Search Results (193)

Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic synthetic speech. Addressing the vulnerabilities inherent to voice-based authentication systems has thus become both urgent and essential. This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. Our comparative analysis demonstrates significant performance gains when dimensionality reduction methods are applied, underscoring their value in enhancing the security and effectiveness of voice biometric verification systems against emerging spoofing threats. Full article

This paper measures the post-treatment effects of Central Bank Digital Currencies (CBDCs) on mobile money and outstanding loans from commercial banks as a percentage of the GDP in Nigeria and the Bahamas, respectively, from the perspective of financial inclusion. The literature on the topic has primarily focused on the technological specifications of CBDCs and their potential future implementation. This article addresses a gap in the empirical literature by examining the effects of CBDCs. To this end, a Synthetic Control Method (SCM) is applied to the Bahamas (SandDollar) and Nigeria (eNaira) to construct a counterfactual scenario and assess the impact of CBDCs on mobile money and commercial bank loans. Nigeria’s mobile money transactions as a percentage of the GDP increased significantly compared to the synthetic control group, suggesting a notable positive effect of the eNaira. Conversely, in the Bahamas, actual performance fell below the synthetic control, implying that SandDollar may have contributed to a decline in outstanding loans. These results suggest that CBDCs could pose a “deposit substitution risk” for commercial banks. However, they may also enhance the performance of other Fintech tools, as observed in the case of mobile money. As CBDC implementations worldwide remain in their early stages, their long-term effects require further analysis. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

Increased digital banking operations have brought about a surge in suspicious activities, necessitating heightened real-time fraud detection systems. Conversely, traditional static approaches encounter challenges in maintaining privacy while adapting to new fraudulent trends. In this paper, we provide a unique approach to tackling those challenges by integrating VAE-QLSTM with Federated Learning (FL) in a semi-decentralized architecture, maintaining privacy alongside adapting to emerging malicious behaviors. The suggested architecture builds on the adeptness of VAE-QLSTM to capture meaningful representations of transactions, serving in abnormality detection. On the other hand, QLSTM combines quantum computational capability with temporal sequence modeling, seeking to give a rapid and scalable method for real-time malignancy detection. The designed approach was set up through TensorFlow Federated on two real-world datasets—notably IEEE-CIS and European cardholders—outperforming current strategies in terms of accuracy and sensitivity, achieving 94.5% and 91.3%, respectively. This proves the potential of merging VAE-QLSTM with FL to address fraud detection difficulties, ensuring privacy and scalability in advanced banking networks. Full article

Blockchain technology is being increasingly deployed to store and process transactions and information in the global financial sector. Blockchain underpins cryptocurrencies such as Bitcoin and facilitates decentralized finance (DeFi), representing a paradigm shift in the global financial landscape, offering alternative solutions to traditional banking, and fostering financial inclusion. In developing economies such as Morocco, where a significant portion of the population remains unbanked, these digital financial innovations present both opportunities and challenges. This study examines the potential role of cryptocurrencies and DeFi in enhancing financial inclusion in Morocco, where cryptocurrencies have been banned since 2017. However, the public continues to use cryptocurrencies, circumventing restrictions, and the Moroccan Central Bank is now preparing to introduce new regulations to legalize their use within the country. In this context, this article analyses the potential of cryptocurrencies to mitigate barriers such as high transaction costs, restricted access to financial services in rural areas, and limited financial literacy in the country. The study pursues a mixed-methods approach, which combines a quantitative survey with qualitative expert interviews and adapts the Unified Theory of Acceptance and Use of Technology (UTAUT) model to the Moroccan context. The findings reveal that while cryptocurrencies offer cost-efficient financial transactions and improved accessibility, their adoption may be constrained by regulatory uncertainty, security risks, and technological limitations. The novelty of the article thus lies in its focus on the key mechanisms that influence the adoption of cryptocurrencies and their potential impact in a specific national context. In so doing, the study highlights the need for a structured regulatory framework, investment in digital infrastructure, and targeted financial literacy initiatives to optimize the potential role of cryptocurrencies in progressing financial inclusion in Morocco. This underscores the need for integrated models and guidelines for policymakers, financial institutions, and technology providers to ensure the responsible introduction of cryptocurrencies in developing world environments. Full article

The recent transformation in Libya’s banking industry, driven largely by the Central Bank of Libya, has led to increased financial inclusion, enhanced banking services, and the adoption of digital banking technologies. While most banks have rapidly transitioned from traditional data analysis methods to using Artificial Intelligence (AI) for daily transaction analysis, the impact of AI on sustainable innovation performance and organizational learning remains underexplored. This study, grounded in dynamic capabilities theory, investigates the mediating role of organizational learning in the relationship between AI adoption in the banking sector and sustainable innovation performance. Data were collected from 401 employees across Libya’s conventional and Islamic banking sectors using a judgmental sampling technique. Partial Least Squares Structural Equation Modeling (PLS–SEM) was used to analyze the data and assess the relationships among the variables. The findings indicate that AI adoption significantly and positively influences sustainable innovation performance and organizational learning. Additionally, organizational learning was found to have a significant positive effect on sustainable innovation performance and to partially mediate the relationship between AI adoption and innovation performance. The study recommends that bank management teams implement training programs to enhance employees’ understanding of AI applications, sustainability objectives, and innovative financial services to improve overall efficiency. Full article

The rising complexity of financial fraud in highly digitalized regions such as the Gulf Cooperation Council (GCC) poses challenging issues owing to class imbalance, adversarial attacks, concept drift, and explainability requirements. This paper suggests a hybrid machine-learning framework (HMLF) that incorporates SMOTEBoost and cost-sensitive learning to address imbalances, adversarial training and FraudGAN to ensure robustness, DDM and ADWIN to achieve adaptive learning, and SHAP, LIME, and human-in-the-loop (HITL) analysis to ensure explainability. Employing real transaction data from the GCC banks, the framework is tested through a design science research approach. Experiments illustrate significant gains in fraud recall (from 35% to 85%), adversarial robustness (attack success rate decreased from 35% to 5%), and drift recovery (within 24 h), while retaining operational latency below 150 milliseconds. This paper substantiates that incorporating technical resilience with institutional constraints offers an auditable, scalable, and regulation-compliant solution for detecting fraud in high-risk financial contexts. Full article

Blockchain technology has sparked significant interest and is currently being researched by academics and practitioners due to its potential to reduce transaction costs, improve the security of transactions, increase transparency, etc. However, there is still much doubt about its impact, and the technology is still in its infancy, with varying degrees of adoption among different financial institutions. Structural Equation Modeling (SEM) analysis was utilized to test the impact of blockchain and dynamic capabilities on the Bank’s Performance of top banks in Spain. The innovative approach seeks to understand how performance can be improved by deploying blockchain technology (BC) in banks. Results showed a significant association between banks’ adoption of blockchain and the generation of dynamic capabilities and financial performance. Thus, we can confirm that a bank adopting blockchain will more likely create dynamic capabilities than those that do not. Hence, blockchain technology is an important tool for achieving dynamic capabilities and increasing performance in banks. Based on the findings, we suggest areas for additional research and highlight policy considerations related to the wider adoption of blockchain technology. Full article

Artificial Intelligence is a disruptive technology that is revolutionizing the accounting sector, e.g., by reducing costs, detecting fraud, and generating reports. However, the manual maintenance of booking ledgers remains a significant challenge, particularly for small and medium-sized enterprises. The usage of AI technologies in this area is rarely considered in the literature depite a significant interest in using AI for other acounting-related activities. Our study, which was conducted during 2023–2024, utilizes natural language processing and machine learning to construct a predictive model that accurately matches bank transaction statements with accounting records. The study employs Feedforward Neural Networks and Support Vector Machines with various settings and compares their performance with that of previous models embedded in similar predictive tasks. Additionally, as a baseline model, a software called Contofox, a rule-based system capable of classifying accounting records by manually creating rules to match bank statements with accounting records, is used. Furthermore, this study evaluates the business value of the model through an interview with an accounting expert, highlighting the potential benefits of artifacts in enhancing accounting processes. Full article

This study investigates the factors that affect the adoption of mobile payment systems in Oman, focusing specifically on small and medium-sized enterprises (SMEs) within the expanding FinTech landscape. By utilizing secondary sources of data from the Central Bank of Oman and global FinTech reports, this research identifies essential enablers, such as security features and ease of use, which are propelled by developments in FinTech solutions. It also addresses the obstacles, such as high transaction fees and issues with authentication, that impede SMEs from embracing these technologies. Through an examination of worldwide FinTech adoption patterns, this research offers perspectives on Oman’s progress toward becoming a cashless society. This study employs sophisticated statistical techniques, including histograms and correlation analysis, to reveal significant trends in the rates of mobile payment adoption. The results emphasize the necessity for cooperative efforts among regulators, financial entities, and FinTech developers to minimize costs, strengthen digital infrastructure, and enhance user experiences. These findings are consistent with Oman’s Vision 2040, which aims to foster financial inclusion and propel the country’s shift toward a robust, digitally oriented economy powered by FinTech innovation. Full article

Despite the rapid advancements in payment technologies, cash continues to play a significant role in modern society. This phenomenon presents a unique area of analysis, particularly within metropolitan societies such as those in the Jakarta metropolitan area in Indonesia. The present study aimed to investigate cash usage in the Jakarta metropolitan area by analyzing two cases: (1) cash usage in physical stores, and (2) intention to continue to use cash in daily activities. To this end, two analytical techniques were employed: logistic regression and structural equation modeling (PLS-SEM). These techniques were implemented using data from 400 respondents residing in the Jakarta metropolitan area. The results of the study indicate a preference for cash over digital payments in transactions among a significant proportion of the respondents. The determinant analysis further identified several factors influencing cash usage in physical stores, including education, employment status, and the number of bank accounts. Furthermore, this study identified attitudes toward behavior, subjective norms, and satisfaction as variables affecting the intention to continue using cash in Indonesian society. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

Companies operating in the energy market in Poland conduct business activity on the basis of special regulations applicable to this type of entity. However, they are, like any other entrepreneur, exposed to the risk of delays in payments, non-payment, restructuring, or even bankruptcy of their contractor. Appropriate instruments should be used to mitigate these risks. There are many methods available today to deal with trading risks. However, they should be tailored to the individual needs of each entrepreneur based on an in-depth analysis of its contractors. This article analyzes the five largest companies selling electricity in Poland in terms of the risk of late payments in the period 2018–2023. It turned out that in the surveyed companies in the period 2018–2013, the amount of receivables was constantly increasing, and the average recovery term was longer than the average payment term in enterprises in general. The real impact of delayed payments on the profitability of the surveyed companies was also calculated. Then, the available methods of transaction risk mitigation (tangible collateral, personal collateral, form of paying, other legal, banking and insurance instruments) were analyzed and described, and whether and to what extent they are used in the surveyed companies. The conducted research also allowed the author to conclude that, unfortunately, despite the existence of many instruments, they are not used due to the costs and formalities associated with their acquisition. Full article

The COVID-19 pandemic has precipitated several challenges, prompting the Indonesian government to enact rules aimed at minimizing direct contact to mitigate the spread of COVID-19, which has also affected transactional activities. Transactions conducted using a digital wallet represent a technological advancement that facilitates a cashless society lifestyle. Bank Indonesia established the Quick Response Code Indonesian Standard (QRIS) as a QR Code standard for digital payments using Electronic Money-Based (EU) servers, electronic wallets, or Mobile Banking. This study aims to identify the elements that affect consumer willingness to convert from cash payments to the QRIS during the COVID-19 epidemic. This study collected data through an online survey, distributing a 17-item questionnaire to QRIS users, yielding 568 valid responses. This research used a modified version of the Push-Pull-Mooring theory and an adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, concentrating on consumers’ intentions to transition from cash payments to QRIS utilization. This study employed the Hybrid SEM-ANN methodology with the SmartPLS and IBM SPSS Statistics 27 applications for data analysis. This investigation had 11 hypotheses, of which 4 were accepted. The findings indicated that alternative attractiveness, trust, critical mass, and traditional payment habits significantly influenced the intention to transition from cash payments to QRIS payments during the COVID-19 pandemic. Full article

The implementation of Central Bank Digital Currencies (CBDCs) faces significant challenges in achieving the same level of anonymity and convenience in offline transactions as cash. This limitation imposes considerable constraints on the development and widespread adoption of CBDCs. Unlike cash, digital currencies, similar to other electronic payment methods, necessitate internet or other network connectivity to verify payment eligibility. This study proposes a secure offline payment model for CBDCs that operates independently of internet or network connections by utilizing a Trusted Platform Module (TPM) to enhance the security of digital currency transactions. Additionally, the monotonic counter, the basic component of the TPM, is integrated into this model to prevent double spending in a completely offline environment. Our research presents a protocol model that combines these easily implementable technologies to facilitate the efficient processing of transactions in CBDCs entirely offline. However, it is crucial to acknowledge the security implications associated with the TPMs and near-field communications upon which this protocol relies. Full article