Transaction Risk Research Articles

EVALUATING THE IMPACT OF MULTI FACTOR AUTHENTICATION ON CYBERSECURITY EFFECTIVENESS

In today's digital age, the convenience of online financial transactions is accompanied by rising cybersecurity risks. The shift to digital platforms for conducting daily financial activities has significantly exposed customers to cyber threats. These threats often result from unauthorized access, phishing attacks, and fraud attempts by cybercriminals. Ensuring the security of these transactions is crucial to maintain users' trust and to protect sensitive financial data. This study proposes a comprehensive security framework to strengthen the safety of online financial transactions, using a combination of multi-factor authentication (MFA) and a machine learning-based fraud detection system, termed "E-cyberboost." The framework operates on two primary layers, each designed to address a different aspect of security: identity verification and real-time fraud detection. The first layer involves multi-factor authentication, where users must verify their identity. This layer typically combines something the user knows (like a password) with something they have (such as a one-time password or OTP sent to their mobile device) or something they are (such as fingerprint or facial recognition). MFA significantly reduces unauthorized access by ensuring that even if one authentication factor is compromised, additional verification is required to gain access. The second layer introduces an advanced machine-learning component. Named "E-cyberboost," this machine learning model actively monitors ongoing transactions, detecting patterns that may indicate fraudulent activity. If the system identifies any unusual or suspicious transaction behaviour, it immediately triggers a security response, such as additional user verification or temporarily blocking the transaction. This proactive approach allows the system to address potential fraud in real time, adapting to evolving threats by learning from previous incidents and continuously improving its detection accuracy. By combining the preventive measures of MFA with the responsive capabilities of machine learning, this framework provides robust security against both common and sophisticated cyber threats. The study discusses the methodology, implementation, and effectiveness of this layered approach in reducing the risk of unauthorized transactions and protecting users' financial information. The proposed framework aims to enhance trust in online financial platforms and ensure a safer digital transaction experience for users. Index Terms—Multifactor authentication, machine learning, fraud detection, E-cyberboost

Literacy dan Income Terhadap Pinjaman Online Pada Generasi Milenial

The development of digital technology has changed many aspects of life, including in the financial sector, because now there has been a Lending Loan Platform or Financial Technology (Fintech) Lending. Online loans provide easy access and processes, so it becomes an interesting alternative, especially for the Millennial generation. That way the millennials must have the ability of individuals to understand and manage personal finance, which includes skills to identify risk, interest, and obligations in every financial transaction. Then the combination of good financial literacy and stable income is the key to the use of healthy and responsible online loans.

Applications of AI in Financial Fraud Detection

Artificial intelligence (AI) has emerged as a pivotal tool in combating financial fraud, offering advanced techniques to detect and mitigate fraudulent activities in real time. By leveraging machine learning algorithms, neural networks, and pattern recognition technologies, AI systems can identify anomalies, assess transaction risks, and predict potential threats with unparalleled accuracy. This research examines the various applications of AI in financial fraud detection, including its role in transactional analysis, user authentication, and predictive modeling. Key areas of focus include the integration of supervised and unsupervised learning methods, the use of natural language processing (NLP) for fraud analysis, and real-time data processing for dynamic threat management. The study also explores the challenges of implementing AI, such as data privacy concerns, algorithmic biases, and the need for regulatory compliance. By analyzing case studies and successful implementations, this research highlights best practices for leveraging AI in financial institutions to ensure robust fraud prevention. The findings underscore AI's transformative potential to enhance security, improve operational efficiency, and build trust in financial ecosystems.

The Impact of Economic Factors on Saudi Arabia’s Foreign Trade with BRICS Countries: A Gravity Model Approach

Our investigation, bolstered by the robust gravity trade model and panel data econometric technique, underscores the pivotal factors that influence trade interactions between Saudi Arabia and the BRICS nations—Brazil, Russia, India, China, and South Africa. The study, spanning from 1998 to 2023, delves into key economic metrics such as the gross domestic product, exchange rate fluctuations, inflationary trends, political conditions, and trade deals. We employ a range of econometric strategies, including pooled Ordinary Least Squares (OLS) and fixed effects models, to reveal that the GDP of BRICS states consistently and significantly impacts trade volumes. Specifically, a 1% increase in the GDP of partner countries correlates with a 0.37% rise in trade volume within the pooled OLS model. This effect amplifies to 1.43% when adjusting for temporal and country-specific factors in the fixed effects, underscoring the importance of accommodating unobserved heterogeneity, which refers to the unmeasured factors that can influence the relationship between GDP and trade volume. The political stability of BRICS nations mitigates transactional risks and promotes more stable trade relationships, thereby enhancing trade flows. Fluctuations in exchange rates exert positive and significant effects. This indicates that a more robust Saudi Riyal, an essential policy instrument, can enhance trade by increasing the competitiveness of Saudi exports. This study demonstrates that economic magnitude, political stability, and exchange rates affect Saudi Arabia’s trade with BRICS nations. These results bolster the Kingdom’s Vision 2030 objectives for economic diversification. This research advocates for stable political climates and strategic trade agreements to enhance trade relations. This study asserts that this approach will guarantee sustainable growth and diminish the Kingdom’s reliance on oil exports, instilling optimism in the Saudi economy.

Enhancing energy security and efficiency through a regional integration policy: The case of the Southern Cone

This paper addresses the knowledge gap concerning foreign exchange risk in energy transactions within the Regional Initiative Energy Integration System of the Southern Cone Countries (SIESUR) of the Latin American region. Focusing on the cross-border electricity, it investigates the impact of exchange rate fluctuations on the transaction model in SIESUR: US dollar-denominated cross-border energy contracts with a time lag between invoicing and payment. The objective of this paper is to estimate foreign exchange risk exposure and propose mitigation solutions. Analyzing foreign exchange risk of energy trading among SIESUR member countries reveals substantial financial costs hindering electricity integration. Energy importing countries, facing greater exposure of their local currencies and weakened bargaining power, are particularly vulnerable. A Monte Carlo simulation model, using historical exchange rate data, highlights significant currency volatility, emphasizing the substantial foreign exchange risk exposure within SIESUR. Notably, cost overruns of energy imports due to foreign exchange risk exposure can reach 12.6% of transaction value. Through a global analysis, potential strategies for risk management are explored to foster electricity exchange expansion. Efforts to mitigate foreign exchange risk are crucial for advancing electricity integration within the SIESUR region. This study underscores policy implications for enhancing regional energy security and promoting sustainable development.

The Role of Social Networks in International Trade and Economic Development

In an era of global trade, cultural differences and trade barriers persist, impacting international business transactions among companies. However, research has revealed that individuals within the same social network, regardless of their physical location, experience a mitigation of information asymmetry. This, in turn, facilitates the establishment of robust cross-border business relationships and enhances the efficiency of international trade. This effect is particularly pronounced among immigrants, who often maintain social networks that bridge cultural gaps and foster social connections. Such networks can markedly amplify the volume of trade for businesses. Moreover, these social networks play a pivotal role in facilitating market entry. Sharing a common social network serves as a trust-building mechanism in cross-border trade, enabling enterprises to share transaction risks and decrease the unpredictability of transactions. The study integrates the latest research on the pivotal role of social networks in global business. The findings demonstrate that ethnic networks, such as those formed by Chinese immigrants, significantly enhance trade volumes, particularly in product categories with high information asymmetry. Greater levels of social connectivity within these networks are associated with substantially higher bilateral trade volumes, especially for goods facing significant information friction. Immigrant networks serve not only as economic connections but also as cultural and social conduits crucial for promoting international trade. In conclusion, social networks mitigate information asymmetry and facilitate rapid expansion into overseas markets for businesses. They also optimize supply chain management by monitoring market dynamics and consumer feedback in real time, while clearly identifying market opportunities.

CORRUPTION RISKS IN THE CONTEXT OF REGIONAL FINANCIAL SECURITY: ANALYSIS BASED ON KOHONEN NEURAL NETWORKS

During the war, threats to national security related to illegal financial transactions with the aggressor country, circumvention of sanctions, fraud in the distribution of investment and humanitarian aid, earmarked funds for the reconstruction of destroyed infrastructure, and new schemes for legalizing dirty money are becoming more acute. Therefore, the study of the risks of illegal financial transactions at the regional level in Ukraine is a primary step towards understanding the specifics of corruption, identifying the main factors that shape them, and developing targeted measures to overcome them. To analyze corruption risks in the regions of Ukraine and the city of Kyiv, this article uses a base of 10 indicators covering key aspects of financial support, economic activity, public opinion, trust in government, and digital transformation to provide a holistic picture of regional security and development. Clustering by the level of corruption risk, which was implemented using Kohonen's self-organizing maps, allowed to identify 4 groups of Ukrainian regions with similar characteristics: Cluster 1 (Vinnytsia, Lviv and Ternopil regions) – regions with a high level of development and medium corruption risks; Cluster 2 (Volyn, Dnipropetrovs'k, Donetsk, Zhytomyr, Ivano-Frankivsk, Kharkiv, Khmelnytskyi and Chernihiv regions) – regions with an average level of development and moderate corruption risks; Cluster 3 (Zakarpattia, Kirovohrad, and Rivne regions) - regions with an average level of development and problems in governance; Cluster 4 (Zaporizhzhia, Kyiv, Luhansk, Mykolaiv, Odesa, Poltava, Sumy, Kherson, Cherkasy, Chernivtsi regions and the city of Kyiv) – regions with high corruption risks. The implemented clustering facilitates the development of individualized and targeted anti-corruption strategies for each group of regions. The results of the study allow us to focus anti-corruption efforts on the most problematic areas and develop targeted programs to effectively reduce corruption risks.

Online transaction fraud detection in the banking sector using machine learning techniques

In this paper Nowadays, online transactions are increasing rapidly and payment scams are also increasing. A hacker is a person who gains access to another person's financial information. Improving feature selection is important to detect transaction risk in large, multidimensional data. Fraud detection and classification performance is divided into two parts: training data and test data. During this process we have focused on data analysis and pre-processing. In a transaction’s dataset, the predictive variables influence how well a machine learning algorithm for transaction risk identification classifies and identifies fraudulent transactions. This study examines the application of machine learning techniques in improving fraud detection in the banking industry. Using advanced algorithms and historical transaction data, the model aims to identify anomalous patterns that indicate fraudulent activity. Feature engineering, ensemble methods, and anomaly detection algorithms are used to improve the accuracy and performance of fraud detection systems. reducing false positives and provides an adaptive security mechanism. By harnessing the power of machine learning, this approach aims to strengthen the banking sectors defines against sophisticated fraud schemes. This study not only contributes to the academic understanding of fraud detection, but also provides industry practitioners with a practical framework for implementing proactive measures. In navigating the complex landscape of financial security, this research serves as a guide for building agile and responsive systems that stay ahead of the threats that evolve in the ever-changing landscape of the banking industry.

A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm

Wind power prediction plays a pivotal role in increasing the power grid stability and mitigating market transaction risks. To enhance the prediction accuracy of wind power, this study presents a novel chaotic time series wind power point and interval prediction approach, focusing on data processing, as well as an improved coati optimization algorithm. First, the improved wavelet threshold denoising (IWTD) technique is constructed to eliminate the noise from the original wind power data. Then, the maximal information coefficient (MIC) is adopted to determine the optimal inputs of the prediction model. Subsequently, the improved coati optimization algorithm (ICOA) is developed to optimize the hyperparameters of the bidirectional long short-term memory (BiLSTM), deep belief network (DBN), and gated recurrent unit (GRU) models, along with the linear weight coefficients of the combined model, thereby enhancing the point prediction accuracy. Finally, kernel density estimation (KDE) is employed to obtain interval predictions for wind power with different confidence levels. Results show that the proposed prediction method effectively improves the prediction accuracy compared with other popular prediction models.

BARTER TRANSACTIONS IN THE FOREIGN ECONOMIC ACTIVITY OF RUSSIAN COMPANIES UNDER SANCTIONS

The article examines the reasons for the return to barter transactions in the context of the application of economic sanctions to business operations in modern Russia, concerning difficulties with settlements in national currencies, difficulties with logistics and others. The results of the analysis of publications on the topic of barter transactions in difficult economic conditions are presented, which showed the ambiguity of scientists' opinions regarding barter transactions: some believe that this situation is associated with a shortage of funds and high inflation, others with non-payments in credit chains and the lack of the possibility of selling counter goods. The authors believe that in any case, it is advisable to take into account the specifics of barter transactions. The article provides an assessment of the dynamics of foreign trade, the trends of which in terms of exports and imports are characteristic, including for barter transactions. The advantages of barter transactions in terms of circumventing difficulties in international settlements and the lack of barter transactions related to determining the equivalent value of exchanged goods are investigated. The advantages of barter transactions are revealed, consisting in demonstrating the flexibility of conducting foreign trade in difficult economic conditions. The risks in barter transactions and the possibilities of their insurance have been identified. The results of the study can be used in the process of developing and conducting various types of barter transactions that contribute to strengthening international relations to maintain commodity exchange in difficult conditions of economic sanctions against Russia.

Suspicious transaction alert and blocking system for cryptocurrency exchanges in metaverse’s social media universes: RG-guard

In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies.

The Mortgage‐Cash Premium Puzzle

ABSTRACTAll‐cash homebuyers account for one‐third of U.S. home purchases between 1980 and 2017. We use multiple data sets and research designs to robustly estimate that mortgaged buyers pay an 11% premium over all‐cash buyers to compensate home sellers for mortgage transaction frictions. A dynamic, representative‐seller model implies only a 3% premium, which would suggest an 8% puzzle. Accounting for heterogeneity in selling conditions explains half of this difference, but a puzzle holds in conditions with high transaction risk. An experimental survey of U.S. homeowners replicates these patterns and suggests that belief distortions can explain the puzzle in these high‐risk states.

A Relational Graph Convolution Network-Based Smart Risk Recognition Model for Financial Transactions

The financial transaction relationships between existing entities are complex and diverse. In this situation, traditional risk control methods mainly ignored such complex and implicit relationship characteristics, remaining difficult to cope with complex and ever-changing financial risks. To address this issue, this paper proposes a novel relational graph convolution network (GCN)-based smart risk recognition model for financial transactions. Firstly, the classic GCN is simplified based on spatiotemporal effect. Then, feature extraction is conducted for financial transaction data, and a transformer encoder-based GCN model is proposed for risk recognition. The proposed model in this work is named as graph transformer graph convolutional network (GT-GCN) for short. In addition, fuzzy evaluation method is added into it. Finally, some experiments are conducted on real-world financial transaction data to make validation for the proposed GT-GCN. The research results indicate that the GT-GCN can not only effectively identify risks in financial transactions, but also has high accuracy and predictive ability. The application of GT-GCN to actual datasets also has good scalability and adaptability, and it can be resiliently extended into many other fields.

The Role of Information Governance in Mitigating Financial Crime Risks in Stablecoin Transactions

This study investigates the role of information governance in mitigating financial crime risks in stablecoin transactions. Using a variety of analytical techniques, including simple linear regression, sentiment analysis with NLP tools, logistic regression, and machine learning models, the research evaluates the impact of information governance on innovation, user trust, financial crimes, and the effectiveness of combined compliance measures. The findings indicate that strict information governance regulations reduce innovation but enhance market stability and user trust. Robust governance correlates strongly with increased user adoption, while higher anonymity features in stablecoins are linked to a higher incidence of financial crimes. Integrating KYC/AML compliance with transaction monitoring significantly improves the detection and prevention of financial crimes compared to standalone approaches. These insights provide valuable guidance for policymakers, regulatory authorities, and financial institutions to develop strategies that balance innovation with enhanced security and compliance in the stablecoin market.

Enhancing the security of web applications through innovative patterns of integration of artificial intelligence

Ensuring the security of digital operations, especially in the areas of e-commerce and financial transactions, remains increasingly relevant. Therefore, the subject of research is the development of a specialized software library. This library aims to improve the security of web applications. The purpose of this study is to develop a software library that uses artificial intelligence and machine learning methods to analyze and improve the level of security of financial transactions. The use of these advanced technologies helps automate the detection of potentially fraudulent or risky transactions, thereby providing a higher level of user protection. The following tasks are solved in the article: analysis of modern methods of processing financial transactions and identification of possible security threats; development of a UML diagram of library classes for processing and analyzing financial transactions; testing and validation of the developed artificial intelligence model for assessing the security of financial transactions on real financial data. Machine learning methods were defined and applied using the scikit-learn library in Python, the algorithms of which are capable of analyzing large volumes of data and identifying potential risks with high accuracy. This ensures effective integration of artificial intelligence technologies. The following results were obtained in the work: the criteria for assessing the riskiness of financial transactions for the identification of potential risks are defined; the program operation algorithm is described, which includes procedures for determining and classifying transaction risks; pseudocode is presented, which illustrates the structure of classes and methods of the model, opening opportunities for its adaptation and scaling; methods of generating test data reproducing realistic scenarios of financial transactions have been developed; an analysis of the results was carried out to assess the effectiveness of the developed model. In conclusion, the results of research and testing allow us to evaluate the model's response to various data and its effectiveness in real conditions, as the work presents examples of processing various types of transactions. In addition, the study presents not only the development and validation of the developed model, but also the prospects of its use on a larger scale, integration with existing web applications.

Payment and Transaction Risk Management at Coffeeshop X Using NIST 800-30 Framework

Payment and Transaction Risk Management at Coffeeshop X Using NIST 800-30 Framework

Implementing the Variance-Covariance Method for Assessing Market Transaction Risks in Raw Material Sector Stocks

The capital market plays a crucial role in supporting a country's economic growth. Besides being a funding source, the capital market also serves as an investment avenue for investors, particularly through stocks. Every investor must be willing to bear risks in line with their targeted returns. Risk is defined as the uncertainty of future outcomes due to market condition changes, and VaR (Value at Risk) is used to determine the tolerated loss at a certain confidence level. This study discusses the application of the Value at Risk (VaR) method using the Variance-Covariance approach to mitigate market risks in the portfolio of raw material sector stocks. The study focuses on two raw material sector stocks in Indonesia, assuming a normal distribution of asset price changes. The measurement results indicate that with an investment of Rp. 100,000,000.00, a 95% confidence level, and a 1-day period, the VaR of the portfolio of these five stocks is Rp. 2,769,750.00. This research provides critical insights to assist investors in understanding and managing portfolio risks, making VaR a key indicator to measure potential future risks and laying the foundation for decision-making in risk management.

Accounting treatment and impact of gambling agreement in Lianchuang Co., LTD

With the full entry of China's capital market into the registration system, the merger and acquisition activities of listed companies will become more and more market-oriented. In order to reduce the transaction risk of the two parties, the listed companies will often attach a gambling agreement in the merger and acquisition business. This paper adopts the case study method, with Shandong Lianchuang Internet Media Co., Ltd. acquired Shanghai Pan Investment Network Technology Co., Ltd. as the research object, and the analysis of Lianchuang Company in 2017-2020 accounting treatment. Summing up the current business accounting standards related to the gambling agreement accounting treatment facing the dilemma. It can be seen from the accounting treatment of contingent consideration that there are still some misunderstandings in the listed companies' understanding of contingent consideration, which reflects the lack of professionalism in the accounting treatment regulations related to the gambling agreement.

Hedging Strategy to Mitigate Exchange Rate Risk in Cross-Border Transactions: Literature Review

Hedging Strategy to Mitigate Exchange Rate Risk in Cross-Border Transactions: Literature Review

Dispersion in news sentiment and M&As outcomes

We suggest that dispersion in news sentiment, representing the dispersion in informative M&A performance forecasts, effectively captures M&A information uncertainty, disseminates information about transaction risks, and enhances investors’ ability to discern high-risk acquisition attempts. We find that consistent with the information uncertainty perspective, dispersion in news sentiment is associated with higher change in the acquirer's information uncertainty, lower M&A announcement return, and lower M&A completion probability. Examining the underlying channel, we show that media reduces information asymmetry between managers and investors and managers listen to the market due to the loss of reputational capital. Our findings are robust to additional analysis, endogeneity test, and robustness check. Overall, we argue that media can function as an alternative channel for corporate governance through dispersion in news sentiment.