Search Results (76)

Background/Objectives: Privacy preservation in big data environments is an NP-hard optimization task that requires the satisfaction of k-anonymity and l-diversity constraints to ensure data utility. Methods: This study proposes a novel hybrid optimization approach, adaptive hybrid FOX–RUN Intensive Mutation (PA-FRIM), to address the privacy–utility trade-off in anonymization process. The proposed approach integrates FOX-based global exploration with RUN-based local search using a hybrid adaptive control strategy and intensive mutation search to improve solution diversity in highly constrained solution spaces. Results: The experimental study on the Adult and Bank Marketing datasets shows that PA-FRIM exhibits stable convergence behavior compared to competing methods. The results indicate that full privacy is achieved on the Adult dataset with a violation value of 0.00, and information loss is minimized with an NIL measure of 0.5686. From the analytical utility perspective, PA-FRIM ensures data usability, even in the constrained region, achieving classification accuracies of 89.61% on the Bank Marketing dataset and 84.90% on the Adult dataset. Conclusions: By using a multi-metric evaluation strategy, PA-FRIM provides a robust optimization framework that eliminates privacy violations while maintaining high analytical performance. Full article

Prices and quantities in agricultural commodity and food product markets are subject to constant changes due to evolving supply and demand conditions. Big and sudden shifts in supply or demand may lead to price movements that bring negative consequences for food producers or consumers. Factors causing such movements can be of different natures, but substantial changes in the world energy price levels are supposed to be one of the most important. The purpose of the study was to investigate the effect of global energy price shocks on the evolution of food commodities and food consumer prices. Using the World Bank data on the respective price indices, we looked for shocks in these data series by utilizing statistical tools. Having identified three global energy price shocks in the period 2000–2024 induced by the financial crisis of 2008, the COVID-19 pandemic, and the outbreak of war in Ukraine, their influence on the world agricultural commodity prices and food consumer prices was assessed. It was found that the series of energy, food commodity, and food consumer price indices were related in the long term. Also, the occurrence of global energy price shocks to a visible extent translated into global food commodity and food consumer price shocks. Applying various statistical and econometric techniques, including Chow tests and MS-VAR modelling, enables the identification of which breaking points led to regime changes between the analysed variables. The most sensitive to the structural breaking points appeared to be the relation between energy and consumer food prices. This discovery can be considered our major contribution. Full article

Open banking reshapes the financial sector by enabling regulated third-party providers to access bank data through APIs, fostering innovation but amplifying operational and financial-crime risks due to increased ecosystem interdependence. To address these challenges, this study proposes an integrated risk-management framework combining System Dynamics, Agent-Based Modelling, and Monte Carlo simulation. This hybrid approach captures feedback effects, heterogeneous agent behaviour, and loss uncertainty within a simulated PSD2-style environment. Simulation experiments, particularly those modelling credential-stuffing waves, demonstrate that stricter onboarding thresholds, tighter API rate limits, and enhanced anomaly detection reduce operational tail losses by approximately 20–30% relative to baseline scenarios. Beyond these specific findings, the proposed framework exhibits significant universality; its modular design facilitates adaptation to broader contexts, including cross-border regulatory variations or emerging BigTech interactions. Ultimately, this multi-method approach translates complex open-banking dynamics into actionable risk metrics, providing a robust basis for targeted resource allocation and supervisory stress testing in evolving financial ecosystems. Full article

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies. Full article

The digital revolution is transforming the financial services sector. Risk is not static; emerging risks continue to pose threats to the financial services sector which influences financial stability and consumer protection regulation mandates. This novel study presents a comparative bibliometric analysis of China and India in examining the effect of trends on the scholarly research outputs discussing the emerging risks in the fintech-driven digital banking environment. Furthermore, the mapping presents the geographical dynamics of Asia, followed by country-level perspectives. The period of study was from 2015 to 2024. Leveraging the Scopus database, data was extracted based on a specified query using the SPAR 4 SLR protocol. Analysis was performed on 162 articles from an initial list of 1257 articles using Scival and Vos viewer tools. Performance indicator metrics and science mapping enabled the answering of research questions. The findings revealed that research output is inclined towards India rather than China; this is despite China domiciling some big tech firms. Comparatively, India dominates when it comes to performance analysis metrics compared to China. The scientific mapping depicted in both countries shows the multifaceted effects of fintech on banking, including trends in user acceptance, competition, emerging risks, technological innovation, and financial stability. The strong connections in both countries across clusters highlight how fintech research is multi-disciplinary, spanning consumer behavior, finance, economics, and financial technology. This study provides a foundation on which a robust risk management framework, which is customized to digital banking existence, can be developed in the face of emerging risks. Full article

Managing and analyzing data in data lakes for big data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The increasing need to process sensitive data emphasizes the relevance of secure-by-design approaches that integrate encryption techniques and governance frameworks to protect personal and confidential information. This study proposes a protocol that combines the capabilities of Databricks and format-preserving encryption to improve data security and accessibility in data lakes without compromising usability or structure. The protocol was developed using a design science methodology, incorporating findings from a systematic literature review and validated through expert feedback and proof-of-concept experiments in banking environments. The proposed solution integrates multiple layers, data ingestion, persistence, access, and consumption, leveraging the processing capabilities of Databricks and format-preserving encryption to enable secure data management and governance. Validation results indicate the protocol is effectiveness in protecting sensitive data, with promising applicability in regulated industries. This work contributes to addressing key challenges in big data security and lays the groundwork for future developments in data governance and encryption techniques. Full article

The comprehensive integration of modern technologies, such as artificial intelligence and big data, into the financial sector in recent years has profoundly transformed the operating model of the traditional financial industry. These technologies not only redefine the operating mechanisms of the financial industry but also significantly reshape the competitive landscape and strategic development of commercial banks. To investigate the impact of FinTech on the overall profitability of commercial banks, this study utilizes a balanced panel dataset comprising 50 listed commercial banks in China from 2012 to 2023 and conducts an empirical analysis based on a fixed-effects model. The findings reveal that, from a dynamic perspective, there exists a significant U-shaped relationship between FinTech and the comprehensive profitability of commercial banks, with a development threshold of 2.86. When the level of FinTech development falls below this critical threshold, its impact on the profitability of commercial banks is predominantly negative. However, once FinTech development surpasses this threshold, its positive effects on enhancing the profitability of commercial banks gradually emerge. Therefore, the government should provide phased policy support to achieve both short-term burden reduction and long-term innovation, and commercial banks should adopt FinTech development as a long-term strategic priority. Full article

In the context of fintech’s transformative impact on banking, many commercial banks blindly adopt emerging technologies, resulting in systemic inefficiencies. To address this, we propose a complex systems-based competitiveness evaluation framework guiding strategic fintech adjustments. First, the Delphi method identifies five fintech subsystems: big data, artificial intelligence, cloud computing, the Internet of Things, and blockchain. Next, an ANP model captures nonlinear dependencies and feedback loops among these subsystems, overcoming the limitations of linear hierarchical methods. Empirical validation using 16 listed Chinese banks demonstrates the model’s systemic coherence and practical utility. This study contributes to systems science by integrating complex adaptive systems (CAS) theory into competitiveness evaluation, shifting from firm-centric governance to ecosystem dynamics. It also offers banks a tool for adaptive strategy iteration in evolving fintech ecosystems. Full article

With the development of artificial intelligence technology, there are increasingly high requirements for processing big data systems. Big data systems have undergone rapid evolution in response to changing demands. Due to the complex structural connections and dispersed component positions of big data processing systems, traditional formal methods find it difficult to dynamically model their structure and position simultaneously. To address this issue, this study proposes a formal modeling framework that extends Bigraph to support the dynamic evolution of big data software architecture. This model is capable of verifying the symmetry consistency of structural connections and component positions in evolutionary systems and evaluating them through real-life case studies of banking big data systems. The results confirmed its correctness and practical feasibility. Full article

The swift expansion of financial technology (FinTech) can substantially improve financial inclusion, especially in the rural regions of emerging nations such as India. FinTech has the potential to drive inclusive growth, reduce inequalities, and foster sustainable economic development. This research examines the determinants affecting the adoption of FinTech services in rural India by synthesizing three theoretical frameworks: The Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Technology Readiness Index (TRI). A mixed methods approach that combines partial least squares structured equation modeling (PLS-SEM) and fuzzy set comparative qualitative analysis (fsQCA) was used to evaluate the suggested framework. The integrated PLS-SEM and fsQCA offer a comprehensive, elegant, and resilient method for data analysis. While fsQCA addresses more intricate patterns within the data, PLS-SEM effectively identifies the relationships among significant factors. This makes the mixed method approach more judicious and advantageous than the single method approach. The findings showed that attitude (β = 0.35), perceived behavioral control (β = 0.28) from the Theory of Planned Behavior (TPB), perceived ease of use (β = 0.31) from the Technology Acceptance Model (TAM), and perceived insecurity (β = −0.19) from the Technology Readiness Index (TRI) all have a big impact on how people use FinTech. The findings also indicate that the desire to adopt FinTech positively influences financial inclusion among rural residents. These research findings enhance the debate on sustainable development by demonstrating how specific FinTech interventions can close the financial inclusion gap, empower rural populations, and achieve various Sustainable Development Goals (SDGs). The study’s findings could help governments, banks, and FinTech firms aiming to enhance the accessibility and use of digital financial services in rural India. Full article

Waterfronts are some of the most well known public spaces that can catalyse urban changes, yet their benefits have not been systematically explored. This study investigates the potential benefits of waterfront regeneration for the subsequent development of the wider surrounding areas and whether these benefits encompass a broader range of influences. Taking an extensive linear catalyst, the Huangpu River waterfronts in Shanghai, as an example, the catalytic effect of each waterfront section was investigated, visualised, compared and discussed within and across different sections and catalytic influential aspects. A multi-method approach driven by multi-sourced big data was used in this study, and the analysis was carried out at two scales: the waterfront area (the catalyst area) and its surroundings of influence (the areas affected by the catalyst area). The research findings suggest that the landscape catalytic effect is more pronounced in the catalyst area itself than in the surrounding areas affected by the catalyst area. Such effects also vary across waterfront sections, and the western bank of the Huangpu River was more obviously influenced than the eastern bank. The possible reasons for these differences may be related to the area’s original function, development limitations and available resources. This study also provides evidence indicating that the relationship between the catalyst and the spatial, social and economic aspects of changes it induces is one of ongoing and mutually supportive interaction. The outcomes of this study include a framework composed of 14 indicators that can disclose the depth and progress of a catalytic effect facilitated by the landscape, as well as implications for the decision-making process in the urban regeneration agenda that centres around waterfronts. Full article

The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and a BiLSTM neural network to deliver high-accuracy transaction analysis. BankNet achieves exceptional predictive performance, with a Root Mean Squared Error of 0.0159 and fraud detection accuracy of 98.5%, while efficiently handling data rates up to 1000 Mbps with minimal latency. By addressing critical challenges in fraud detection and operational efficiency, BankNet establishes itself as a robust decision support system for modern Internet banking. Its scalability and precision make it a transformative tool for enhancing security and trust in financial services. Full article

The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI in finance has grown significantly, which shows the evolution and importance of this area of research. Eight main topics were obtained in this area: bank, prediction, impact, decision, valuesstock, genetic algorithm, big data analysis, and social data analysis. This study shows us how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements. Full article

Fraud detection within transaction data is crucial for maintaining financial security, especially in the era of big data. This paper introduces a novel fraud detection method that utilizes quantum computing to implement community detection in transaction networks. We model transaction data as an undirected graph, where nodes represent accounts and edges indicate transactions between them. A modularity function is defined to measure the community structure of the graph. By optimizing this function through the Quadratic Unconstrained Binary Optimization (QUBO) model, we identify the optimal community structure, which is then used to assess the fraud risk within each community. Using a Coherent Ising Machine (CIM) to solve the QUBO model, we successfully divide 308 nodes into four communities. We find that the CIM computes faster than the classical Louvain and simulated annealing (SA) algorithms. Moreover, the CIM achieves better community structure than Louvain and SA as quantified by the modularity function. The structure also unambiguously identifies a high-risk community, which contains almost 70% of all the fraudulent accounts, demonstrating the practical utility of the method for banks’ anti-fraud business. Full article

The war in Ukraine and the related disruptions in its supply chains shook global markets for agricultural and energy commodities, causing their prices to increase to unprecedented levels. At the same time, this situation highlighted the fact that Ukraine is an important global producer and exporter of certain agricultural products. The complete opening of the EU market to duty-free imports from Ukraine showed that Ukrainian products constitute competition for both EU and Polish food producers. This, in turn, caused further disruptions in the food supply chains within the EU. The aim of this article is to assess the competitive position of Polish and Ukrainian food producers in the EU market and the prospects for the evolution of their competitive advantages. The analysis was carried out using selected quantitative indicators of competitive position, namely Balassa’s Revealed Comparative Advantage Index (RCA) and the Trade Coverage Index (TC). The calculations were made using statistical data from the World Bank WITS-Comtrade database. The research covered the period from 2018 to 2023, inclusive. The research shows that between 2018 and 2023, the share of products in Polish exports to the EU, in which both countries compete, increased to 37.5%; that is, both countries had comparative advantages in these products on this market. The current competition includes, among others, poultry meat, bakery products, wafers and cookies, chocolate, corn, fruit juices, frozen fruit, water and other non-alcoholic drinks, and wheat. At the same time, more than half of Polish exports consisted of products that may become the subject of such competition in the future (currently, only Poland has comparative advantages in the export of these products). These may include, among others, cigarettes, animal feed, fresh or chilled beef, other food products, smoked fish, canned meat, fish fillets, pork, canned fish, and liquid milk and cream. Therefore, Polish food producers face big challenges; the process of the post-war reconstruction of Ukraine and its potential integration with the single European market will strengthen the competitive position of Ukrainian food producers in the EU market. The current competitive strategy of Polish producers, based on cost and price advantages, may turn out to be ineffective under these conditions. Therefore, they must look for new sources of competitive advantage that will distinguish Polish products from the cheaper Ukrainian ones. Therefore, a strategy of competing on quality may prove effective. Full article