Journals
A Disruptive Blockchain-Based Remittance Network Model for Financial Institutions
The Society for Worldwide Interbank Financial Telecommunication (SWIFT) has long served as a critical infrastructure for cross-border payments, but its high fees and slow processing times have prompted calls for improvement. In response, this paper introduces ”Rhythm,” a novel remittance network model that integrates characteristics of both consortium and public blockchains. Rhythm allocates a portion of remittance fees to incentivize participant nodes, such as banks, thereby enhancing network security and supporting fair distribution of mining rewards based on computing resource contributions. To further improve network efficiency, Rhythm partitions each block into Transaction Space for remittance transactions and Reserved Space for instruction messages, with instruction volume dynamically adjusted according to transaction urgency. This design encourages miner nodes to optimize block composition by balancing transaction fees with transaction urgency. We conduct experiments using historical remittance data from Industrial and Commercial Bank of China (Macau) Limited under various scenarios. The results demonstrate that Rhythm provides stable incentives, fair rewards, and improved processing efficiency. These findings suggest that Rhythm offers financial institutions a flexible, efficient, and cost-effective alternative for cross-border payment operations, while also supporting inclusive participation and dynamic transaction prioritization.
Minimizing block incentive volatility through Verkle tree-based dynamic transaction storage
Transaction fees are a crucial revenue source for miners in public and consortium blockchains. However, while public blockchains have additional revenue streams, transaction fees serve as the primary income for miners in consortium blockchains formed by various financial institutions. These miners allocate different levels of computing resources to process transactions and earn corresponding fees. Nonetheless, relying solely on transaction fees can lead to significant volatility and encourage non-standard mining behaviors, thereby posing threats to the blockchain’s security and integrity. Despite previous attempts to mitigate the impact of transaction fees on illicit mining behaviors, a comprehensive solution to this vulnerability is yet to be established. To address this gap, we introduce a novel approach that leverages Dynamic Transaction Storage (DTS) strategies to effectively minimize block incentive volatility. Our solution implements a Verkle tree-based storage mechanism to reduce bandwidth consumption. Moreover, to configure the DTS strategies, we evaluate several optimization algorithms and formulate the challenge as a Vehicle Routing Problem. Our experiments conducted using historical transactions from Bitcoin and remittance data from the Industrial and Commercial Bank of China reveal that the strategy focusing on time-based transaction incorporation priority, while excluding a designated space for small-fee transactions, as discovered by the gradient-based optimizer algorithm, proves most effective in reducing volatility. Hence, the DTS strategy can sustain stable block incentives irrespective of transaction types or user bidding behavior. Furthermore, the inclusion of higher-fee transactions, often smaller in size, can alleviate propagation delays and the occurrence of forks.
Mitigating Blockchain Extractable Value threats by Distributed Transaction Sequencing Strategy
The rapid growth of blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including sandwich attacks, liquidations, transaction replay etc. have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with sandwich attacks being particularly impactful. Additionally, the growing adoption of blockchain in traditional finance highlights the challenge of TID, wherein high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS) that integrates forking mechanisms with an Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of a Normalized Allocation Disparity Metric (NADM) that ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrated that the DTSS effectively mitigated BEV risks, enhanced transaction fairness, and significantly improved the security and transparency of DeFi ecosystems.
An efficient dynamic transaction storage mechanism for sustainable high-throughput Bitcoin
As coin-based rewards dwindle, transaction fees play an important role as mining incentives in Bitcoin. In this paper, we propose a novel mechanism called efficient dynamic transaction storage (EDTS) for dynamically allocating transactions among blocks to achieve efficient storage utilization. By leveraging a combination of Cuckoo Filter and dynamic transaction storage (DTS) strategies, EDTS is able to improve the scalability while remaining sustainable even after Bitcoin enters a transaction-fee regime. In addition to preventing deviant mining behaviors under the transaction-fee regime, EDTS can also provide differentiated transmission priorities based on transaction fees while allowing the investors to engage in pledging more transaction fees. In EDTS, we applied the multi-objective optimization algorithm U-NSGA-III to find the best DTS strategy and its corresponding attributes. Experimental results show that the EDTS mechanism together with the optimized DTS strategies can achieve a throughput of 325.3 TPS. EDTS offers scalability improvements comparable to the best Graphene solution and outperforms most of the latest scaling solutions by at least 11.6% while maintaining sustainability under the transaction-fee regime.
CDGAT: a graph attention network method for credit card defaulters prediction
Recognizing potential defaulters is a crucial problem for financial institutions. Therefore, many credit scoring methods have been proposed in the past to address this issue. However, these methods rarely consider the interaction among customers such as bank transfer and remittance. With rapid growth in the number of customers adopting online banking services, such interaction information plays a significant role in assessing their credit score. In this paper, we propose a novel scalable credit scoring approach called CDGAT (Graph attention network for credit card defaulters) for predicting potential credit card defaulters. In CDGAT, a customer’s credit score is calculated based on transaction embedding and neighborhood embedding. To obtain the neighborhood embedding, CDGAT first utilizes the Amount-bias Sampling (AbS) strategy to extract a subgraph for each customer. Next, CDGAT directly aggregates neighbors’ features according to their influence weights. The experimental results on the dataset from Industrial and Commercial Bank of China (Macau) Limited (ICBC (Macau)) show that CDGAT significantly outperforms the baseline methods. Furthermore, experimental results reveal that the proposed method is also superior to several state-of-the-art Graph Convolutional Neural Network models in terms of scalability and performance.
Conference Proceedings
X. Zhao and Y. -W. Si, “NFTCert: NFT-Based Certificates With Online Payment Gateway,”
2021 IEEE International Conference on Blockchain (Blockchain), Melbourne, Australia, 2021, pp. 538-543, doi: 10.1109/Blockchain53845.2021.00081.
X. Zhao and Y. -W. Si, “Dynamic Transaction Storage Strategies for a Sustainable Blockchain,”
International Conference on Services Computing (SCC), Chicago, IL, USA, 2021, pp. 309-318, doi: 10.1109/SCC53864.2021.00044.
X. Zhao and Y. -W. Si, “Challenges of Blockchain adoption in financial services in China’s Greater Bay Area,”
2023 Fifth International Conference on Blockchain Computing and Applications (BCCA), Kuwait, Kuwait, 2023, pp. 68-75, doi: 10.1109/BCCA58897.2023.10338862.
H. -W. Long, X. Zhao & Y. -W. Si, “Dynamic Mining Interval to Improve Blockchain Throughput,”
International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 46-55, doi: 10.1109/BigData59044.2023.10386281.