Integrated Data Analytics Research Articles

Banking Transformation in the Digital Era: Bank Cooperation with Financial Technology and the Role of the Financial Services Authority in Digital Bank Supervision

The Industrial Revolution 4.0 has brought significant transformations to various aspects of life, including financial activities, which have increasingly transitioned into digital systems facilitated by financial technology (fintech). Fintech accommodates remote financial activities, reshaping the banking industry through the emergence of digital banks that enhance customer convenience in accessing banking services. This development reflects the growing cooperation between banking institutions and fintech companies. However, digital-based financial activities are inherently susceptible to system vulnerabilities, potentially causing customer losses. This study explores two main issues: (1) the nature of cooperation between banks and fintech and (2) the role of the Financial Services Authority (OJK) in supervising digital banking operations. The findings reveal that collaboration between banking and fintech is achieved by integrating banking services with fintech platforms, enabling seamless customer access. The OJK employs direct (on-site) and indirect (off-site) supervision methods to ensure regulatory compliance and mitigate risks. Additionally, the OJK leverages advanced technologies such as the OSIDA (OJK Suptech Integrated Data Analytics) system to optimize supervision and implement a regulatory sandbox framework to foster innovation while maintaining oversight.

Revolutionizing Supply Chain : The Synergy of Warehouse Management and Data Analytics

This article examines the transformative impact of integrating data analytics with warehouse management in modern supply chains. It explores how this synergy is revolutionizing inventory management, order fulfillment, and customer service across industries. The article discusses key elements of effective warehouse management, including optimal space utilization, efficient inventory control, and timely order processing. It then delves into how data analytics elevates these functions through inventory optimization, real-time monitoring, and predictive analytics. Case studies from e-commerce and manufacturing sectors illustrate real-world applications and benefits. Finally, the competitive advantages gained through enhanced operational efficiency, cost reduction, and improved service levels are analyzed, highlighting the critical role of this integration in shaping the future of supply chain management.

How to Implement a Data Analytics and Emerging Technologies-Enabled Accounting Curriculum

ABSTRACT As technological advancements reshape the accounting profession, the School of Accounting and Finance (SAF) at the University of Waterloo integrated Data Analytics and Emerging Technologies (AET) into its curriculum. This initiative equips students with essential and advanced AET skills, employing tools like spreadsheets, Tableau, Alteryx, R, Python, RPA, and blockchain. This paper builds on our experiences to provide a framework to help educators integrate AET into the accounting program at their institutions. We outline the strategic development and implementation challenges experienced in developing our program. JEL Classifications: M41.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

Conceptual framework for enhancing decision-making in higher education through data-driven governance

In an increasingly complex and competitive landscape, higher education institutions must leverage data-driven governance to enhance decision-making processes. This paper presents a conceptual framework aimed at integrating data analytics into the governance structures of higher education, facilitating informed decision-making, and improving institutional effectiveness. The framework emphasizes the importance of collecting, analyzing, and utilizing data across various dimensions, including academic performance, student engagement, financial management, and operational efficiency. By adopting a data-driven approach, institutions can identify trends, forecast outcomes, and make strategic decisions that align with their mission and goals. The study outlines key components of the framework, including data governance, analytics capabilities, stakeholder engagement, and institutional culture. Data governance ensures that data quality, integrity, and security are maintained, while analytics capabilities empower decision-makers with actionable insights. Engaging stakeholders—faculty, administrators, students, and external partners—fosters a collaborative environment where data-driven decisions are embraced and supported. Furthermore, the framework addresses the challenges institutions face when implementing data-driven governance, such as resistance to change, data silos, and inadequate technological infrastructure. Strategies for overcoming these challenges include investing in professional development, enhancing data literacy, and promoting a culture of continuous improvement. The framework also highlights the significance of ethical considerations and transparency in data usage, ensuring that decision-making processes are equitable and accountable. The findings indicate that by embracing data-driven governance, higher education institutions can enhance their responsiveness to changing demands, optimize resource allocation, and improve overall institutional performance. Ultimately, the conceptual framework serves as a guide for higher education leaders seeking to harness the power of data in their decision-making processes, fostering a proactive and adaptive institutional environment.

Cybersecurity and defense in intelligent transportation systems

Intelligent Transportation Systems (ITS) have become increasingly crucial for enhancing transportation networks' efficiency, safety, and security. ITS includes advanced technologies such as electronic sensors, data transmission, and intelligent control technologies, providing better driver and rider services. However, these technologies pose significant security and privacy risks, in which attackers can easily disrupt transportation systems. This paper presents a survey of the challenges with ITS, the engineering requirements, attack challenges, and related work of securing ITS. This survey also discussed the elements of ITS, including advanced traffic management systems, connected and automated vehicles, intelligent infrastructure, and integrated data analytics, as well as the role of government policies, public-private partnerships, and stakeholder engagement. The aim is to look at present security challenges associated with ITS and provide better ways to the system. which is critical for creating smart cities and increasing the importance of individual and public transportation systems.

Strategies for Effective Product Roadmap Development and Execution in Data Analytics Platforms

Manufacturing is only one of several industries going through a digital transformation in this era of digital disruption. Manufacturing businesses are racing to adopt IoT-based solutions in order to innovate, increase productivity, lower costs, and gain greater market share because of the enormous transformational potential offered by Internet-of-Things (IoT) & Big Data. It is being used by industrial companies all over the world to increase flexibility while achieving cost savings, reduced inefficiencies, and better performance. It is no longer a trend for the future. But putting Industry 4.0 technology support into practice is a tough endeavour that gets increasingly harder in the absence of a common method. These are produced during the course of business operations and are kept in databases, email communication, transaction logs, free form texts on (business) social media, and other places. Businesses want to integrate data analytics methods into their decision-making processes by utilisation of these data. The field of Big Data analyses has seen tremendous progress in the IT sector in recent years. It appears that in order to deal with the extremely dynamic situations of today, new methods for product route mapping are required. This article provides an overview of the literature in science on product road mapping in order to shed light on the current state of the art & pinpoint research gaps. In order to demonstrate the influence of Industry 4.0 technological innovations on the manufacturing sector, this study provides an organised and content-focused evaluation of the literature. This paper offers recommendations for converting a legacy manufacturing facility into an Industry 4.0-compliant smart plant. Research gaps encompass topics including how to include objectives or results in product roadmaps, how to match a roadmap with the item's vision, and how to include activities related to product discovery in product roadmaps.

Integrating data analytics in academic institutions: enhancing research productivity and institutional efficiency

This review paper explores the strategic integration of data analytics within academic institutions, focusing on its potential to enhance research productivity and institutional efficiency. As higher education faces growing demands for improved outcomes and accountability, data analytics emerges as a critical tool for informed decision-making, resource optimization, and academic excellence. The paper begins by examining the role of data analytics in driving research insights, predictive planning, and collaboration, ultimately improving publication metrics and research impact. It then delves into how data analytics can streamline administrative processes, enhance student outcomes, and optimize resource management. The paper also addresses the significant challenges and barriers to integration, including concerns over data privacy, technical and organizational hurdles, and financial constraints. Finally, the paper offers recommendations for future directions, emphasizing strategic integration, capacity building, fostering a data-driven culture, and leveraging emerging technologies like AI and real-time data processing. These insights provide a comprehensive framework for academic institutions aiming to harness the power of data analytics to advance their research and operational goals.

An integrated GRC approach to combating fraud in microloan services

This research aims to reduce fraud risks in Indonesian banks and non-bank financial institutions providing microloan services. The study employs data analytics and machine learning techniques using employee data from Bank X spanning 2017-2019 with samples of 28,004 workers (2017-2018) and 27,274 employees (2019). Confirmatory factor analysis and XGBoost predictive modelling are applied within the fraud triangle framework to identify critical fraud risk indicators related to employee pressure. An algorithmic approach categorizes personnel based on fraud risk ratings enabling the detection of potentially suspicious activities for proactive intervention. The analysis reveals that incorporating data analytics into governance, risk management and compliance (GRC) systems can accurately forecast fraud probability by focusing on factors associated with employee pressure and opportunities. This facilitates targeted fraud prevention solutions by integrating control mechanisms, risk processes and auditing standards. The predictive model provides valuable insights for policymakers to combat fraud by enhancing governance and risk management practices specific to microloans. This research concludes that the predictive model is a pragmatic decision-making tool for banks offering micro-loans. It mitigates dangers by detecting high-risk personnel and transactions. Integrating data analytics with robust GRC frameworks enables financial institutions to uphold integrity through proactive fraud monitoring and targeted preventive interventions tailored to identify risk profiles. The study offers an integrated technological organizational approach to protect microlending activities.

Integrating Data Analytics Training into Curricula at Small Colleges: A Practical Framework for Enhancing Student Skills

Quantitative Data Analytics (QDA) is a critical skill for professionals across various natural science disciplines. However, college curricula, especially at small colleges, often lag behind the evolving workforce’s needs due to limited faculty resources. Despite abundant training materials, there is a noticeable gap in practical frameworks for effectively developing and implementing QDA training programs, particularly addressing existing curriculum gaps and resource constraints. This study presents a practical framework for developing introductory QDA training programs tailored for non-statistical majors at resource-constrained small colleges with various educational settings. Employing a design science strategy, we developed a modular co-curricular QDA Training Program (CQDATP), consisting of five training modules refined over three iterative phases. This flexible design integrates seamlessly with existing courses or can function as standalone workshops, bolstering students’ preparedness for advanced courses and research opportunities. Our program evaluation indicates that our approach significantly enhanced students’ QDA capabilities without necessitating extensive curriculum overhauls. By focusing on adaptability and resource efficiency, our framework holds the potential to benefit educators, curriculum developers, and administrators at small colleges, ensuring that graduates are well-prepared for data-driven industries, even when resources are limited.

(Invited) Chemical Mechanical Planarization Integrated Data Collaboration: Uncover Hidden Interactions for Predictive Maintenance and PCN

This article introduces an innovative analytical methodology that employs machine learning to optimize and predict consumable impacts for Chemical Mechanical Planarization (CMP). By harnessing fleet CMP equipment performance and individual equipment characteristics, our proposed approach aims to elevate wafer-level quality, maximize equipment utilization rates, and minimize unnecessary downtime. CMP, a multifaceted process, its performance is often influenced by strong interactions from its critical components including slurry, pad, machine recipe, and dresser usage. This requires control beyond hardware advancements, especially when equipment is pushed to its limits. Therefore, the demand for higher precision on-wafer performance mandates a data collaboration with sophisticated analytical strategy. In addition, a digitalized product change notification (PCN) concept is also introduced to demonstrate effectiveness of applying integrated data analytics collaboratively across semiconductor chain to accelerate material manufacturing process changes, minimizing impacts on wafer results and shortening the learning cycle and ramp time to full production.Our study leverages data analytics to explore differences between equipment within the same fleet of similar processing. Engineers can deploy a process based on the historical performance of specific equipment, supported by a model trained on data from the same fleet. Furthermore, our approach enables each equipment to predict its own fingerprinted consumable maintenance, accurately forecasting the maintenance time for consumable changes. This concept is equally applicable for consumable PCN migrations.The first use case is to demonstrate the use of integrated data for fleet matching of CMP equipment and/or consumable batches running similar processes. This is particularly crucial as the demand for process precision increases and the need to reduce post-CMP result variability in removal rates and wafer-level uniformity intensifies. This approach could add proactive data driven strategy for process engineers who traditionally concentrate on the removal rate, a highly influential factor, with the standard deviation of platen and machine data identified as a key parameter.The second use case employs heatmaps analysis and proposes scatter plots as an intuitive method to comprehend intricate connections between average removal rates and various parameters. Machine learning is particularly advantageous in this context, as no single parameter plays a dominant role. Preliminary observations suggested, for example that dresser and pad characteristics may have compounding impacts to the wafer level performance.In summary, this article provides a roadmap for in-depth CMP data analysis fed into machine learning, aiming to unravel trends and influential factors that can enhance process optimization and shorten the PCN qualification timeline to meet the increasing demands for CMP process precision and efficiency in High Volume Manufacturing (HVM).

Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm

Bus passenger flow prediction integrates data analytics and modelling techniques to forecast the number of passengers using bus services, incorporating historical usage patterns, demographics, weather, and events for optimal scheduling and resource allocation. The Bi-LSTM fusion model enhances accuracy by processing past and future features simultaneously, leveraging bidirectional LSTM layers and an attention mechanism to capture temporal dependencies. This approach not only refines insights crucial for urban mobility challenges like traffic management and demand forecasting but also improves route planning and service efficiency. The SMO algorithm initializes with a diverse spider monkey population exploring solution spaces. Through local and global leader phases, it iteratively updates positions based on fitness and probabilistic selections, maintaining a balance between exploration and exploitation. Perturbation-based updates in the local leader phase ensure adaptability, preventing premature convergence, while the global leader phase guides towards better solutions, enhancing efficiency in complex optimization tasks and promoting dynamic adaptation. In Dataset 1, the proposed model achieved a training time of 137 seconds, slightly longer than HA (115s), SARIMA (112s), GRU (123s), and ST-ResNet (113s). It demonstrated superior accuracy at 89%, surpassing HA (66%), SARIMA (68%), GRU (63%), DeepST (78%), and ST-ResNet (84%). In Dataset 2, the model exhibited the lowest RMSE, MAE, and MAPE%, indicating superior predictive accuracy over SVR, CNN, GCN, LSTM, and CONV LSTM models. These findings validate the proposed model's effectiveness in enhancing predictive capabilities for transit forecasting, underscoring its potential to optimize urban mobility and transportation management strategies significantly.

Emerging techniques for the trace elemental analysis of plants and food-based extracts: A comprehensive review

This comprehensive review investigates the dynamic landscape of trace elemental analysis methodologies applied to plants and food-based extracts. The exploration spans from the inception of techniques to the latest procedures, contributing to heightened precision and sensitivity in elemental detection. According to the WHO, herbal plants and medicine from varied soil compositions serve as crucial therapeutic agents for 70–80 % of the world's population. Yet, their susceptibility to trace element toxicity poses a significant risk to human health. Rising population and increased food demands have led to environmental pollution, contaminating the food chain through unintended activities like industrialization, mining, and pesticide production. The elemental composition of plants and derived extracts is central to comprehending nutritional profiles, evaluating product quality, and ensuring food safety. Methodological advancements, progressing from manual procedures to sophisticated technologies such as X-ray fluorescence (XRF), neutron activation analysis (NAA), inductively coupled plasma optical emission spectroscopy (ICP OES), and inductively coupled plasma mass spectrometry (ICP-MS), are delineated. Recent strides in paper-based electrochemical sensors are highlighted for their distinctive capabilities and elucidation of associated advantages and limitations.Moreover, the review delves into innovative sample preparation methodologies, encompassing microwave-assisted digestion and solid-phase microextraction, to amplify the efficiency of elemental extraction and subsequent analysis. Integrating data analytics and machine learning in elucidating complex elementary datasets is explored, underscoring the potential for heightened accuracy and automation in trace elemental analysis. This review compiles literature data, summarizing sample preparation methods for various herbal parts (roots, soil, stems, bark, fruits, food). Standard protocols from WHO, United States Pharmacopeia-National Formulary (USP-FR), Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy (AYUSH) are considered. Lead, Cadmium, Mercury, and Arsenic are the primary toxic elements of concern in herbal medicines. This review furnishes valuable insights tailored for researchers, analysts, and policymakers actively involved in advancing the domain of trace elemental analysis in plants and food-based extract.

Measuring the Integration of Data Analytics in the Computerized Accounting Curriculum

This study demonstrates a framework for integrating data analytics into an existing undergraduate accounting curriculum, that students are introduced to data analytics progressively while not requiring additional semester credit units. The Qasim model proposes course learning outcomes related to data analysis with specific course levels from lecturers and students of accounting study program. The findings showed that data analysis materials are identified as existing and impact changes in learning materials. Empirically, there are differences in the level of understanding and mastery of skills in data analysis tools between lecturers and students. The course that needed to develop the adoption of data analysis material is intermediate financial accounting expansively. This model can be a reference for lecturers and curriculum committees when updating the accounting curriculum to include data analytics integrating.

REVIEW OF DATA ANALYTICS AND INFORMATION SYSTEMS IN ENHANCING EFFICIENCY IN FINANCIAL SERVICES: CASE STUDIES FROM THE INDUSTRY

This study explores the transformative impact of integrating data analytics and information systems on enhancing efficiency in the financial services industry. The research highlights significant improvements in operational efficiency, risk management, and customer satisfaction through detailed case studies of JPMorgan Chase, Allstate Insurance, BlackRock, and Bank of America. The findings reveal that AI-driven analytics tools at JPMorgan Chase led to a 30% reduction in fraud-related losses and a 20% increase in customer satisfaction. Through predictive analytics, Allstate Insurance achieved a 40% reduction in claims processing time and a 25% improvement in underwriting accuracy. BlackRock reported a 35% increase in portfolio returns due to machine learning and predictive analytics. In comparison, Bank of America experienced a 22% increase in customer retention and a 15% rise in satisfaction through data-driven CRM systems. These outcomes underscore the critical role of advanced data analytics and information systems in driving innovation and operational excellence in financial services. The study emphasises the importance of continuous technological advancements and strategic implementation to maximise the benefits of these tools in the industry.

Optimizing Decision-Making: Data Analytics Applications in Management Information Systems

This study delves into integrating data analytics applications within Management Information Systems (MIS), exploring their impact on decision-making processes in organizational settings. The discussion synthesizes qualitative and quantitative methodologies, presenting insights from scholarly literature, surveys, and interviews. Scholarly discourse highlights the transformative potential of data analytics tools in facilitating informed decision-making, aligning with practical applications showcased in empirical studies. However, inherent challenges surface, primarily concerning data quality, as revealed by 62\% of respondents, underscoring the need for organizations to address these obstacles. Despite challenges, substantial adoption rates of data analytics tools (78\%) affirm their growing recognition in decision-making within diverse industries. Reported enhancements in operational efficiency (35\%) and competitive advantage (22\%) among organizations leveraging data analytics validate their efficacy in driving organizational performance metrics within MIS. Further research should address ethical implications, longitudinal analyses of data analytics efficacy, and interdisciplinary collaborations exploring the nexus between data analytics and managerial decision-making. This study is a foundational step, providing empirical evidence and future research trajectories essential for organizations aiming to optimize decision-making through data analytics applications within Management Information Systems.

UX Design for an E-commerce Application: A Design Thinking Perspective

The development of e-commerce applications has revolutionized the way people conduct business, but challenges still exist in designing applications that align with user needs and expectations. This study aims to document the design-thinking (DT) approach used in the development of a customized e-commerce application based on the user's perspective, with the goal of extracting the user experience of traditional commerce processes to identify its features. In this study, we focused on the first four phases of DT, which include empathize, define, ideate, and prototype, to gather insights and understand the experiences, needs, pain points, and solutions of the local producers. The data gathered from interviews and observations was subjected to content analysis to identify patterns and themes related to desired features. After analysis, the researchers identified key app features, including integrating data analytics for product performance and demand forecasting, introducing a peer-vendor marketplace, a vendor-managed inventory management system, and a product verification system for authenticity. A mockup prototype design was created and presented to users for feedback. The study highlights the importance of user perspectives in e-commerce app design and the value of design thinking in creating a customized user experience that aligns with user needs.

EFFECTIVE DECISION MAKING USING DATA ANALYTICS

In contemporary business landscapes, the transformative potential of data analytics in decision-making processes cannot be overstated. This comprehensive abstract delves into the multifaceted realm of leveraging data analytics for informed and effective decision-making. It scrutinizes the intricate interplay between data analytics methodologies and decision-making frameworks, elucidating how organizations can harness data to drive strategic initiatives, enhance operational efficiency, and gain competitive advantages. The abstract begins by elucidating the foundational principles of data analytics, encompassing data collection, preprocessing, analysis, and interpretation. It underscores the pivotal role of advanced analytics techniques, including machine learning, predictive modeling, and data visualization, in extracting actionable insights from complex datasets. Furthermore, it explores the significance of establishing robust data governance frameworks to ensure data quality, integrity, and security throughout the decision-making process. Building upon this foundation, the abstract delineates the pivotal stages of the decision-making process wherein data analytics can exert a transformative influence. It elucidates how data-driven insights facilitate comprehensive situational awareness, enabling decision-makers to anticipate market trends, customer preferences, and operational risks with unprecedented accuracy. Moreover, it highlights the role of predictive analytics in scenario planning and risk management, empowering organizations to proactively mitigate potential threats and capitalize on emerging opportunities. However, the abstract acknowledges the inherent challenges associated with integrating data analytics into decision-making processes. It addresses concerns regarding data privacy, ethical considerations, and algorithmic biases, emphasizing the imperative of responsible data usage and ethical decision-making frameworks. Moreover, it underscores the significance of fostering a data-driven organizational culture characterized by data literacy, collaboration, and a propensity for experimentation. To exemplify the transformative impact of data analytics on decision-making, the abstract incorporates real-world case studies and industry examples across diverse domains, including finance, healthcare, marketing, and supply chain management. These case studies elucidate how organizations have leveraged data analytics to optimize resource allocation, streamline operations, enhance customer experiences, and drive innovation.

Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.

Big Data Analysis and Deep Learning Optimization in Artificial Intelligence Production of Information Enterprises

Intelligent manufacturing technology is required to upgrade existing enterprises' management and production operations. To construct a ground breaking fusion structure, this project unites the theoretical underpinnings, technical breakthroughs, and applications of data analytics, optimisation, and intelligent production engineering. It is driven by China's desire of cutting-edge commodities and efficient growth methods. This research establishes the broad framework for merging optimisation and data analytics. There is a list of data analytics and system optimisation technologies that can address important challenges with intelligent manufacturing. By integrating data analytics and optimisation, businesses may better forecasting and management of new terrain, as well as reveal hidden information to increase decision-making efficacy.