Blockchain Papers

Yu-Ru Feng, Yufeng Wang

No abstract is available for this record.

Jing Zhao

No abstract is available for this record.

Shivani Bharatbhai Patel, Pronaya Bhattacharya, Sudeep Tanwar, Neeraj Kumar

In this paper, we propose KiRTi, a deep-learning-based credit-recommender scheme for public blockchain to facilitate smart lending operations between prospective borrowers (PB) and prospective lenders (PL) to eliminate the need of third party credit-rating agencies (CRAs) for credit-score (CS) generation. Thus loan grants to PB from PL is secured, authorized, and automated so as to expedite the disbursement process. KiRTi stores PB historical transactions, current assets, and liabilities as time-series sequenced data in a public blockchain. The sequenced data is fetched from blockchain by a long-short term memory (LSTM) model that generates CS for loan recommendations based on proposed lending algorithms for PB and PL. To ensure real-time updation of CS, edge-weights are updated based on boolean indicators from PB and PL, which indicates the successful repayments and loan-defaults. The process is iterated to improve the accuracy of edge-weights and generated CS to ensures the correct credibility of PB for future lending. Smart contracts (SC) are proposed for automatic setup of loan repayments between PB and PL. To model the LSTM recommender scheme, a German credit dataset from UCI repository is considered with 1000 credit-histories of PB, with 700 successful repayments and 300 defaults. KiRTi achieves an accuracy of 97.5% in comparison to conventional approaches with an F-measure of 0.98304. The security evaluation of KiRTi shows that it has computation cost of 20.96 ms and communication cost of 121 bytes compared to other state-of-the-art approaches.

Ramani Teegala

The increasing digitization of financial services by the late 2010s resulted in the generation of massive volumes of transactional data across payment systems, trading platforms, digital banking applications, and regulatory reporting pipelines. These transaction logs, originally designed for auditing, reconciliation, and failure recovery, gradually emerged as a valuable source of behavioral and operational insight. However, the scale, velocity, and structural heterogeneity of transactional logs posed significant challenges to traditional analytical techniques, which were often optimized for static datasets or narrowly defined reporting use cases. As a result, organizations began exploring systematic approaches to mine patterns from transaction logs in order to better understand system behavior, detect anomalies, and improve decision-making. Pattern mining from transaction logs refers to the process of discovering recurring structures, sequences, correlations, and deviations within recorded transactional events. By September 2019, this practice was informed by a combination of data mining research, distributed systems logging techniques, and operational analytics developed in large-scale production environments. Unlike conventional business intelligence queries, pattern mining emphasizes the identification of latent relationships and temporal structures that are not explicitly encoded in application logic. These patterns may reflect normal operational workflows, emergent system behaviors, or early indicators of faults, fraud, or performance degradation. In financial systems, transaction logs capture more than simple state changes; they encode regulatory-relevant actions such as authorization decisions, settlement progressions, risk evaluations, and ledger mutations. Mining patterns from these logs enables institutions to analyze end-to-end transaction lifecycles, correlate technical events with business outcomes, and identify systemic inefficiencies or vulnerabilities. Importantly, such analysis must operate within strict constraints related to data privacy, auditability, and regulatory compliance, distinguishing transaction log mining in financial domains from analogous practices in less regulated environments. This paper examines pattern mining from transaction logs as understood and applied by September 2019, situating it within the broader evolution of logging, distributed systems observability, and data mining research. It synthesizes academic literature and industry practices to propose a conceptual and architectural framework for extracting meaningful patterns from transactional data at scale. The analysis focuses on methodological considerations, architectural layering, and practical challenges encountered in regulated, high-throughput systems, while avoiding retrospective interpretations based on post-2019 technologies or techniques.

Hans Byström

In this paper I discuss how blockchains potentially could affect the way credit risk is modeled, and how the improved trust and timing associated with blockchain-enabled real-time accounting could improve default prediction. To demonstrate the (quite substantial) effect the change would have on well-known credit risk measures, a simple case-study compares Z-scores and Merton distances to default computed using typical accounting data of today to the same risk measures computed under a hypothetical future blockchain regime.

Yunsen Wang

This dissertation consists of three essays that design and evaluate the continuous audit analytics and fraud prevention systems using three emerging technologies (i.e., the blockchain, in-memory cloud computing, and deep learning). The first essay designs a framework of Blockchain-based Transaction Processing System using the homomorphic encryption and zero-knowledge proof mechanisms. Furthermore, this study develops a prototype of the designed system to demonstrate its applications in real-time accounting, continuous monitoring, and fraud prevention. Although the simulation tests show the Blockchain-based Transaction Processing System consumes more computational overhead than the conventional database-based ERP system, the blockchain should be considered as a promising technology for future accounting and auditing practice. The second essay introduces the database architecture that manages data in main physical memory and columnar format. This essay proposes a conceptual framework for applying the in-memory columnar database system to support high-speed continuous audit analytics. Moreover, this study develops a prototype and conducts the simulation tests to evaluate the proposed framework. The test results show the high efficiency and effectiveness of the in-memory columnar database relative to the conventional ERP system regarding the computational time and the storage volume. Furthermore, the deployment of the in-memory columnar database to the cloud shows great promise of applying the in-memory columnar database for continuous audit analytics. The third essay designs a continuous fraud detection system based on modified deep learning technology. Specifically, this essay builds an accounting layer on top of the deep learning architecture to process financial data for predicting the fraudulent financial statements. A prototype is developed to evaluate the prediction accuracy of the proposed design. The test results show the deep learning-based continuous fraud detection system provides high prediction accuracy relative to the existing studies of financial statement fraud detection.

Henrik Smeby

Oppgaven analyserer noen sentrale skatterettslige sider ved bruk av BitCoin etter norsk rett. Problemstillingene vil også ha relevans for andre kryptovalutaer såfremt de fungerer på samme måte.

Simon Cornée

No abstract is available for this record.