Transaction Security Research Articles

A Study on the Role of Digital Transformation in Enhancing Bank Performance

This study investigates the role of digital transformation in enhancing the performance of banks, focusing on how technological advancements have reshaped the banking sector. The research explores various dimensions of digital transformation, including the adoption of digital banking platforms, artificial intelligence (AI), blockchain technology, and big data analytics. By analyzing secondary data from financial reports, industry studies, and case studies of banks that have undergone digital transformation, the study aims to assess the impact of these technologies on operational efficiency, customer experience, and financial performance. Key performance indicators such as cost- to-income ratios, transaction processing times, customer satisfaction levels, and risk management effectiveness are evaluated to understand the overall benefits of digital transformation. The findings reveal that digital transformation significantly enhances bank performance by improving operational efficiency, increasing customer engagement, and driving revenue growth. Specifically, the adoption of digital platforms has streamlined processes, reduced costs, and improved the accuracy of transactions. AI and machine learning have enabled more personalized customer service and effective risk management, while blockchain technology has enhanced transaction security and transparency. The research concludes with recommendations for banks to optimize their digital strategies and overcome these challenges, and suggests areas for further research to explore emerging trends and technologies in the banking sector. Keywords: Digital Transformation, Bank Performance, Financial Technology (Fintech) Artificial Intelligence (AI), Financial Performance Metrics, Technology Adoption

Discovering outlying attributes of outliers in data streams

Data streams, continuous sequences of timestamped data points, necessitate real-time monitoring due to their time-sensitive nature. In various data stream applications, such as network security and credit card transaction monitoring, real-time detection of outliers is crucial, as these outliers often signify potential threats. Equally important is the real-time explanation of outliers, enabling users to glean insights and thereby shorten their investigation time. The investigation time for outliers is closely tied to their number of attributes, making it essential to provide explanations that detail which attributes are responsible for the abnormality of a data point, referred to as outlying attributes. However, the unbounded volume of data and concept drift of data streams pose challenges for discovering the outlying attributes of outliers in real time. In response, in this paper we propose EXOS, an algorithm designed for discovering the outlying attributes of multi-dimensional outliers in data streams. EXOS leverages cross-correlations among data streams, accommodates varying data stream schemas and arrival rates, and effectively addresses challenges related to the unbounded volume of data and concept drift. The algorithm is model-agnostic for point outlier detection and provides real-time explanations based on the local context of the outlier, derived from time-based tumbling windows. The paper provides a complexity analysis of EXOS and an experimental analysis comparing EXOS with existing algorithms. The evaluation includes an assessment of performance on both real-world and synthetic datasets in terms of average precision, recall, F1-score, and explanation time. The evaluation results show that, on average, EXOS achieves a 45.6% better F1 Score and is 7.3 times lower in explanation time compared to existing outlying attribute algorithms.

Analysis of the Impact of Mobile Banking Technology, Fintech, and Digital Transaction Security on Customer Loyalty at BUMN Banks in Indonesia

This study investigates the impact of mobile banking technology, fintech innovations, and digital transaction security on customer loyalty at state-owned banks in Indonesia. Utilizing a quantitative approach, data were collected from 300 respondents through a structured questionnaire with a Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The data were analyzed using SPSS version 25, employing descriptive statistics, reliability analysis, exploratory factor analysis, correlation analysis, and multiple regression analysis. The results revealed that all three independent variables—mobile banking technology, fintech innovations, and digital transaction security—positively and significantly influence customer loyalty. Mobile banking technology had a correlation coefficient of 0.62, fintech innovations 0.57, and digital transaction security 0.68 with customer loyalty. The regression analysis showed that these factors collectively explained 38% of the variance in customer loyalty, with digital transaction security having the strongest impact. The findings underscore the importance of enhancing technological capabilities and security measures to foster customer loyalty in state-owned banks. The study provides valuable insights for banking institutions seeking to improve customer satisfaction and retention through advanced technological solutions and robust security protocols.

Adopting Artificial Intelligence to Strengthen Legal Safeguards in Blockchain Smart Contracts: A Strategy to Mitigate Fraud and Enhance Digital Transaction Security

As blockchain technology increasingly underpins digital transactions, smart contracts have emerged as a pivotal tool for automating these transactions. While smart contracts offer efficiency and security, their automation introduces significant legal challenges. Detecting and preventing fraud is a primary concern. This paper proposes a novel application of artificial intelligence (AI) to address these challenges. We will develop a machine learning model, specifically a Convolutional Neural Network (CNN), to effectively detect and mitigate fraudulent activities within smart contracts. The AI model will analyze both textual and transactional data from smart contracts to identify patterns indicative of fraud. This approach not only enhances the security of digital transactions on blockchain platforms but also informs the development of legal standards and regulatory frameworks necessary for governing these technologies. By training on a dataset of authentic and fraudulent contract examples, the proposed AI model is expected to offer high predictive accuracy, thereby supporting legal practitioners and regulators in real-time monitoring and enforcement. The ultimate goal of this project is to contribute to legal scholarship by providing a robust technological tool that aids in preventing cybercrimes associated with smart contracts, thereby laying a foundation for future legal research and development at the intersection of law, technology, and security.

Identification and Analysis of Credit Card Fraud Based on Machine Learning Methods

Credit card has become an indispensable payment tool in People's Daily life. However, credit card fraud has also increased, resulting in huge financial losses for banks and individuals. Based on the real world credit card transaction data, this paper uses a variety of machine learning algorithms to identify and analyze credit card fraud. Firstly, the problem of missing and unbalanced data is solved through data preprocessing and feature selection, and important features are screened out, Such as transaction amount, product code, payment card type, etc. Then, integrated logistic regression, random forest, LightGBM, XGBoost, and Stacking models were constructed, and the models were trained and evaluated through cross-validation and parameter optimization. The experimental results show that the integrated Stacking model performs best in credit card fraud detection, with an AUC(Area Under the Curve) value of 0.960, which can accurately identify fraudulent transactions and provide an effective fraud warning mechanism for financial institutions. This mechanism significantly benefits both financial institutions and credit card users by reducing fraud losses and enhancing transaction security. However, it is acknowledged that the study has limitations, including the potential impact of data imbalance on model performance, the need for further testing of model generalization ability to new fraud patterns, and the importance of optimizing real-time performance to ensure practical applicability.

Security matters: Empowering e-commerce in Sri Lanka through customer insights

In the fast-paced, post-COVID digital world, e-commerce presents promising prospects for significant advancement. However, customers often feel uncertain due to persistent concerns about the robustness of security measures safeguarding e-commerce platforms. The primary objective of our study was to identify factors affecting the security of e-commerce platforms based on the perceptions of Sri Lankan customers. This research was conducted using data collected from Sri Lankan e-commerce users via both online and offline surveys. An ordered probit regression model was utilised, demonstrating that transaction security, privacy, vendor system security, and platform quality positively impact the perceived security of e-commerce. The e-commerce industry in Sri Lanka is expected to see growth and an increased user penetration rate. The findings of this study are anticipated to assist e-commerce business owners and policymakers in addressing critical security issues, namely vulnerabilities in transactional security, low privacy, inadequate system security, and poor e-commerce platform quality. These improvements are expected to build trust and credibility among consumers, maximising e-commerce success.

Perbandingan Hukum Lelang Non Eksekusi Sukarela pada E-Market Place Auction antara Indonesia dengan Amerika Serikat

This research aims to compare non-executive voluntary auction laws on e-marketplaces between Indonesia and the United States. In Indonesia, the regulatory framework for non-executive voluntary auctions falls under the jurisdiction of the Ministry of Finance, focusing on transparency, consumer protection, and fairness in the auction process. The study highlights that despite Indonesia's adoption of digital technology to enhance auction accessibility and efficiency, regulations are still evolving to keep pace with technological advancements and market developments. Meanwhile, in the United States, non-executive voluntary auctions are detailed under federal and state laws, emphasizing consumer protection, transaction security, and robust enforcement to prevent harmful business practices. These stringent regulations encompass participant verification, bidding processes, and transaction settlement to ensure fairness and safety for all involved parties. The findings of this research indicate that differences in legal regulations, implementation, and approaches between Indonesia and the United States influence how non-executive voluntary auctions are conducted and governed in both countries. This comparison provides deep insights into the challenges and opportunities in developing legal policies that support digital economic growth in this era of globalization.

SMPTC3: Secure Multi-Party Protocol Based Trusted Cross-Chain Contracts

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

The blockchain university: disrupting 'disruption'?

This paper explores the promise of disruption of higher education offered by latest platform technologies - a combination of mobile applications for connecting teachers and students and blockchain technology for secure transactions of information and money. We start with a brief examination of several generations of technological disruptions arriving from the Silicon Valley with a special focus to educational technology. Showing that these disruptions are primarily focused to furthering capitalist mode of production, we question whether the latest disruption could provide different results. We briefly examine a historical (utopian) attempt at disrupting education described in Ivan Illich's book Deschooling Society (1971). While this analysis firmly belongs into the past, it presents us with important insights about connections between education, technology, capitalism and the environment which are just as relevant today. We proceed with an analysis of the world's first blockchain university - the Woolf University. Advertised as 'Uber for students, Airbnb for teachers', the Woolf University offers the seductive promise of radical transformation of higher education based on cooperative principles. We examine blockchain technology in detail and identify its main novelty - the transfer of trust from people to technologies. We briefly question this transfer, leaving a more detailed analysis for further research. Instead we focus to ideological underpinnings of the blockchain university, as they reflect to teaching, learning, and university administration. We show that further analyses of the blockchain university will be best supported by adopting a networked learning perspective and especially its wide body of knowledge about various (learning) connections. The Woolf University has not even admitted its first cohort of students, and the question remains as to whether Woolf will now adopt the blockchain in such a manner as to radically disrupt 'disruption', or it will simply blend into the existing powerful political, educational and economic structures. Our analysis, which is therefore based on early ideas about the development of the Woolf University, indicates that it has the potentials to offer cooperative learning to students, cooperative employment to academic workers, all the while retaining highest quality of teaching and learning modelled after ancient scholastic principles. On that basis, we conclude that the Woolf University, together with other adaptations of blockchain technology for educational purposes, does offer a lot of potential for fundamental disruption of higher education and should be closely watched in the times to come.

Cointegration and causality relationship of Indian stock market with selected world markets.

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

The Role of Technology in Transforming Real Estate Transactions in Uganda

Technology is significantly transforming real estate transactions in Uganda, addressing inefficiencies and modernizing the market. Digital platforms and marketplaces, such as Jumia House and Lamudi, have revolutionized property listings and accessibility, while blockchain technology enhances transaction security and reduces fraud. Mobile money and digital payments streamline financial transactions, and Geographic Information Systems (GIS) and drones improve property surveying and valuation. Artificial Intelligence (AI) and Big Data provide valuable insights and predictive analytics, optimizing investment decisions. These technological advancements have increased transparency, efficiency, and market reach, reduced costs, and improved transaction experiences. Despite challenges such as the digital divide and evolving regulatory frameworks, ongoing investment in technology and infrastructure promises further growth and innovation in Uganda’s real estate sector. Keywords: Digital Platforms, Blockchain Technology, Mobile Payments, Geographic Information Systems (GIS), Artificial Intelligence (AI)

The Role of Technology in Transforming Real Estate Transactions in Uganda

Technology is significantly transforming real estate transactions in Uganda, addressing inefficiencies and modernizing the market. Digital platforms and marketplaces, such as Jumia House and Lamudi, have revolutionized property listings and accessibility, while blockchain technology enhances transaction security and reduces fraud. Mobile money and digital payments streamline financial transactions, and Geographic Information Systems (GIS) and drones improve property surveying and valuation. Artificial Intelligence (AI) and Big Data provide valuable insights and predictive analytics, optimizing investment decisions. These technological advancements have increased transparency, efficiency, and market reach, reduced costs, and improved transaction experiences. Despite challenges such as the digital divide and evolving regulatory frameworks, ongoing investment in technology and infrastructure promises further growth and innovation in Uganda’s real estate sector. Keywords: Digital Platforms, Blockchain Technology, Mobile Payments, Geographic Information Systems (GIS), Artificial Intelligence (AI)

Enhancing dealership management software with AI integration for improved customer service and future innovations

This review paper explores the integration of Artificial Intelligence (AI) into dealership management software to enhance customer service and foster future innovations. The primary objective is to synthesize existing research and industry practices to understand how AI technologies can streamline operations, personalize customer interactions, and drive business growth within automotive dealerships. The paper reviews key AI applications, including AI-driven chatbots and virtual assistants that provide automated support, predictive analytics for customer behavior and sales forecasting, and personalized marketing strategies. The analysis reveals that AI significantly improves customer service efficiency by reducing response times and enhancing customer satisfaction. Predictive maintenance and inventory management powered by AI algorithms ensure optimal resource allocation, minimizing downtime and operational costs. Additionally, the paper identifies potential future innovations facilitated by AI advancements, such as augmented reality (AR) for virtual vehicle tours and blockchain for secure transaction management. These innovations promise to elevate the customer experience and streamline dealership processes, creating a competitive edge in the market. The integration of AI into dealership management software presents substantial benefits in improving customer service and operational efficiency. The paper emphasizes the need for continuous investment in AI technologies and employee training to fully harness these advantages. Future research should focus on exploring emerging AI applications and their long-term impact on the automotive industry, ensuring dealerships remain adaptable and competitive in a rapidly evolving technological landscape. Keywords: Artificial Intelligence, Dealership Management Software, Customer Service, Predictive Analytics, Augmented Reality, Blockchain, Automotive Industry.

Smart Contracts Management: The Interplay of Data Privacy and Blockchain for Secure and Efficient Real Estate Transactions

The digital transformation of the real estate industry is being significantly influenced by blockchain technology and smart contracts, which promise enhanced efficiency, transparency, and security in transactions. This study aims to develop a secure and efficient smart contract management protocol that balances the benefits of blockchain with robust data privacy practices. The methodology involves descriptive analytics of transaction data from the Ethereum blockchain, feasibility studies using synthetic transaction data, and a regulatory compliance analysis to map the impact of different regions' regulations on blockchain adoption in real estate. The findings reveal that while smart contracts can automate various processes and reduce reliance on intermediaries, challenges related to data privacy and regulatory compliance persist. Higher privacy features in smart contracts are associated with increased execution costs, indicating a trade-off between privacy and cost efficiency. Smart contracts with privacy level 3 had an execution cost of 0.025 ETH, compared to those with privacy level 1 at 0.02 ETH. Integrating permissioned blockchains and zero-knowledge proofs offers a promising solution, though their complexity limits broader adoption. Zero-knowledge proofs maintained high privacy (achieving privacy levels of up to 0.76) at a reasonable computational cost (proof generation time of 1.9 seconds). Thus, the integration of permissioned blockchains and zero-knowledge proofs offers a promising pathway to address these challenges. However, the complexity of these techniques requires specialized knowledge, limiting broader adoption. The study concludes with recommendations to develop specialized training programs, collaborate on regulatory frameworks, invest in advanced cryptographic research, and implement targeted strategies to overcome adoption barriers. These efforts will contribute to the digital transformation of asset management, fostering innovation and enhancing the overall efficiency of real estate transactions.

Implementasi Teknologi Pengenalan Wajah Untuk Login Website Chic Glasses Berbasis Convolutional Neural Network(CNN)

The research discussed the development of a face recognition technology for the login system of an e-commerce website. The study's main focus was enhancing the security and efficiency of online transactions through the use of biometric identification systems. The researchers implemented a Face Recognition Login Website using a CNN, known for its ability to extract features and classify faces effectively. The research involved various stages, such as data collection through a web camera, data pre-processing, and the design of a CNN architecture with feature learning and classification layers. By deploying this CNN-based facial recognition technology on e-commerce websites, the researchers expect to add an extra layer of security, boost user trust, and offer a safer and more convenient online transaction experience.

Personalization and Customer Experience in E-Commerce

Innovation is crucial for maintaining a competitive edge and delivering exceptional shopping experiences in the ever-evolving realm of e-commerce. Businesses continuously seek creative approaches to enhance the e-commerce landscape, incorporating technologies like immersive experiences, seamless payment solutions, Artificial Intelligence (AI), and personalized services. This study investigates the emerging technologies and innovations within e- commerce, particularly focusing on the application and impact of AI. Utilizing conceptual research methodology and secondary data from various scholarly articles, journals, and online sources, the study concludes that recent innovations such as AI-driven personalization, Augmented Reality (AR) and Virtual Reality (VR), chatbots, blockchain for transaction security and supply chain management, social commerce, drone delivery, and e-wallet solutions have significantly advanced the e- commerce sector. Noteworthy AI applications include platforms like Google AI Cloud Platform, IBM Watson, BigML, and Infosys Nia, which are also leveraged for product pricing and distribution strategies. Additionally, the adoption of diverse payment methods such as e- wallets and UPI contributes to enhancing user satisfaction in e-commerce transactions.

Comparative secured transactions law: how and to what extent do global standards affect the reform of Japanese inventory financing law

Abstract Since March 2021 Japan has been undertaking official legislative discussions at the Committee of Secured Transactions Law Reform under the Legislative Council of the Ministry of Justice Japan to draft a statute of secured transactions of personal property. These discussions are now in their final stages. Enhancing the reliability and feasibility of security devices for inventory financing is the core purpose of the proposed legislation. As a result of policy-making decisions, the conventional Japanese security device of ownership transfer for security purposes will not be replaced by a new regime which reflects the modern principles originating from the UCC9 and illustrated in the UNCITRAL Model Law on Secured Transactions. However, the significance of such global standards in the context of Japanese law has been two-fold. First, they illustrate what conventional Japanese law achieves through its own alternative rules. Second, the Council Committee is likely to propose several changes to refresh our conventional security device by adding new principles which have traditionally been of less concern to Japanese practitioners. In this article, the author substantiates the influence of these global standards on Japanese legislation. This article focuses on the functional approach taken by the ownership transfer type security interest, the rules relevant to publicity of security interests as modified by the first-to-register-or-possession rule as suggested by global standards, the concept of aggregate of inventory as collateral as modified by registration to cover perfection in security interests in goods joining the aggregate after a debtor's dishonest disposition, and the assignment of accounts receivables accompanied with ownership transfer of inventory which may be viewed as functionally close to security interests in proceeds of inventory.

Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security

Financial fraud poses a significant threat to the global economy, necessitating advanced measures for detection and prevention. This paper explores the application of machine learning techniques to enhance transaction security and combat financial fraud. It provides a comprehensive overview of machine learning algorithms, including supervised and unsupervised learning, neural networks, and anomaly detection. Each technique's application in identifying and preventing fraudulent activities is discussed, along with their advantages and limitations. Challenges in implementing machine learning for fraud detection, such as data quality, scalability, real-time processing, and model interpretability, are examined. Ethical and privacy concerns associated with using machine learning in financial transactions are also addressed. By highlighting these aspects, the paper aims to contribute to developing more effective and ethical machine learning-based fraud detection systems, ensuring robust transaction security and fostering trust in financial institutions.

Blockchain technology in financial services: enhancing security, transparency, and efficiency in transactions and services

Blockchain technology revolutionizes financial services by enhancing security, transparency, and efficiency in transactions and services. This paper examines the fundamental aspects of blockchain, including its core concepts and components, and explores its application in financial services. Blockchain's cryptographic hashing, decentralization, and immutability significantly improve security by preventing tampering and reducing fraud risks. Increased transparency through immutable audit trails, real-time updates, and open ledgers fosters trust among customers, regulators, and institutions. Furthermore, blockchain streamlines operations by automating processes, reducing intermediaries, and expediting transactions, resulting in substantial cost reductions and improved efficiency. Despite these advantages, challenges such as scalability, interoperability, regulatory uncertainty, and environmental impact must be addressed for widespread adoption. The prospects of blockchain in financial services are promising, with potential applications in decentralized finance, digital identity verification, and central bank digital currencies. This paper underscores the transformative potential of blockchain technology in creating a more secure, transparent, and efficient financial ecosystem.

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.