Agentic AI for Secure Financial Data Processing: Real-Time Analytics, Cloud Migration, and Risk Mitigation in AWS-Based Architectures

This study explores the optimization of financial data workflows through the implementation of AI-enhanced cloud migration strategies. With the growing reliance on cloud infrastructure in financial institutions, the integration of artificial intelligence (AI) has emerged as a critical factor in improving data handling, reducing latency, and enhancing processing efficiency. This paper discusses the benefits of cloud migration and the application of AI in optimizing data workflows for financial systems, emphasizing cost reduction, scalability, and data security. We present a detailed methodology for implementing AI-driven workflows within cloud frameworks, backed by a review of recent literature on AI and cloud migration in the finance sector. Our findings suggest that AI-optimized cloud migration significantly improves financial data handling by reducing bottlenecks and enhancing real-time analytics. This manuscript provides a comprehensive approach to financial data workflow optimization, aiming to guide financial institutions in their cloud transformation journey.

The opening up of the market for equity market data raises the question of whether data will be sufficiently consolidated and of high enough quality post-MiFID, or whether it will become too fragmented, thereby hindering price transparency and the implementation of best execution policies. This paper outlines the market for financial market data, the provisions of MiFID, and the implementing measures regarding financial data and data consolidation. It also looks at the approaches taken by the Committee of European Securities Regulators (CESR), the UK's Financial Services Authority (FSA) and the US authorities. It concludes that markets should be capable of adapting, that additional licensing requirements, such as those proposed by the FSA, are, in fact, premature and act as a barrier to the single market, and that neither would a US-style monopoly consolidator be needed in this case.

This article explores how modern data lakehouse architecture transforms the management of SAP financial data. By unifying storage, enhancing processing, and reducing costs, lakehouses address the limitations of traditional data warehouses. This research outlines implementation strategies, benefits like real-time analytics, and governance frameworks, supported by a case study. It concludes that adopting a data lakehouse is vital for future-ready financial data systems.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

Background/Objectives: Mpox, a viral disease marked by distinctive skin lesions, has emerged as a global health concern, underscoring the need for scalable, accessible, and accurate diagnostic tools to strengthen public health responses. This study introduces ITMA’INN, an AI-driven healthcare system designed to detect Mpox from skin lesion images using advanced deep learning. Methods: The system integrates three key components: an AI model pipeline, a cross-platform mobile application, and a real-time public health dashboard. We leveraged transfer learning on publicly available datasets to evaluate pretrained deep learning models. Results: For binary classification (Mpox vs. non-Mpox), Vision Transformer, MobileViT, Transformer-in-Transformer, and VGG16 achieved peak performance, each with 97.8% accuracy and F1-score. For multiclass classification (Mpox, chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy skin), ResNetViT and ViT Hybrid models attained 92% accuracy (F1-scores: 92.24% and 92.19%, respectively). The lightweight MobileViT was deployed in a mobile app that enables users to analyze skin lesions, track symptoms, and locate nearby healthcare centers via GPS. Complementing this, the dashboard equips health authorities with real-time case monitoring, symptom trend analysis, and intervention guidance. Conclusions: By bridging AI diagnostics with mobile technology and real-time analytics, ITMA’INN advances responsive healthcare infrastructure in smart cities, contributing to the future of proactive public health management.

The exponential growth of global digital commerce and the increasing complexity of international data protection regulations have created unprecedented challenges for multinational enterprises managing cross-border data transfers. Traditional compliance frameworks, characterized by manual oversight and reactive monitoring systems, are proving inadequate in addressing the dynamic nature of contemporary data governance requirements. This research presents a comprehensive enterprise-wide artificial intelligence-driven compliance framework specifically designed for real-time cross-border data transfer risk mitigation in the evolving regulatory landscape of 2025. The proposed framework integrates advanced machine learning algorithms, automated risk assessment protocols, and intelligent policy enforcement mechanisms to ensure continuous compliance with diverse international data protection standards including the General Data Protection Regulation, California Consumer Privacy Act, Personal Data Protection Act, and emerging regulatory frameworks across multiple jurisdictions. The study employs a mixed-methods research approach, combining quantitative analysis of compliance metrics from Fortune 500 companies with qualitative assessments of regulatory challenges faced by multinational enterprises. Primary data collection involved structured interviews with 45 chief compliance officers, 38 data protection officers, and 52 information technology directors from organizations spanning North America, Europe, and Asia-Pacific regions. Secondary data analysis examined regulatory violation patterns, financial penalties, and operational disruptions associated with cross-border data transfer non-compliance incidents between 2020 and 2024. The research methodology incorporates comparative policy analysis across 28 countries, examining regulatory convergence and divergence patterns that impact enterprise compliance strategies. Key findings reveal that organizations implementing AI-driven compliance frameworks experience a 73% reduction in regulatory violation incidents and a 68% decrease in compliance-related operational costs compared to traditional manual compliance systems. The proposed framework demonstrates superior performance in real-time risk identification, automated policy adjustment, and predictive compliance analytics. Statistical analysis indicates that enterprises utilizing the AI-driven framework achieve 94% compliance accuracy rates across multiple jurisdictions simultaneously, compared to 67% accuracy rates observed in conventional compliance systems. The framework's adaptive learning mechanisms enable continuous improvement in risk prediction accuracy, with machine learning models achieving 89% precision in identifying potential compliance violations before they occur. The research contributes to the growing body of knowledge in international data governance by proposing a novel theoretical model that bridges artificial intelligence capabilities with regulatory compliance requirements. Practical implications include enhanced operational efficiency, reduced regulatory risk exposure, and improved stakeholder confidence in cross-border data handling practices. The framework's modular architecture enables scalable implementation across diverse organizational structures and regulatory environments. Future research directions include longitudinal studies examining the framework's long-term effectiveness and investigations into emerging regulatory challenges associated with quantum computing and advanced AI technologies. Keywords: Artificial Intelligence, Compliance Framework, Cross-Border Data Transfer, Risk Mitigation, Regulatory Technology, Data Governance, Machine Learning, International Data Protection, Enterprise Security, Automated Compliance.

AI technology is reshaping the paradigm of cross-border financial data flows within the digital trade framework, shifting from static transmission to dynamic collaboration and giving rise to threefold risks: data breaches, algorithmic abuse, and sovereignty conflicts. Traditional regulatory logic centered on territorial jurisdiction and post-incident accountability is becoming obsolete. Fragmented international rules lead to regulatory arbitrage and inefficient dispute resolution. A transformative pathway emerges, grounded in three theoretical pillars: sovereignty constraints, collaborative governance, and risk prevention. This approach replaces territorial sovereignty with algorithmic sovereignty, builds a multi-stakeholder collaborative governance network, and implements real-time circuit breaker-style risk prevention. The access system design centers on three core mechanisms: a dynamic data classification list anchored by the AI lifecycle, overcoming limitations of fixed attribute categorization; differentiated entity qualification reviews based on responsibility binding, clarifying the boundaries of rights and responsibilities for financial institutions, technology platforms, and overseas recipients; and a data purpose binding system using technical hardening, achieving “usable but invisible” financial data and automatic compliance. The implementation strategy involves: internally advancing regulatory technology empowerment and collaborative governance platform development; externally adopting a three-step approach—ASEAN pilot programs, Belt and Road expansion, and multilateral rule integration—to embed China's dynamic data classification into the global financial governance framework.

With the fast development of natural language processing (NLP), financial text data processing has gained much attention due to its huge potential business value. Deep learning model based on manual perception-based labeling is commonly used to illustrate implicit meanings behind financial text. However, such manual labeling is costly and subjective, and may not perform well due to its weak link with direct financial trading. This paper therefore proposes a novel learning model called Enhancement Learning (EL) on financial text data by using task-based labeling. Financial text often has its application task. The derived financial trading data from such task called task-based label is objective and links to certain characters of financial text. Compared to model trained only on manual labels, EL consists of two models trained by manual perception-based labels and derived task-based labels respectively. Then, the task-based model will be used as main model to produce initial judgment on text, with the perception-based model as a filter to drop cases which are different from common perception. This task-oriented perception-enhanced approach can improve the performance of financial text data due to its direct link with financial task and further verification from perception. This paper illustrates the proposed Enhancement Learning on financial text data by the case in stock return prediction. Numerical experiments show that EL performs better than both the perception-based model and the task-based model.

A security policy constitutes one of the major actors in the protection of communication networks. For this, and in order to manage the access grants in accordance with the security constraints, a security policy has to be validated before its deployment. Unfortunately, in the literature, there is no well established validation mechanisms ensuring the well founded of such security policies. This paper proposes a validation framework for security policies where: (1) executable specifications are used to build an 'Executable Security Policy', (2) a validation model is proposed to support the validation activity, and (3) a validation of the executable security policy is performed. The main contributions provided by this paper concerns the adaptation of some concepts and mechanisms traditionally used in software engineering for validation aims, such as specification, executable specification or reachability graph. All the definitions made in this paper have been proposed in accordance with the firewall case.

The rapid adoption of cloud-based enterprise systems has intensified the need for governance models that ensure security, scalability, compliance, and operational resilience. The paper presents a formal governance structure specifically designed to work with Oracle Cloud ERP aimed at supporting the complicated control needs of contemporary digital businesses. The framework integrates strategic oversight, operational control mechanisms, and technical security enforcement into a unified governance architecture aligned with business objectives. The offered model integrates the policy-based access control, segregation-of-duty, data protection, and automated compliance standards in the ERP processes. The framework reduces the number of overheads of manual governance by using cloud-native features like real-time analytics, workflow automation, and ongoing audit logging and enhances accountability and transparency. Scalability is facilitated by modular components of governance that can be scaled to organizational expansion, multi-entity frameworks as well as global regulatory requirements. The results of the evaluation show that risk mitigation, performance governance, and operational efficiency have shown improvements that can be measured after the implementation of the framework. Automation-based financial controls were found to enable organizations to note improved speed in resolving incidents, greater accuracy in detecting frauds, and less spending on operations. The framework was also found to be more ready in compliance by continuous monitoring and inbuilt reporting systems. The study brings on board an application-focused, cloud-oriented governance framework that allows companies to optimize the strategic worth of the Oracle Cloud ERP yet with a high level of security posture, regulatory compliance, and scalable digital business.

Treasury management plays a pivotal role in maintaining an organization’s financial stability, ensuring optimal liquidity, working capital efficiency, and comprehensive risk mitigation. As businesses navigate an era marked by digital transformation, integrating robust cybersecurity measures and leveraging real-time analytics has emerged as a strategic necessity. Cybersecurity fortifies treasury functions against escalating cyber threats, safeguarding sensitive financial data and operational integrity. Concurrently, real-time analytics enables treasury professionals to access actionable insights instantaneously, enhancing their ability to make data-driven decisions on liquidity, working capital, and risk exposures.

Financial information systems play a crucial role in managing and processing company financial data. However, manual data collection and processing often require time and are prone to errors. This study aims to optimize financial information systems by utilizing microcontrollers in the automation of data collection and processing. The method used involved the development of a microcontroller-based prototype system to automate real-time financial data monitoring and reporting. The results showed that the use of microcontrollers significantly improved the efficiency and accuracy of financial data processing, while reducing the time needed for reporting. In conclusion, a microcontroller-based system can be an effective solution to enhance the quality and speed of financial data processing in companies.

Open Radio Access Network (Open RAN) triggers a shift towards multi-vendor deployments with increased programmability, flexibility, and intelligence. Still, several critical questions remain unanswered, particularly regarding Open RAN's impact on power consumption. New interfaces and procedures, increased softwarization, real-time analytics, and optimization tasks introduce complexities whose effects on energy usage are currently unknown. Moreover, well-established power consumption models for RAN do not address the additional elements of Open RAN architecture, such as the RAN Intelligent Controller (RIC), the central element for near-real-time decision-making and network optimization. In this context, this paper presents the first evaluation of the power consumption of the RIC platform across diverse scenarios. Specifically, we address key questions about RIC's consumption by performing power measurements under varying workloads, assessing RIC's scalability, and the impact of different parameters on power consumption. Additionally, also for the first time, this work introduces a RIC power consumption model, which can be directly utilized to assess the system-level power consumption of RIC on different RAN site scenarios (macro, micro, and pico sites), with a mean absolute percentage error (MAPE) of only 0.54%. Interestingly we find that RIC's power consumption alone, even when operating only with its essential functionalities, may even exceed 14 kW while overseeing 300 sites, this is equivalent in terms of power to over 160 micro sites. In cases, RIC may even impose a power consumption overhead that exceeds 100% of the RAN power consumption. Additionally, when monitoring 100 heterogeneous network clusters (HetNets), this power may scale up to 60 kW, resulting in an overhead of over 30%. These conditions present significant challenges to the scalability of RIC and raise questions on the feasibility of achieving net energy savings through RIC under specific scenarios. Finally, we discuss and propose potential strategies for optimizing RIC energy efficiency in next-generation Open RAN systems.

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

Financial crime is now considered the biggest threat to the world economy. It is increasing rapidly, with huge losses every year. Traditional fraud detection methods have proven inadequate in the race, with fraudsters continuously adopting newer tactics to challenge the new technology. This review paper answers how Artificial Intelligence (AI) is transforming the dimension of financial crime prevention from a comprehensive analytical perspective. The paper will assess the rise of different AI methods to tackle dynamic fraud and discuss their strengths and weaknesses with real-world applications. Here, special attention has been given to evaluating the nature of the financial fraud data, the challenges that may arise during deployment and real-time analytics. In advance, this review paper has discussed some new technologies such as Explainable AI(XAI), federated training, and others, emphasizing their potential to improve fraud detection systems regarding accuracy, scalability, and transparency. By recognizing existing gaps and providing future direction, this study aims to contribute to developing a more accurate, transparent, and efficient fraud detection framework in the financial sector.