The article presents a theoretical and applied analysis of the methodological foundations for merging structured and unstructured data sources within machine learning systems. The study is based on an interdisciplinary approach that integrates architectural design of ML pipelines, data representation theory, and practices of heterogeneous format integration. Particular attention is paid to the analysis of recent scientific publications highlighting the application of Retrieve–Merge–Predict architectures, agent-based discovery systems, and multimodal frameworks involving large language models. Four stable strategies for data merging are identified, ranging from static unification to end-to-end processing within a unified training loop. The importance of selecting matching metrics and adaptation schemes when dealing with unstable data streams is demonstrated using experiments from Cappuzzo and Eltabakh. Special emphasis is placed on the methodological limitations of universal solutions, including the generalization paradox, sensitivity to structural evolution, and the lack of formalized testing scenarios in agent-oriented pipelines. It is shown that sustainable development of ML architectures requires a shift from linear ETL pipelines to coherent, iterative systems with internal adaptation and feedback from the model to the data. The article will be of interest to researchers in data preparation automation, developers of multimodal ML systems, data engineering specialists, and digital platform architects working with multi-format sources.

This paper presents the design, simulation, and characterization of a dual-band microstrip patch antenna intended for sub-6 GHz wireless communication applications. The proposed antenna incorporates a rectangular patch with optimized slots and a partial ground plane to achieve dual-band operation while maintaining a compact form factor. Detailed parametric studies were conducted to fine-tune the resonant frequencies and impedance bandwidths. The antenna was fabricated on an FR4 substrate, and its performance was evaluated using both simulation and experimental measurements. Results demonstrate that the antenna operates effectively across two frequency bands—centered at 3.5 GHz and 5.2 GHz—with satisfactory return loss, radiation patterns, and gain characteristics suitable for modern wireless systems such as 5G and WLAN. The findings highlight the potential of the proposed design for integration into portable and low-profile wireless communication devices.

Artificial intelligence (AI)-based predictive analytics has also become a revolutionary approach to contemporary financial planning, as it allows organizations to harness the power of large volumes of data to make accurate predictions, risk management and strategic planning. This article reviews how the element of predictive analytics is being incorporated into corporate financial systems and their potential to contribute to more accurate decisions, more effective utilization of resources and creating sustainable competitive advantages. With a quantitative comparative methodology, the authors review secondary data published in industry reports, financial databases, and peer-reviewed case studies, extending the discussion to use cases in bank, asset management, and corporate treasury activities. Evidence suggests that companies that implement AI-driven predictive analytics enjoy an average improvement in forecasting accuracy, an average 15-20 percent reduction in operational expenses, and a 10-15 percent increase in ROI within the first two years of use. The paper introduces a dual-frame model: the algorithmic basis and strategic refractions of AI implementation into financial workflows and a KPI-based ROI measurement framework based on practical performance experience. The innovation is in the combination of technical information on AI modeling with applied strategic financial performance, which addresses a gap noted in previous literature where implementation has not always been explained in terms of concrete business performance. This research makes a contribution to the financial technology literature by linking predictive analytics outcomes to executive-level decision pipelines, giving the research a dual theoretical and practical value to CFOs, risk managers, and policymakers. The paper closes with findings and recommendations on how to implement effective governance, ethical compliance and capability development strategies to ensure long term value contribution through adoption of AI in the context of an increasingly data-driven economy.

This article explores the integration of artificial intelligence (AI) technologies into information security systems, aiming to enhance the effectiveness of threat detection and response. The research is grounded in a comprehensive review of existing literature. It examines AI’s capabilities in processing large volumes of data, forecasting potential threats, and automating their identification. The discussion also addresses associated vulnerabilities, including issues related to the quality of training datasets, susceptibility to adversarial attacks, and algorithmic bias. Special attention is given to the development of technological solutions designed to protect AI-driven systems themselves. These include data encryption, access control mechanisms, anomaly detection systems, behavioral analytics, and architectural strategies such as multi-layered defense and containerization. The findings presented are relevant to cybersecurity researchers and practitioners, machine learning specialists, and developers of intelligent systems engaged in building interdisciplinary approaches to threat analysis, prediction, and mitigation in today’s complex digital environments. This material is also of value to professionals seeking to bridge theoretical frameworks with practical implementation in the context of rapidly evolving digital ecosystems and critical infrastructure.

The new aspect of integrating blockchain technology in business finance has turned out to be a game-changer in solving challenges that have long plagued transparency, business operations efficiency, and stakeholder trust. This paper presents the measurable effect of blockchain integration inside financial J-managers by integrating the latest empirical research findings of multinational corporations, financial organizations, and fintech developers. Multimethod, with a mix of quantitative and analytical elements, will be used to evaluate the improvement in performance in terms of transaction processing time, cost per transaction, fraud incidence rates, audit cycle duration, and trust perception index. High-quality data obtained in verified industry reports and peer-reviewed research indicate that the use of blockchain can reduce settlement time by up to 60 percent, transaction costs by an average of 30-50 percent, and increase the rate of fraud detection by about 50 percent in comparison to legacy systems. Further, stakeholder trust rates, which are independently calculated in a survey improve significantly after a blockchain adoption due to the nature of the immutable verifiable ledger. What makes this research novel is its holistic criticism of operational efficiency and trust indicators in a single analytical framework, which fills in the gap in the literature that tends to discuss these aspects independently. The results indicate that blockchain can be used to improve financial process performance, strengthen compliance by ensuring auditability, and in general, increase the level of trust between counterparties. The final recommendation section provides several suggestions on what policymakers, regulators, and financial leaders can do to make sure that they are getting the most out of blockchain and that they are also managing to overcome any barriers that it may face during its adoption, including regulatory uncertainty, interoperability issues and scalability problems. This study gives a blueprint of how a blockchain can be implemented within business finance to produce real and sustainable enhances in the transparency-efficiency-trust triangle.

Financial statement fraud is a persistent challenge that undermines investor trust, corporate governance, and financial market stability. Traditional auditing approaches often fail to capture subtle manipulations within complex financial data, highlighting the need for advanced computational methods. In this study, we investigate the effectiveness of machine learning models in detecting fraudulent financial reporting. Using a publicly available dataset, we applied rigorous preprocessing, feature selection, and feature extraction techniques before evaluating five models: Logistic Regression, Support Vector Machines, Random Forest, Gradient Boosting Machines, and Deep Neural Networks. The results indicate that Gradient Boosting Machines achieved the best overall performance, with an accuracy of 94%, precision of 91%, recall of 88%, and an AUC-ROC score of 0.96. Random Forest also demonstrated strong performance, particularly in balancing recall and F1-score. These findings suggest that ensemble-based models are highly effective for identifying complex fraud patterns in financial statements. The study provides empirical evidence supporting the integration of machine learning into auditing and financial risk management systems, offering a scalable and reliable approach to strengthen fraud detection practices.

The increasing pace and complexity of cyberattacks have made cybersecurity a more than technical aspect, even to the extent that cybersecurity is now a part of corporate financial strategy. This paper will address the relationship between business resilience and cyber risk exposure with specific reference to investment decision making in IT security, on quantifiable financial outcome. Using a mixed approach, the study combines secondary quantitative data--based on validated industry reports, stock market response studies, and corporate financial disclosures with qualitative analysis of resilience strategies across a variety of sectors. The return on investment (ROI) of proactive security spending is evaluated by regression modeling and scenarios of financial simulation of direct (e.g., breach response, legal liabilities) and indirect costs (e.g., reputational damage, market valuation decline). The analysis shows that companies that invest a greater proportion of their annual revenues in cybersecurity experience statistically significant decreases in the financial losses they incur in the event of a breach, and faster time to resume normal operations, which again translates into greater investor confidence in the company and a higher credit rating. In addition, cyber resilience can be incorporated into enterprise risk management frameworks to help organizations ensure greater alignment of capital allocation to longer-term value creation. The uniqueness of the study is that it helps connect the gap between cyber risk modeling and corporate finance because the study presents the issue of cybersecurity as a strategic asset and not a discretionary cost. These findings offer practical recommendations to Chief Financial Officers (CFOs), Chief Information Security Officers (CISOs), policymakers, and investors, specifically, the need to co-locate IT security spending with overall business resilience and financial management planning.

Cloud computing has rapidly established itself as the prevailing model for enterprise IT, with major providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) leading global adoption. The cloud promises scalability, flexibility, and cost efficiency, but it also creates complex governance, risk, and compliance challenges due to shared infrastructure, multi-tenancy, and interdependent service layers. To guide assurance efforts, ISACA has issued dedicated audit frameworks: the AWS Audit Program (2019), the Azure Audit Program (2020), the GCP Audit Program (2023), and a broader Cloud Computing Audit Program (2016). These programs structure risk assessment and testing across domains such as governance, identity and access management, incident response, configuration management, logging, and business continuity.
To integrate these audit practices with enterprise-level governance, the study employs the COBIT 2019 framework, ISACA’s globally recognized model for governing and managing information and technology. COBIT 2019 provides structured objectives and processes across governance, planning, implementation, service delivery, and monitoring that link IT controls directly to business goals, risk optimization, and value delivery.
This study undertakes a comparative review of the cloud audit programs, aligning their focus areas with COBIT 2019’s governance and management objectives. The findings highlight distinct emphases: AWS concentrates on configuration and misconfiguration risks, Azure underscores continuity, shared responsibility, and service reliability, GCP emphasizes hierarchical structure, identity, and permission inheritance, and the general cloud computing program provides a broad governance foundation applicable across providers. Comparative analysis shows Azure exhibits the closest alignment with COBIT 2019, while AWS and GCP reveal gaps in governance integration. To address these gaps, the study proposes harmonization strategies involving cyber-risk quantification, structured risk registers, and continuous auditing. By linking technical audit domains to COBIT 2019’s governance objectives, the study reframes cloud audits from static, checklist-based exercises into dynamic governance mechanisms that foster compliance, risk optimization, and digital trust.

The article discusses the development and justification of a Zero-Touch framework for multi-level authentication, which provides a dynamic balance between user convenience and security reliability when performing operations of varying risk levels. The relevance of the study is determined by the need to minimize user friction without reducing the level of protection, which requires new models of adaptive authentication. The paper aims to develop and methodologically substantiate a Zero-Touch framework that automatically strengthens authentication checks only when risk increases, relying on session context (behavioral, network, and hardware parameters) and the regulatory requirements of NIST SP 800-63B, PSD2, and GDPR. This approach eliminates unnecessary steps for low-risk operations and ensures a reliable escalation process for critical actions. The novelty of the proposed approach lies in the integration of four asynchronous layers (risk assessment engine, Policy Decision Point, user journey orchestrator, and log analytics) with a three-level risk gradation, aligned with AAL1–AAL3. The innovative architecture ensures a seamless user experience, invisible blocking of suspicious requests, and selective strengthening of factors for only a fraction of operations, which fundamentally differs from the static schemes of traditional MFA. Results of piloting the Zero-Touch framework were a jump in authentication accuracy to 86% with only 12% false positives, a System Usability Scale rating well above 80 points, plus five percentage points added to critical transaction conversion, and reduction of incident response time to minutes while maintaining validation delays at 5–7 seconds even when it has to be escalated. This article is intended for researchers and developers of information security systems, digital service architects, and compliance specialists.

This paper discusses the use of a microservice architecture with Spring Boot and Spring Cloud for scaling banking applications. It aims to identify the constraints of conventional monolithic banking platforms and then design architectural solutions with Spring Boot that offer modularity, separate scaling capabilities, and ease of service testing. The motivation for such a study lies in explaining how transaction volumes are growing very fast in the global payments industry and further increases are forecasted through instant transfers, together with highly stringent regulatory requirements under PSD2, GDPR, and Basel III Besides this, downtime in banking systems is highly costly-with the possibility of running into several thousand dollars per minute. The novelty of the research lies in the comprehensive combination of five key Spring Cloud components—Service Discovery, API Gateway, Config Server, Circuit Breaker and Observability—with the Database per Service pattern, the saga pattern and two‑phase commit, CQRS and Event Sourcing; this approach ensures high fault tolerance, regulatory compliance and predictable horizontal scaling under peak loads. The main conclusions indicate that the proposed microservice framework reduces downtime to industry-minimum levels, simplifies adaptation to changing regulatory requirements, guarantees data integrity and flexibility, and that built-in security mechanisms and Zero-Trust models secure personal and critical operational data. This article will be helpful to IT architects and developers in banking organizations and fintech companies who are engaged in designing and implementing scalable, fault-tolerant microservice systems.

The advent of digital transformation, facilitated by state-of-the-art IT systems, is changing the strategic paradigm of corporate finance by empowering organizations to attain increased efficiency, transparency, and nimbleness in the financial decision-making process. This article discusses how IT can serve as a strategic tool in generating quantifiable business value in the context of corporate finance departments, in particular its impact as a driver of capital allocation and risk management, forecast accuracy and value creation. The research employs a quantitative comparative study design synthesizing the secondary data found in the financial databases, industry benchmarks, and peer-reviewed case studies, and applications in the corporate treasury, capital budgeting, and financial reporting. Increased financial forecasting precision, operational cost reductions, and ROIC growth by 25-35%, 15-20%, and 12-18 percent, respectively, are indicators of success demonstrated by empirical evidence in the first three years of adoption by the organizations that implement IT-enabled digital transformation initiatives in the financial realm. The analysis also notes that integrated enterprise system, advanced analytics platform, and automation technologies can be used to increase working capital efficiency, ensure better regulatory compliance, and enhance stakeholder trust. The novelty in this research is entered by providing the linkage between how new technology may be adopted and how quantifiable corporate finance performance results are achieved. The research has developed a performance measurement framework that ties IT investment to the tangible and intangible business value. By integrating technical knowledge of IT systems with the strategic mindsets of finance, the study will serve the research domain of digital transformation with a feasible guide that the CFOs, finance strategists, and policymakers can consider. The paper ends with practical suggestions on governance, capability building and performance monitoring so that the digital transformation of finance can deliver sustained competitive advantage in an ever more technology-based economy.

Background: In the containerized architecture on Fargate design, business logic resides within the API repository, and an infrastructure repository contains a description of the infrastructure. Since startups must iterate rapidly and deploy new versions frequently, a fast and reliable CI/CD pipeline is critical, regardless of the chosen container platform. The regular solutions are either expensive and slow ones (such as Terraform Cloud, Atlantis, or Spacelift), or even if you have a self-hosted plan, or even with a self‑hosted Terraform pipeline, running a full plan/apply for every deployment is slow and adds unacceptable latency to releases in an MVP or startup context between the Terraform state and the actual cloud configuration.
Methods: A typical process utilizing the track_latest property, which was added in February 2024 — specifically, version 5.37.0 of the Terraform AWS Provider. Concurrently, the Terraform configuration invokes data.aws_ecs_container_definition with track_latest = true, so that a subsequent Terraform plan compares not with the ARN stored in the state file, but with the latest revision in the cloud.
Results: Across a sample of 50 releases, the complete cycle was reduced from 9.6 ± 1.1 minutes to 1.9 ± 0.2 minutes—an approximately 80 % acceleration. Once track_latest was enabled, all subsequent Terraform plan executions in the three environments completed with no changes. Infrastructure is up‑to‑date, eliminating drift.
Conclusions: Enabling the track_latest attribute in the Terraform AWS Provider enables a lightweight, secure, and deployment of ECS services without the need for external CI tools or workaround scripts. A single configuration parameter supplants expensive and complex solutions, preserving Terraform’s declarative paradigm and automatically preserving Terraform’s declarative paradigm and preventing drift—Terraform plan compares against the live revision. At the same time, the state file itself retains the prior ARN. The method’s limitations are the requirement for a provider version ≥ v5.37.0 and for tracking environment variable changes made outside of Terraform.

This paper explores how FinTech innovations and IT infrastructure can support financial inclusion and improve digital payment systems. Using recent global data - such as India Unified Payments Interface (UPI) which recorded 19.47billion transactions worth 25.08trillion (~USD 293 billion) in July 2025 and the World Bank Findex metrics which show account ownership in developing economies increased in 2022 (71%) compared to 2011 (42%). This paper uses a data-driven mixed-methods design. Quantitative analysis makes use of cross-country time-series data by regressing IT infrastructure readiness (including mobile broadband access indicators, digital public infrastructure measures) on digital payment adoption and financial inclusion indicators. The qualitative insights are based on the case studies of India Stack (including UPI) in India and Africa, which is fast growing in its digital payment ecosystem. Based on key findings, there was a statistically significant positive correlation between the quality of IT infrastructure and the magnitude of digital payment adoption- particularly in ecosystems with digital public infrastructures such as the UPI. This synergy has been able to catalyze quantifiable increases in account penetration and transaction volumes especially to underserved populations. The originality of the study is that all of the technological infrastructure, empirical modeling, and business ramifications were combined in order to shed light on the ways of sustainable financial inclusion. Potential implications of the findings to FinTech firms include strategic application of platform-based models to operate with robust infrastructure, whereas, suggestions to policy-makers include investment in open and interoperable systems and the promotion of regulatory frameworks to support scalable and inclusive digital financial ecosystems.

Under conditions of high price volatility in commodity markets, tightening environmental regulation, and chronic overcapacity, enterprises of the mining and metallurgical complex face the imperative not of incremental but of structural shifts in operational efficiency. In response to this challenge, the paper develops and theoretically substantiates an integrative framework that combines project management (PM) with lean manufacturing practices as complementary mechanisms of organizational development. The central proposition of the study is that sustainable operational excellence is achieved not by isolated Lean initiatives, but by their systematization and disciplined implementation within the contour of mature project procedures: from portfolio prioritization and stage-gate management to benefits control and the replication of best practices. Methodologically, the work draws on a systematic analysis of academic and industry literature, content analysis of managerial documentation, and case studies. In conclusion, it is argued that the synergy of PM and Lean, reinforced by digital transformation (sensors and MES/APS, analytics and predictive models, process mining), moves the enterprise from point improvements to systemic, reproducible changes in the operating model, with a direct effect on financial results and business resilience. The materials contained in the study will be of interest to senior executives and operations directors of the mining and metallurgical complex, as well as to researchers in industrial engineering and strategic management.

This paper provides a systematic approach to planning and operating multisite network environments in the face of economic, natural, and cyber crises. The goal of the study is to identify methodological approaches and technological solutions that ensure maintaining business processes via a geo‑distributed network and synthesize them. This includes RTO and RPO requirements and topologies (active–active, active–passive, geo‑distributed clusters) definition as well as a hybrid‑cloud scenario economic feasibility assessment. The relevance of this work is grounded in the increasing sensitivity of the digital economy to downtime, rising cyber risks, and socio-economic shocks (such as migration waves and climate catastrophes), which demand strategic responses to multidimensional threats based on distributed infrastructure and international standards. The novelty of the research lies in a comprehensive content analysis of 18 sources—from reports by Uptime Intelligence, IBM, and UNHCR to the ISO 22301, DORA, NIST SP 800‑34, and ISO/IEC 27001 regulations. A cascading approach to calculating RTO/RPO involves criticality classification of services, dependency modeling, regular drill-over tests, and the development of financial justifications for CAPEX and OPEX flows under TCO and risk bonus. Key findings show that with multisite architecture plus a hybrid cloud, resilience to failures is ensured by automatic failover, regular DRP/BCP testing in operation monitoring MTTR/MTBF continuous error‑budget management; international standards integration resulting in exhaustive checklists for Business Impact Analysis and verification of target metrics; SOAR automation application plus predictive maintenance which mitigates staff shortage problem speeding up recovery; network segmentation and multilayer encryption inside Zero‑Trust framework. This article will be of use to IT infrastructure managers, network system architects, and specialists in business continuity and information security.