Authors: Rivansh Kaushik

Abstract: The abstract provides a concise summary of the review article, emphasizing the integration of machine learning (ML) techniques with SAP-based financial planning to improve forecast accuracy. Financial forecasting is a critical function for organizations to manage budgets, allocate resources, and make strategic decisions. Traditional forecasting methods in SAP, such as time-series analysis and rule-based approaches, often struggle with complex and dynamic market conditions, leading to suboptimal planning. Machine learning offers advanced predictive capabilities by identifying hidden patterns in historical data and adapting to new trends over time. This review highlights the role of ML algorithms including regression models, neural networks, and ensemble methods in enhancing forecasting precision. The article systematically examines the integration process of ML with SAP systems, exploring data preprocessing, model selection, and deployment within SAP environments. Key case studies and research findings demonstrate measurable improvements in forecast accuracy, including reduced error metrics such as RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error). The review also addresses practical challenges, such as data quality issues, computational resource demands, and organizational adoption hurdles. Finally, it outlines future directions, including real-time predictive analytics, AI-driven planning, and hybrid approaches combining ML with traditional statistical models. By providing a comprehensive overview, the article aims to guide both practitioners and researchers in leveraging machine learning for enhanced financial decision-making within SAP-based systems. The abstract serves as a snapshot, giving readers insight into the objectives, methodology, findings, and implications of integrating ML in SAP financial planning, emphasizing the potential for improved efficiency, accuracy, and strategic value.

DOI: http://doi.org/10.5281/zenodo.18162637

Published by: vikaspatanker

Authors: Mrinal Daksheen

Abstract: Achieving enterprise-wide financial transparency is a critical challenge for large organizations due to fragmented data, manual reconciliation processes, and delayed reporting. SAP provides a robust platform for integrated financial management, yet traditional reporting methods often fall short in delivering real-time, accurate insights. This article explores the application of data-centric AI pipelines within SAP to enhance financial transparency across the enterprise. By emphasizing high-quality, validated data over purely model-centric approaches, these pipelines enable automated data extraction, cleaning, transformation, and validation, supporting real-time dashboards, predictive forecasting, anomaly detection, and compliance monitoring. The discussion covers pipeline architecture, integration strategies, implementation best practices, and potential benefits, including improved accuracy, operational efficiency, risk mitigation, and regulatory compliance. Challenges such as data inconsistency, integration complexity, and model maintenance are also addressed, along with future directions in adaptive AI and enterprise-wide intelligent financial systems. By adopting data-centric AI pipelines, organizations can transform financial reporting into a proactive, insight-driven function, enhancing decision-making, stakeholder trust, and organizational agility.

DOI: https://doi.org/10.5281/zenodo.18162511

Published by: vikaspatanker

Authors: Bhavya Kaironit

Abstract: Risk-aware cloud architectures play a pivotal role in enhancing the security, compliance, and operational efficiency of SAP-enabled financial and healthcare systems. By integrating risk management principles, data protection mechanisms, and governance frameworks directly into cloud environments, organizations can proactively address threats while enabling innovation. In financial systems, these architectures support secure transaction processing, fraud detection, and regulatory reporting, ensuring reliability and compliance. In healthcare, they facilitate secure electronic health records, analytics, and telemedicine services while adhering to privacy regulations such as HIPAA and GDPR. Incorporating AI and automation further strengthens risk detection, response, and monitoring capabilities. This approach ensures that sensitive financial and patient data are safeguarded, regulatory requirements are met, and organizational trust is maintained. The paper highlights how risk-aware design principles in SAP cloud architectures can simultaneously enable innovation and resilience in highly regulated industries.

DOI: https://doi.org/10.5281/zenodo.18162423

 

Published by: vikaspatanker

Authors: Ritvik Nandesh

Abstract: Predictive maintenance has emerged as a strategic capability for industrial and enterprise environments seeking to reduce unplanned downtime, optimize asset performance, and improve operational efficiency. Traditional SAP-based maintenance systems primarily rely on historical data and scheduled maintenance plans, limiting their ability to respond to real-time equipment conditions. The integration of Internet of Things technologies and artificial intelligence enables a data-driven approach that transforms maintenance operations from reactive to predictive. This article presents a unified AI and IoT architecture integrated with SAP platforms to support intelligent predictive maintenance operations. The proposed architecture leverages IoT sensors and edge computing for real-time data acquisition, AI models for failure prediction and anomaly detection, and SAP systems for orchestrating maintenance workflows and enterprise processes. Key architectural components, data flows, and predictive maintenance workflows are discussed, along with security, governance, and compliance considerations. The article also examines performance evaluation metrics, business impact, and implementation challenges. Finally, emerging trends such as edge AI, digital twins, and autonomous maintenance systems are explored, highlighting their potential to further enhance SAP-based predictive maintenance solutions. The insights provided aim to guide organizations in designing scalable, secure, and intelligent maintenance architectures aligned with enterprise objectives.

DOI: https://doi.org/10.5281/zenodo.18162331

Published by: vikaspatanker

Authors: Anvesha Tilvani

Abstract: Enterprises are increasingly adopting intelligent technologies to remain competitive in rapidly evolving digital environments. SAP ecosystems play a central role in this transformation by integrating core business processes with advanced technologies such as artificial intelligence, cloud computing, and the Internet of Things. The convergence of these technologies enables real-time data processing, predictive analytics, and intelligent automation, transforming traditional SAP systems into adaptive and insight-driven enterprise platforms. This article explores the evolution of SAP ecosystems toward future-ready architectures that support intelligent enterprise capabilities. It examines cloud foundations, AI-enabled enterprise applications, and IoT integration within SAP environments, highlighting how their convergence enhances operational efficiency, scalability, and decision-making. Key use cases across manufacturing, supply chain management, finance, and customer experience are discussed to illustrate practical business value. The article also addresses security, privacy, and governance considerations, as well as implementation challenges related to integration complexity, data quality, and organizational readiness. Finally, emerging trends such as autonomous enterprise systems, generative AI, edge intelligence, and sustainable SAP ecosystems are explored, emphasizing their role in shaping the next generation of intelligent enterprises. The insights presented aim to guide organizations in designing resilient, scalable, and future-ready SAP ecosystems aligned with strategic business objectives.

DOI: https://doi.org/10.5281/zenodo.18162235

Published by: vikaspatanker

Authors: Nishka Vardhan

Abstract: Regulatory auditing and compliance in SAP financial systems are critical for ensuring organizational accountability, risk management, and adherence to standards such as SOX, IFRS, and GDPR. Traditional audit approaches, often manual and rule-based, struggle with real-time monitoring and predictive insights, creating gaps in efficiency and transparency. This article investigates the role of Explainable Artificial Intelligence (XAI) in enhancing SAP financial auditing and compliance processes. It presents a structured XAI framework that integrates SAP ERP and S/4HANA data sources, anomaly detection, risk scoring, and human-interpretable explanations to support auditors and compliance teams. Use cases, including explainable fraud detection, continuous compliance monitoring, and audit decision assistance, are analyzed. An experimental evaluation demonstrates that XAI models achieve competitive predictive accuracy while significantly improving transparency, traceability, and auditor trust compared to black-box models. The discussion addresses trade-offs between interpretability and performance, adoption challenges in SAP environments, and ethical considerations. Overall, the study highlights XAI’s potential to transform financial auditing by providing actionable, explainable insights that align with regulatory requirements and foster a more transparent, accountable, and efficient audit ecosystem.

DOI: http://doi.org/10.5281/zenodo.18162206

Published by: vikaspatanker

Authors: Ira Somketu

Abstract: The healthcare industry generates vast amounts of sensitive patient data daily, creating both opportunities and challenges for healthcare providers. Secure management and intelligent analysis of this data are critical for improving patient outcomes, operational efficiency, and regulatory compliance. SAP systems, widely used in healthcare, provide robust platforms for data storage, integration, and management; however, traditional implementations often struggle with unstructured data analysis, predictive insights, and advanced security requirements. The integration of Artificial Intelligence (AI) with SAP systems addresses these gaps by enabling real-time analytics, predictive modeling, anomaly detection, and automated security monitoring. This article explores the convergence of AI and SAP in healthcare, focusing on secure patient data management, AI-driven intelligence, implementation strategies, and emerging trends. Through AI-enhanced SAP systems, healthcare organizations can transform complex datasets into actionable insights while maintaining patient privacy, regulatory compliance, and operational excellence. The article also examines future directions, including real-time analytics, ethical AI, and the integration of emerging technologies such as blockchain and federated learning, highlighting the strategic importance of AI-powered patient data intelligence in modern healthcare ecosystems.

DOI: https://doi.org/10.5281/zenodo.18162131

 

Published by: vikaspatanker

Authors: Drupad Gowda N, GS Rajdeep, Puneeth Kumar GJ, Vignesh Shenoy R

Abstract: Videos are now used everywhere — in education, smart cameras, video calls, hospitals, industries, and home security. But whenever the internet becomes slow or the wireless signal becomes weak, the normal video transmission systems fail and the video quality becomes very poor. The MDVSC (Model Division Video Semantic Communication) technique solves this by transmitting only the meaningful information from video frames instead of sending the entire frame pixel by pixel. It carefully separates the information that stays the same across frames (like background or static objects) and the information that changes (like moving objects). Then, it sends only the important features first. Because of this, MDVSC provides better video clarity, uses less data, and continues working even when network strength is low.

Published by: vikaspatanker

Authors: Aarvik Bhatnagar

Abstract: Effective financial governance is critical for ensuring accuracy, transparency, compliance, and risk mitigation in enterprise resource planning (ERP) systems. SAP ERP provides robust financial and control functionalities; however, traditional governance mechanisms largely depend on static rule-based controls and manual audits, which are increasingly insufficient in handling high-volume, complex, and dynamic financial transactions. This paper proposes an intelligent financial governance approach for SAP ERP systems using hybrid machine learning models that combine rule-based logic, statistical methods, and advanced machine learning techniques. The proposed framework integrates seamlessly with SAP financial modules to enable real-time monitoring, anomaly detection, predictive risk assessment, and continuous compliance management. By leveraging hybrid model architectures, the approach balances adaptability and learning capability with transparency and regulatory interpretability. Practical use cases, including fraud detection, compliance monitoring, and predictive financial controls, demonstrate the effectiveness of the proposed solution. Experimental evaluation highlights the superiority of hybrid models over traditional rule-based and standalone machine learning approaches in terms of detection accuracy, false-positive reduction, and operational scalability. The findings indicate that hybrid machine learning models can transform financial governance in SAP ERP from a reactive control function into a proactive, intelligent, and strategic capability.

DOI: http://doi.org/10.5281/zenodo.18159759

Published by: vikaspatanker

Authors: Mehar Bediya

Abstract: The rapid expansion of the Internet of Things (IoT) and the increasing reliance on wireless connectivity have driven the integration of cloud computing into large-scale IoT ecosystems. While cloud-based solutions offer elastic resources and centralized management, the growing number of connected devices, heterogeneous workloads, and dynamic wireless conditions pose significant challenges in terms of scalability and adaptability. Traditional Cloud–IoT architectures often struggle to efficiently accommodate massive device connectivity, fluctuating data rates, and varying quality-of-service requirements. Consequently, there is a growing need for architectural designs that can dynamically scale resources and adapt system behavior in response to changing network and application conditions. This review paper provides a comprehensive analysis of scalable and adaptive Cloud–IoT ecosystem design for wireless networks. It examines foundational architectural models, wireless communication technologies, and cloud computing paradigms that support IoT deployments. The paper further investigates scalability challenges related to device density, data volume, network capacity, and resource provisioning, as well as adaptability mechanisms that enable context-aware, autonomous, and mobility-aware system operation. Key architectural approaches, including edge and fog computing, microservices, containerization, and serverless computing, are reviewed and compared. In addition, the paper discusses resource management, orchestration strategies, and critical considerations related to security, privacy, and reliability. Through a comparative analysis of existing solutions and an exploration of application domains, the review identifies current limitations, trade-offs, and open research challenges. The paper aims to guide researchers and practitioners in designing resilient, efficient, and future-ready Cloud–IoT ecosystems for dynamic wireless environments.

DOI: http://doi.org/10.5281/zenodo.18159755

Published by: vikaspatanker