the evolving DeFi landscape. (PDF) A Systematic Review on the Application of Artificial Intelligence in Decentralized Finance. Available from: https://www.researchgate.net/publication/397514996_A_Systematic_Review_on_the_Application_of_Artificial_Intelligence_in_Decentralized_Finance [accessed Nov 11 2025].

Jia Wen, Yue Yu, Bo Meng, Dejun Wang

The development of blockchain technology has promoted the growth of cryptocurrencies, but it has also provided criminals with opportunities to engage in fraudulent activities. Currently, Ethereum fraud detection models based on machine learning face challenges such as feature redundancy and class imbalance, which lead to a high false negative rate. To address these issues, this paper proposes an improved CT-ARF detector to enhance the detection of fraudulent activities on Ethereum. This approach combines the ARFBoost feature enhancement mechanism and the CTDA (Conditioned Table Generative Adversarial Network-based) data augmentation strategy. First, we apply Recursive Feature Elimination (RFE) to select the most relevant and representative features from Ethereum transaction data, removing redundant or irrelevant features to improve data quality and obtain an optimal feature subset. Next, we design a multi-dimensional synchronized embedding technique that integrates performance evaluation metrics for each feature into the embedding model, thereby enhancing the contribution of each feature to target identification. Additionally, we use CTDA for data augmentation to address the class imbalance issue, further improving the model’s robustness and accuracy. Finally, we evaluate a CT-ARF detector with the Ethereum fraud detection dataset using LightGBM, XGBoost, CatBoost, AdaBoost, RF, DT, and KNN. The results demonstrate that a CT-ARF detector effectively improves the recall rate of Ethereum fraud detection while reducing the false negative rate.

Niranjan Sapkota

This research explores the factors contributing to the failure of cryptocurrency exchanges by analyzing a sample of 845 exchanges. Using logit and probit models, it identifies key variables affecting cryptocurrency exchange defaults. The results show that cryptocurrency exchanges that are centralized, located in countries with high transparency indices, and offer fewer peer cryptocurrencies are more likely to default. Additionally, exchanges that impose high withdrawal fees and have no restrictions on clients from the United States are also positively associated with defaults. Moreover, the absence of referral schemes and having lower ratings each contributes marginally to defaults. Machine learning (ML) models including random forest, support vector machine, stacked ensemble confirm the robustness and high predictability of cryptocurrency exchange defaults. • This study uses statistical and ML models to predict cryptocurrency exchange default. • Centralized exchanges from high-transparency-index nations are more prone to default. • Limited coin listings, high fees, and U.S. client access increase the default risk. • Lacking referral programs and low ratings both marginally contribute to default.

Rik Ghosh, Arka Datta, Aggarwal, Vidhi, Sudipan Sinha · 5 authors

Decentralized Finance (DeFi), a financial ecosystem without centralized controlling organization, has introduced a new paradigm for lending and borrowing. However, its capital efficiency remains constrained by the inability to effectively assess the risk associated with each user/wallet. This paper introduces the 'On-Chain Credit Risk Score (OCCR Score) in DeFi', a probabilistic measure designed to quantify the credit risk associated with a wallet. By analyzing historical real-time on-chain activity as well as predictive scenarios, the OCCR Score may enable DeFi lending protocols to dynamically adjust Loan-to-Value (LTV) ratios and Liquidation Thresholds (LT) based on the risk profile of a wallet. Unlike existing wallet risk scoring models, which rely on heuristic-based evaluations, the OCCR Score offers a more objective and probabilistic approach, aligning closer to traditional credit risk assessment methodologies. This framework can further enhance DeFi's capital efficiency by incentivizing responsible borrowing behavior and optimizing risk-adjusted returns for lenders.

Gibran Gómez, Kevin van Liebergen, Davide Sanvito, Giuseppe Siracusano · 6 authors

Cryptocurrency abuse reporting services are a valuable data source about abusive blockchain addresses, prevalent types of cryptocurrency abuse, and their financial impact on victims. However, they may suffer data pollution due to their crowd-sourced nature. This work analyzes the extent and impact of data pollution in cryptocurrency abuse reporting services and proposes a novel LLM-based defense to address the pollution. We collect 289K abuse reports submitted over 6 years to two popular services and use them to answer three research questions. RQ1 analyzes the extent and impact of pollution. We show that spam reports will eventually flood unchecked abuse reporting services, with BitcoinAbuse receiving 75% of spam before stopping operations. We build a public dataset of 19,443 abuse reports labeled with 19 popular abuse types and use it to reveal the inaccuracy of user-reported abuse types. We identified 91 (0.1%) benign addresses reported, responsible for 60% of all the received funds. RQ2 examines whether we can automate identifying valid reports and their classification into abuse types. We propose an unsupervised LLM-based classifier that achieves an F1 score of 0.95 when classifying reports, an F1 of 0.89 when classifying out-of-distribution data, and an F1 of 0.99 when identifying spam reports. Our unsupervised LLM-based classifier clearly outperforms two baselines: a supervised classifier and a naive usage of the LLM. Finally, RQ3 demonstrates the usefulness of our LLM-based classifier for quantifying the financial impact of different cryptocurrency abuse types. We show that victim-reported losses heavily underestimate cybercriminal revenue by estimating a 29 times higher revenue from deposit transactions. We identified that investment scams have the highest financial impact and that extortions have lower conversion rates but compensate for them with massive email campaigns.

Polaki Durga Prasad, Yelleti Vivek, Vadlamani Ravi

Federated Learning (FL) has garnered widespread attention in finance, banking, and healthcare due to its decentralized, distributed training and the ability to protect privacy while obtaining a global shared model. However, FL faces challenges such as communication overhead and limited resource capability. This motivated us to propose a first-of-its-kind, two-stage FL approach as follows: (i) During phase I, under non-federated settings, synthetic dataset is generated by employing two different probability distributions as noise to the vanilla conditional tabular generative adversarial neural network (CTGAN) resulting in modified CTGAN. We also employed standard metrics to assess the quality of synthetic datasets. (ii) In phase II, the Federated Probabilistic Neural Network (FedPNN) is developed for building globally shared classification model. Despite PNN being a one-pass learning classifier, its complexity depends on the training data size. Therefore, we employed a modified evolving clustering method (ECM), another one-pass algorithm, to cluster the training data, in between the input and pattern layers of the FedPNN. The effectiveness of our approach is validated on credit card fraud detection and Polish bankruptcy prediction datasets.

Keting Yin, Zheng Zhu, XiaoXue Ren, Xing Wang

In recent years, Non-Fungible Token (NFT) has gradually become the key application of blockchain technology. Static NFT is the most common type of NFT. Once static NFT is minted on the blockchain, its additional metadata is immutable. However, some NFTs that mark real assets, games, sports, and other types must dynamically update the metadata. Therefore, a dynamic NFT with changeable features is needed. The emergence of dynamic NFT has greatly expanded the application innovation scene, and promoted the rapid development of community ecology, but also brought new problems and challenges to anti-fraud and supervision. This paper aims to realize the classification and detection of dynamic NFT. First, define and classify dynamic NFTs from both dynamic and static perspectives. Second, a complete dataset of dynamic NFT smart contract codes on Ethereum was constructed for the first time, and analyzed from multiple perspectives. Third, a smart contract feature model of dynamic NFT is proposed, and machine learning methods are used for recognition and classification. After experimental verification, the method proposed in this article can be effectively used to detect and identify dynamic NFTs, helping NFT holders avoid risks.

Hanyao Gao, Gang Kou, Haiming Liang, Hengjie Zhang · 7 authors

Abstract This study provides a comprehensive review of machine learning (ML) applications in the fields of business and finance. First, it introduces the most commonly used ML techniques and explores their diverse applications in marketing, stock analysis, demand forecasting, and energy marketing. In particular, this review critically analyzes over 100 articles and reveals a strong inclination toward deep learning techniques, such as deep neural, convolutional neural, and recurrent neural networks, which have garnered immense popularity in financial contexts owing to their remarkable performance. This review shows that ML techniques, particularly deep learning, demonstrate substantial potential for enhancing business decision-making processes and achieving more accurate and efficient predictions of financial outcomes. In particular, ML techniques exhibit promising research prospects in cryptocurrencies, financial crime detection, and marketing, underscoring the extensive opportunities in these areas. However, some limitations regarding ML applications in the business and finance domains remain, including issues related to linguistic information processes, interpretability, data quality, generalization, and the oversights related to social networks and causal relationships. Thus, addressing these challenges is a promising avenue for future research.