Analytical Integration Research Articles

Risk Management and Software Solutions in Financial Institutions: Move Towards Effective Implementation

Technological advancements and intensified regulatory pressures have led to a new level of the evolution of risk management in financial institutions. The entities have exposure to different categories of risks, such as credit risks, market risks, operational risks, and cybersecurity risks. The growing intensity of these factors makes proper risk management crucial for the stabilization and success of such enterprises. This report analyzes the best practices and software applications adopted by financial institutions for effective risk management. Improved risk identification, assessment, mitigation, and reporting within the organization would be through the integration of advanced analytics, AI, ML, and cloud computing. Based on this, the current paper assesses the part that these technologies play in contributing to compliance, cost savings, and better decision-making skills.

Governmentality in construction claim management: Role of smart data initiatives

Despite efforts to identify and prevent causes, construction claims remain inevitable and repetitive. Therefore, focus has shifted to managing claims with systematic processes, yet challenges related to claim preparation persist. Drawing from political governance, where data analytics promote self-governance to overcome similar challenges, this study contrasts politics with construction claims, aiming to investigate potential for adopting similar data-driven approaches in the construction claim context. Through a systematic literature review in conjunction with ten industry interviews, we present similarities in control mechanisms and disciplinary powers as regulatory elements but reveal dissimilarities in self-governance and utilisation of data analytics. This study proposes categorizing initiatives in claim preparation to people-led, organisational-led, and data-led with data analytics pivotal in each. Through the application of data analytics resembling governmentality within the realm of political governance, the study reveals that the integration of self-governance and smart data analytics presents a potential solution for effectively addressing the challenges inherent in the preparation of construction claims.

HARVESTING INSIGHTS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING AGRICULTURAL FINANCE

The intersection of artificial intelligence (AI) and agricultural finance has emerged as a transformative force in enhancing the efficiency and sustainability of farming practices. This review article explores the multifaceted applications of AI technologies in agricultural finance, focusing on credit scoring, risk assessment, and financial decision-making for farmers and agricultural businesses. We examine the integration of machine learning algorithms and big data analytics in providing tailored financial products, enabling precision agriculture, and improving access to credit in under banked communities. By synthesizing current literature and case studies, we identify key challenges and opportunities, such as the need for data security, transparency, and the democratization of AI tools in rural finance. Furthermore, we discuss the potential of AI to address climate change impacts on agriculture and its implications for financing sustainable practices. This article aims to provide stakeholders in the agricultural finance sector—policymakers, financial institutions, and technology developers—with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.

Integration of big data analytics in the investigation of the relationship between acromegaly and cancer

ObjectiveTo evaluate the association between acromegaly and cancer and different types of cancer by using natural language processing systems and big data analytics. Material and methodsWe conducted an observational, retrospective study utilizing data from the electronic health records (EHRs) of Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Information from the EHRs was extracted using artificial intelligence techniques and analyzed using Savana Manager 4.0 software. ResultsOut of a total of 708,047 registered patients (54.7% females), 544 patients (0.08%; 330 women, 60.7%; mean age at diagnosis 53.0±15.8 yr) were diagnosed with acromegaly. The incidence of cancer was higher in patients with acromegaly vs those without this condition (7.7% vs 3.9%, p<0.001; OR, 2.047, 95%CI, 1.493–2.804). Male acromegalic patients had a higher prevalence of cancer vs females (57.1% vs 42.9%, p=0.012). A significantly higher prevalence of colorectal cancer (2.9% vs 1.4%, p=0.006), bladder cancer (1.1% vs 0.3%, p=0.005), and lymphoma (1.1% vs 0.3%, p=0.009) was observed in patients with acromegaly vs those without the condition. Acromegalic men had significantly higher prevalence rates of colorectal cancer (4.7% vs 1.3%, p=0.001), bladder cancer (2.8% vs 0.4%, p<0.001), breast cancer (0.9% vs 0.2%, p=0.042), gastric cancer (0.9% vs 0.1%, p=0.011), lymphoma (1.4% vs 0.3%, p=0.037), and liver cancer (0.9% vs 0.1%, p=0.012) vs non-acromegalic men. On the other hand, acromegalic women showed a higher prevalence of thyroid cancer (1.2% vs 0.4%, p=0.043) vs non-acromegalic women. ConclusionOur study, based on artificial intelligence techniques and analysis of real-world data and information, revealed a significant association between acromegaly and cancer in our hospital population, mainly acromegalic men, with a higher frequency of colorectal cancer, bladder cancer and lymphoma in particular.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

The Role of AI-powered financial analytics in shaping economic policy: A new era for risk management and national growth in the United States

The integration of AI-powered financial analytics is revolutionizing how U.S. enterprises and policymakers’ approach economic forecasting and risk management. This article explores the role of AI-based tools, such as machine learning and predictive analytics, in enhancing financial decision-making processes and shaping national economic policy. By providing real-time insights and accurate predictions, AI enables businesses and regulators to anticipate economic trends, mitigate financial risks, and foster financial stability. Through case studies, the research demonstrates how AI has driven economic growth, reduced risks, and enhanced financial oversight within key sectors. Additionally, it investigates the implications of AI on policymaking, highlighting how AI-enabled tools support the development of data-driven economic policies for long-term national growth. Despite the transformative potential, the article also addresses the ethical and regulatory challenges in deploying AI for economic decision-making. Ultimately, this research emphasizes that the future of economic policy in the United States is increasingly tied to the continued advancement and integration of AI-powered financial analytics.

A GENERALIZED APPROACH TO INTEGRATION PROCESS BY AUXILIARY FUNCTIONS

Analytical integrals of functions in closed form are rare and one has to resort frequently to numerical integration techniques. In this work, approximate auxiliary functions that can be integrated are replaced by the original function to determine approximate analytical and numerical integrals. Information on one and two points of the function is used in determining the parameters of the auxiliary trial functions. Three alternative methods are proposed. Once the function without an available closed-form integral is replaced with an integrable one, analytical and numerical integration results can be found. The integration step size can be substantially increased by the employment of the methods.

Statistical challenges and solutions in multidisciplinary clinical research: Bridging the gap between

This paper delves into the intricate challenges and innovative solutions in applying statistical methodologies within clinical research, aiming to bridge the gap between biostatistics and medicine. The study meticulously examines fundamental biostatistical concepts, addressing the complexities of modern clinical trials and observational studies. Through a comprehensive review of advanced regression models, causal inference techniques, and machine learning algorithms, the paper illuminates the evolving landscape of biostatistics in handling high-dimensional data and confounding variables. The methods employed in this study involve an extensive analysis of current literature, case studies, and practical applications that demonstrate the utility of these advanced methodologies. Key findings reveal that traditional statistical approaches often fall short in capturing the complexities of clinical data, necessitating the adoption of more sophisticated techniques. The integration of non-linear regression models, robust causal inference methods, and machine learning has significantly enhanced the accuracy and reliability of research outcomes, offering deeper insights into patient outcomes and treatment efficacy. Conclusions drawn from this study underscore the critical need for a paradigm shift in clinical research, moving beyond the rigid reliance on p-values towards a more holistic approach that emphasizes effect sizes, confidence intervals, and practical significance. The paper recommends continued innovation in statistical methodologies, particularly the integration of big data analytics and machine learning, to address the growing complexities of biomedical data. Furthermore, it advocates for interdisciplinary collaboration and ethical considerations in the application of these advanced techniques to ensure that biostatistics continues to contribute meaningfully to the advancement of medical science.

Analytic forms for the e+e− annihilation cross sections around a resonance including initial state radiation

For the first time, the exact analytic integration of the product between the Kuraev-Fadin radiative function and the Born-level cross section has been achieved for e+e− annihilation near a narrow resonance. The analytic result is in a perfect agreement with the one obtained through direct numerical integration. The extraction of resonance parameters is demonstrated through the unfolding of the initial state radiation effect. The analytic integration significantly accelerates the regression process by more than 160 times compared to numerical integration. Moreover, it facilitates the determination of experimental measurements for physical parameters, such as the two-dimensional confidence region of the branching fraction and the relative phase between the strong and electromagnetic amplitudes, within just one hour instead of one week typically required for numerical integration. Published by the American Physical Society 2024

A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection

This research presents a novel framework designed to enhance cybersecurity through the integration of Big Data analytics, addressing the critical need for scalable and real-time threat detection in large-scale environments. Utilizing technologies such as Apache Kafka for efficient data ingestion, Apache Flink for stream processing, and advanced machine learning models like LSTM and Autoencoders, the framework offers robust anomaly detection capabilities. It also includes automated response mechanisms using SOAR and XDR systems, significantly improving response times and accuracy in threat mitigation. The proposed solution not only addresses current challenges in handling vast and complex data but also paves the way for future advancements, such as the integration of more sophisticated AI techniques and application across various domains, including IoT and cloud security. This research contributes to the field by providing a comprehensive, adaptive, and scalable framework that meets the demands of modern cybersecurity landscapes.

A comparison of data visualization tools: A case study in health-related research

Data visualization tools are important tools for making health decisions based on large amounts of data. However, very few comparisons of data visualization tools have been done in the context of health-related research. This paper compares commonly available visualization tools from the perspective of a dashboard developer in the context of scientific health research using real-world data and to provide general suggestions for dashboard developers’ future visualization efforts in health-related scientific discoveries. We evaluated four commonly available visualization tools (Tableau, Spotfire, PowerBI, and R Shiny). Each tool was evaluated on visualization outcomes (comparing the generated outputs of tables, scatterplots, correlation plots, treemaps, heatmaps, and geographic maps), usability, Cloud compatibility, analytic integration, and a few other factors. All tools generated comparable visualization outcomes in our tests and were equally usable with the exception of R Shiny which is not recommended for users with limited coding experience. Every tool except R Shiny supports connections to PostgreSQL, gSheets, and Athena without additional configurations. All tools support integration with R and Python; however, PowerBI requires some additional configuration. Every tool is capable of data pre-processing and supports data transformation, null detection and outlier detection. Each tool has its own unique advantages and limitations.

Optimizing the food supply chain through the integration of financial models and big data in procurement: A strategy for reducing food prices

Optimizing the food supply chain through the integration of financial models and big data analytics presents a transformative strategy for reducing food prices. In an increasingly complex global market, managing procurement effectively is crucial for cost control and price stabilization. Financial models, such as cost-benefit analysis, financial forecasting, and risk assessment, provide valuable insights into procurement decisions, helping organizations anticipate price fluctuations and manage expenses. When combined with big data analytics, these models enable more precise predictions and strategic planning. Big data analytics harnesses vast amounts of information from various sources, including market trends, supplier performance, and consumer behavior. By analyzing this data, organizations can identify patterns, optimize inventory levels, and forecast demand with greater accuracy. This data-driven approach enhances decision-making in procurement, allowing for better negotiation with suppliers, efficient inventory management, and the reduction of wastage. The integration of financial models with big data provides a holistic view of the supply chain, enabling companies to implement dynamic pricing strategies and adjust procurement practices based on real-time insights. This integration helps in minimizing operational costs, improving supply chain efficiency, and ultimately reducing food prices for consumers. For example, predictive analytics can identify potential supply disruptions and adjust procurement strategies proactively, while financial models can evaluate the cost-effectiveness of various sourcing options. Challenges such as data integration, quality, and privacy must be addressed to fully leverage these technologies. Moreover, investment in advanced analytics tools and skilled personnel is essential for successful implementation. In conclusion, the synergy between financial models and big data analytics offers a strategic approach to optimizing the food supply chain. By enhancing procurement practices and leveraging real-time insights, this strategy contributes to reducing food prices and improving overall supply chain efficiency, benefiting both businesses and consumers. Keywords: Food Supply Chain, Financial Models, Big Data Analytics, Procurement Optimization, Cost Control, Price Reduction, Inventory Management, Predictive Analytics, Supply Chain Efficiency.

Building Resilient Supply Chains with Intelligent Automation

Building Resilient Supply Chains with Intelligent Automation represents a transformative approach to modern supply chain management, focusing on optimizing operations through the integration of advanced technologies, data analytics, and process automation. In an increasingly complex and competitive global market, these solutions offer a framework to enhance efficiency, transparency, and responsiveness across the entire supply chain from procurement to final delivery. This paper explores the core components of Intelligent Automation, including data integration, supply chain visibility, inventory and demand management, and process automation. However, implementing these systems also presents challenges such as overcoming data silos, managing supply chain complexity, and ensuring regulatory compliance. This study aims to provide a comprehensive understanding of Intelligent Automation in supply chains, emphasizing their benefits, challenges, and the critical role they play in the evolution of supply chain management in today's dynamic business environment. Keyword: Supply Chain, Advance Logistics, Courier Management, Supply Solution, Automation, Demant Management

Integration of Big Data Analytics in Management Information Systems for Business Intelligence

In the era of big data, organizations are increasingly leveraging advanced analytics to extract valuable insights from vast and complex datasets. Management Information Systems (MIS) play a crucial role in collecting, processing, and analyzing data to support decision-making. Integrating big data analytics into MIS can enhance business intelligence and improve organizational performance. This study aims to investigate the integration of big data analytics into MIS and its impact on business intelligence. Specifically, the study seeks to identify the challenges and opportunities associated with this integration and explore best practices for implementation. A qualitative research approach was adopted for this study. Data was collected through semi-structured interviews based on a survey of over 312 information technology (IT) professionals from 21 industries with IT managers and business analysts from January 2022 to December 2023. Thematic analysis was used to analyze the data and identify key themes related to integrating big data analytics into MIS. The findings indicate that integrating big data analytics into MIS can significantly improve business intelligence. According to the respondents, on average, there was a 30% increase in the accuracy of decision-making processes after the integration. Additionally, organizations reported a 25% reduction in operational costs and a 20% increase in revenue as a result of the integration. Moreover, 70% of the respondents agreed that integrating big data analytics into MIS improved their organization's overall performance. Integrating big data analytics into MIS offers numerous benefits, including improved decision-making, cost savings, and revenue growth. However, organizations must overcome challenges such as data privacy and security concerns and the need for skilled personnel to manage and analyze big data. Overall, this study highlights the importance of integrating big data analytics into MIS for enhancing business intelligence and achieving organizational success.

Soliton solutions of cubic quintic septimal nonlinear Schrödinger wave equation with conformable derivative by two distinct algorithms

This paper investigates the cubic-quintic-septimal nonlinear Schrödinger wave equation with a conformable derivative, which governs the evolution of light beams in a weak nonlocal medium. The analysis utilizes the Kudryashov method and the enhanced modified tanh expansion method. By utilizing these analytical integration schemes, various optical wave solutions are derived within the present conformable model. The paper demonstrates the significance of these optical soliton solutions by illustrating different soliton solutions, including kink-type, bell-shaped, singular, dark, and wave soliton solutions, depicted via contour, three-dimensional, and two-dimensional representations. Moreover, it is crucial to emphasize the importance of analyzing the cubic-quintic-septimal nonlinear Schrödinger wave equation, which finds utility across a spectrum of fields including optics, quantum mechanics, and the study of nonlinear wave propagation. Moving forward, these approaches hold promise for investigating diverse sets of differential equations within multiple domains of applied sciences. This governing equation also has numerous applications in nonlinear optics, such as describing the propagation of laser beams through materials with nonlinear optical properties. The inclusion of these nonlinearities illustrates the interaction and behavior of light beams in weakly non-local media.

Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes

Tax evasion through complex network schemes poses a significant challenge to tax authorities, leading to substantial revenue losses. This paper aims to develop and evaluate an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.

How can open data help airports to improve customer satisfaction?

In the evolving landscape of airport management, leveraging public data emerges as a transformative strategy to enhance operational efficiency and customer satisfaction. This paper explores the integration of data analytics in airport operations, illustrating through case studies how airports can utilise open data to optimise performance, improve passenger experience and streamline security processes. We highlight the significant benefits of adopting data-driven approaches, including increased operational transparency, improved decision making and enhanced service quality. Our findings underscore the pivotal role of public data in driving innovation and sustainability in airport management, offering actionable insights for airport authorities and stakeholders. This paper serves as a stimulus on how to benefit from the use of open data.

THE ROLE OF DATA ANALYTICS IN MAKING MANAGEMENT DECISIONS BY THE LOGISTICS INTERMEDIARIES

Data analytics plays a crucial role in increasing the effectiveness of management decisions by the logistics of intermediaries. The aim of the article is to identify the extent to which the quality of data analytics affects the effectiveness of decision-making in the logistics intermediaries, in particular, the speed of delivery of the studied companies.The study employed regression and correlation analysis to identify key influencing factors in terms of data analytics on the effectiveness of management decisions of the logistics intermediaries. The significance of investment in the qualification of analysts (with a coefficient of -1.6754), analytical tools (with a coefficient of -1.2575), and integration of analytics in decision-making processes (with a coefficient of -3.2511) directly affect the reduction of delivery time.It is emphasized that each analytical project contributes to the reduction of delivery time by 0.48 hours. Correlation analysis confirmed the relationship between the efficiency of logistics and the level of qualification of analysts (-0.283617), investment in analytical tools (-0.257322), the number of analytical projects (-0.343792), the level of integration of analytics (-0.712058). The strongest correlation was observed for the integration of analytics in management decision-making.It is recommended to focus on the development of analytical competencies, increase of investment in tools, intensification of projects, and integration of analytics in strategic management. Further research is planned on the use of artificial intelligence to optimize management decisions in logistics as part of ensuring the company’s sustainable development.

Continuous Service Improvement in IT Operations through Predictive Analytics

In the evolving landscape of IT operations, continuous service improvement is essential for maintaining high performance, reliability, and customer satisfaction. Predictive analytics, leveraging advanced data analysis techniques and machine learning algorithms, offers a transformative approach to enhancing IT service management. This research paper explores the integration of predictive analytics into IT operations to drive continuous service improvement. It investigates how predictive models can forecast potential issues, optimize resource allocation, and enhance decision-making processes, ultimately leading to improved operational efficiency and service quality. The study begins with an overview of traditional IT service management practices and the limitations they face in adapting to dynamic and complex IT environments. Conventional approaches often rely on reactive problem-solving and periodic reviews, which can lead to inefficiencies and missed opportunities for proactive intervention. Predictive analytics offers a paradigm shift by utilizing historical data and real-time information to predict future outcomes, enabling organizations to address potential problems before they impact operations.

Application of Big Data and Quantum Computing in the Secure Federated Internet of Things

Federated Internet of Things (IoT) presents both unprecedented opportunities and challenges in security and data management. This study explores the integration of big data analytics and Quantum Computing as potential solutions to address security concerns within the Federated IoT ecosystem. The study examines the implications of leveraging big data analytics to process and analyze the massive volume of data generated by IoT devices. Advanced analytics techniques, including machine learning and anomaly detection algorithms, are employed to enhance the detection and mitigation of security threats such as unauthorized access, data breaches and malicious attacks. Furthermore, the study investigates the role of Quantum Computing infrastructure in providing scalable and reliable resources for securely storing, processing and transmitting IoT data. By offloading computational tasks to quantum-based platforms, the aim is to alleviate the burden on edge devices while ensuring robust security measures are in place to safeguard sensitive information. A comprehensive review of existing literature and case studies identifies key challenges and opportunities in implementing big data and Quantum Computing solutions within the Federated IoT environment. The study also proposes potential frameworks and methodologies for integrating these technologies effectively, considering factors such as data privacy, scalability and interoperability. Overall, this research aims to advance secure IoT systems by leveraging big data analytics and cloud computing. By addressing security concerns proactively and adopting innovative approaches, the goal is to create a more resilient and trustworthy Federated IoT ecosystem, benefiting society at large.