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Journal articles on the topic 'Predictive Compliance Analytics'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Mohamad, Fayazoddin. "AI-Enhanced Regulatory Compliance in Pharmacies: A Predictive Analytics Approach." International Journal of Science and Research (IJSR) 11, no. 1 (2022): 1704–8. https://doi.org/10.21275/sr220113091642.

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Praveen, Kumar Tammana. "Risk Mitigation Through Predictive SLA Management in Pega Systems." European Journal of Advances in Engineering and Technology 7, no. 12 (2020): 39–44. https://doi.org/10.5281/zenodo.10889978.

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<strong>ABSTRACT</strong> This paper examines the critical role of Service-Level Agreements (SLAs) in ensuring optimal business operations and customer satisfaction. It introduces Pega systems as an advanced tool for effective SLA management, highlighting its capabilities in overseeing complex business processes. Central to this discussion is the innovative role of predictive analytics in Pega systems. The paper explores how predictive analytics can proactively identify risks of SLA breaches, enabling organizations to implement timely mitigation strategies. By integrating these advanced analyt

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Azubuike, John Ikechukwu. "The Role of Predictive Analytics in Automating Risk Management and Regulatory Compliance in the U.S. Financial Sector." European Journal of Accounting, Auditing and Finance Research 12, no. 10 (2024): 19–31. http://dx.doi.org/10.37745/ejaafr.2013/vol12n101931.

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The increasing complexity of regulatory requirements and the dynamic nature of risks in the U.S. financial sector have created significant challenges for financial institutions. These institutions are under growing pressure to manage risks more effectively while ensuring strict compliance with evolving regulatory standards. Traditional risk management and compliance methods, often reliant on manual processes, have proven to be inadequate in addressing the complexities of the modern financial environment. In response, predictive analytics has emerged as a powerful tool capable of processing lar

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Yogesh Gadhiya. "Predictive Analytics for Managing Drug and Alcohol Testing Risks." Kuwait Journal of Advanced Computer Technology 1, no. 1 (2025): 01–17. https://doi.org/10.52783/kjact.264.

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Drug and alcohol testing programs are critical for ensuring workplace safety and compliance with legal standards. However, the current methodologies face significant challenges, including inefficiencies, high costs, and compliance risks. Predictive analytics offers a transformative approach to identifying and mitigating these risks through data-driven insights. This paper explores the integration of predictive analytics into drug and alcohol testing, focusing on risk prediction, model development, and deployment strategies. The research highlights key advancements in machine learning, data pre

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Mahmudova, Irina N. "Process analytics in the compliance control system." Vestnik of Samara University. Economics and Management 16, no. 1 (2025): 63–73. https://doi.org/10.18287/2542-0461-2025-16-1-63-73.

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The article examines the problem of personnel risks of the organization, as part of the economic security system of the organization. For its implementation, it is advisable to organize the functioning of the compliance system on a permanent basis. The article reveals its essence and the main areas of its activity and new analysis tools — process analytics. Predictive analytics and its software products, capable of identifying fraud and predicting the behavior of individuals, are becoming a comprehensive solution for ensuring control over remote employees. In this toolbox, a special place is g

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Odetunde, Azeez, Bolaji Iyanu Adekunle, and Jeffrey Chidera Ogeawuchi. "Using Predictive Analytics and Automation Tools for Real-Time Regulatory Reporting and Compliance Monitoring." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 2 (2022): 650–61. https://doi.org/10.54660/.ijmrge.2022.3.2.650-661.

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In today’s complex and dynamic regulatory environment, financial and insurance institutions face increasing pressure to ensure compliance across multiple jurisdictions in real-time. The growing volume and sophistication of regulatory requirements necessitate the integration of advanced technological solutions to enhance the efficiency and effectiveness of compliance programs. This explores the use of predictive analytics and automation tools for real-time regulatory reporting and compliance monitoring. Predictive analytics harnesses large datasets and machine learning algorithms to anticipate

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Edith Ebele Agu, Njideka Rita Chiekezie, Angela Omozele Abhulimen, and Anwuli Nkemchor Obiki-Osafiele. "Building sustainable business models with predictive analytics: Case studies from various industries." International Journal of Advanced Economics 6, no. 8 (2024): 394–406. http://dx.doi.org/10.51594/ijae.v6i8.1436.

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Predictive analytics has emerged as a powerful tool for businesses across various industries to build sustainable business models. This review provides insights into the significance of predictive analytics in fostering sustainability and showcases case studies from different sectors where predictive analytics has been effectively employed. Predictive analytics enables businesses to anticipate future trends, identify potential risks, and make data-driven decisions, thereby enhancing operational efficiency, improving customer experiences, and driving growth. By leveraging historical data and ad

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Shree, Chand Chhimpa. "Predictive Analytics in Financial Forecasting: Methods, Applications, and Challenges." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.10673796.

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Predictive analytics plays a crucial role in financial forecasting, offering organizations the ability to anticipate future trends, mitigate risks, and make data-driven decisions. This paper provides an in-depth exploration of predictive analytics in financial forecasting, covering methods, applications, challenges, and emerging trends. Through case studies and empirical examples, we illustrate the practical applications and tangible benefits of predictive analytics across various industries, including retail, banking, and telecommunications. We discuss key methodologies such as regression ana

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Vivian Ofure Eghaghe, Olajide Soji Osundare, Chikezie Paul-Mikki Ewim, and Ifeanyi Chukwunonso Okeke. "Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks." Finance & Accounting Research Journal 6, no. 10 (2024): 1893–925. http://dx.doi.org/10.51594/farj.v6i10.1644.

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The growing complexity of financial crimes necessitates advanced Anti-Money Laundering (AML) strategies that leverage data analytics to improve regulatory compliance in banks. As traditional AML methods face challenges in detecting sophisticated money laundering schemes, data analytics offers transformative solutions by enabling real-time monitoring, enhanced risk detection, and predictive analysis. This review explores the integration of data analytics in AML systems and its impact on regulatory compliance, focusing on strategies that banks can adopt to mitigate risks and adhere to evolving r

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Gandhi, Anup Kumar. "Redefining ESG Compliance with Machine Learning and Predictive Analytics." International Journal of AI, BigData, Computational and Management Studies 6 (2025): 66–74. https://doi.org/10.63282/3050-9416.ijaibdcms-v6i2p108.

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11

Valli, Latha Narayanan. "A succinct synopsis of predictive analysis applications in the contemporary period." International Journal of Multidisciplinary Sciences and Arts 3, no. 4 (2024): 26–36. http://dx.doi.org/10.47709/ijmdsa.v3i4.4625.

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A potent subset of data analytics called predictive analytics is revolutionizing a number of industries by using historical data, machine learning methods, and statistical algorithms to predict future events and guide strategic choices. The uses and advantages of predictive analytics in the fields of finance, healthcare, manufacturing, energy and utilities, retail, and marketing are highlighted in this thorough overview. Predictive models improve market risk management, fraud detection, and credit risk assessment in the financial sector, promoting stability and confidence. Applications in heal

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Chianumba, Ernest Chinonso, Nura Ikhalea, Ashiata Yetunde Mustapha, Adelaide Yeboah Forkuo, and Damilola Osamika. "Developing a Predictive Model for Healthcare Compliance, Risk Management, and Fraud Detection Using Data Analytics." International Journal of Social Science Exceptional Research 1, no. 1 (2022): 232–38. https://doi.org/10.54660/ijsser.2022.1.1.232-238.

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This paper explores the application of predictive analytics in enhancing healthcare compliance, risk management, and fraud detection. With the increasing complexity of healthcare systems, ensuring compliance with regulatory standards and preventing fraud has become an essential aspect of organizational governance. The study examines how machine learning, artificial intelligence (AI), and other data-driven techniques can be utilized to predict and mitigate risks before they escalate into costly compliance violations or fraudulent activities. It provides a detailed review of the regulatory frame

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Emmanuel Paul-Emeka George, Courage Idemudia, and Adebimpe Bolatito Ige. "Predictive analytics for financial compliance: Machine learning concepts for fraudulent transaction identification." Open Access Research Journal of Multidisciplinary Studies 8, no. 1 (2024): 015–25. http://dx.doi.org/10.53022/oarjms.2024.8.1.0041.

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Predictive analytics has emerged as a pivotal tool in financial compliance, offering sophisticated methods for identifying fraudulent transactions through the application of machine learning (ML) concepts. As financial institutions grapple with increasingly complex fraud schemes and stringent regulatory requirements, the integration of predictive analytics with ML provides a proactive approach to fraud detection and prevention. Machine learning algorithms excel in analyzing vast datasets, identifying hidden patterns, and making real-time predictions. In the realm of financial compliance, super

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Adeleke Damilola Adekola and Samuel Ajibola Dada. "Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework." Computer Science & IT Research Journal 5, no. 11 (2024): 2580–93. http://dx.doi.org/10.51594/csitrj.v5i11.1709.

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The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addre

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Olawale, Habeeb Olatunji, Ngozi Joan Isibor, and Joyce Efekpogua Fiemotongha. "A Predictive Compliance Analytics Framework Using AI and Business Intelligence for Early Risk Detection." International Journal of Management and Organizational Research 2, no. 2 (2023): 190–95. https://doi.org/10.54660/ijmor.2023.2.2.190-195.

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In an era of escalating regulatory complexity and costly compliance breaches, this paper proposes a predictive compliance analytics framework that harnesses artificial intelligence and business intelligence tools to enable early risk detection in financial and insurance sectors. By integrating machine learning models with interactive dashboard platforms such as Tableau, SQL, and Python, the framework transforms traditional reactive compliance approaches into a proactive, data-driven system. The framework incorporates diverse data sources, advanced algorithms, and real-time visualization to ide

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16

Jay Shah and Dr Amit Kumar Jain. "Scaling Cloud Data Platforms for Compliance Analytics: A Strategic Approach for the Pharmaceutical Industry." Universal Research Reports 12, no. 1 (2025): 288–96. https://doi.org/10.36676/urr.v12.i1.1483.

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In today’s rapidly evolving pharmaceutical landscape, ensuring compliance with complex regulatory requirements while harnessing innovative data analytics is paramount. This study explores a strategic approach to scaling cloud data platforms for compliance analytics tailored specifically for the pharmaceutical industry. By integrating advanced cloud computing technologies with robust analytics frameworks, the proposed model addresses critical challenges such as data security, regulatory adherence, and real-time monitoring of compliance metrics. The methodology involves the deployment of scalabl

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Amazing, Hope Ekeh, Elachi Apeh Charles, Somtochukwu Odionu Chinekwu, and Austin-Gabriel Blessing. "Automating Legal Compliance and Contract Management: Advances in Data Analytics for Risk Assessment, Regulatory Adherence, and Negotiation Optimization." Engineering and Technology Journal 10, no. 01 (2025): 3684–703. https://doi.org/10.5281/zenodo.14777107.

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The integration of data analytics into legal compliance and contract management is transforming traditional processes by automating risk assessments, enhancing regulatory adherence, and optimizing negotiations. This paper reviews state-of-the-art applications of advanced analytics, focusing on technologies such as predictive analytics, machine learning, and natural language processing (NLP). These tools enable organizations to streamline contract drafting, detect compliance risks in real-time, and derive actionable insights to enhance negotiation strategies. The proposed framework leverages pr

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Godwin Ozoemenam Achumie, Oluwaseun Adeola Bakare, and Njideka Ihuoma Okeke. "Implementing fair lending practices: Advanced data analytics approaches and regulatory compliance." Finance & Accounting Research Journal 6, no. 10 (2024): 1818–31. http://dx.doi.org/10.51594/farj.v6i10.1623.

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Implementing fair lending practices is crucial for financial institutions to ensure equal access to credit and comply with regulatory requirements. Advanced data analytics approaches offer powerful tools for detecting and mitigating potential biases in lending decisions. This paper provides a comprehensive framework for leveraging advanced data analytics techniques to enhance fair lending practices and maintain regulatory compliance. The review begins by outlining the importance of fair lending and the role of advanced data analytics in achieving this goal. It then discusses the regulatory lan

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19

PANDEY, SWASTIKA. "USER PERCEPTION TOWARDS PREDICTIVE ANALYTICS IN CREDIT RISK MANAGEMENT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32730.

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This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical

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Wilhelmina Afua Addy, Chinonye Esther Ugochukwu, Adedoyin Tolulope Oyewole, Onyeka Chrisanctus Ofodile, Omotayo Bukola Adeoye, and Chinwe Chinazo Okoye. "Predictive analytics in credit risk management for banks: A comprehensive review." GSC Advanced Research and Reviews 18, no. 2 (2024): 434–49. http://dx.doi.org/10.30574/gscarr.2024.18.2.0077.

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This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical

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Wilhelmina, Afua Addy, Esther Ugochukwu Chinonye, Tolulope Oyewole Adedoyin, Chrisanctus Ofodile Onyeka, Bukola Adeoye Omotayo, and Chinazo Okoye Chinwe. "Predictive analytics in credit risk management for banks: A comprehensive review." GSC Advanced Research and Reviews 18, no. 2 (2024): 434–49. https://doi.org/10.5281/zenodo.11216644.

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This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical

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Musa, Zainab I., Sahalu Balarabe Junaidu, Baroon Ismaeel Ahmad, A. F. Donfack Kana, and Adamu Abubakar Ibrahim. "An Enhanced Predictive Analytics Model for Tax-Based Operations." International Journal on Perceptive and Cognitive Computing 9, no. 1 (2023): 44–49. http://dx.doi.org/10.31436/ijpcc.v9i1.343.

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In order to meet its basic responsibilities of governance such as provision of infrastructure, governments world over require significant amount of funds. Consequently, citizens and businesses are required to pay certain legislated amounts as taxes and royalties. However, tax compliance and optimal revenue generation remains a major source of concern. Measures such as penalties and in the current times Data and Predictive Analytics have been devised to curb these issues. Such effective Analytics measures are absent in Bauchi State and Nigeria as a whole. Previous studies in Nigeria have done m

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Katragadda, Mani Kiran Chowdary. "Integrating Predictive Analytics with Core Banking Systems: Lessons from PenFed and IFC." European Journal of Accounting, Auditing and Finance Research 13, no. 4 (2025): 59–71. https://doi.org/10.37745/ejaafr.2013/vol13n45971.

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The integration of predictive analytics with core banking systems represents a transformative approach for financial institutions seeking to enhance operational efficiency, risk management, and customer experience. This article examines key considerations in this integration process, drawing insights from the PenFed Credit Union's PANGEN Project for credit card processing and the International Finance Corporation's iPortal and iDesk applications. This article explores essential factors, including data quality frameworks, system interoperability challenges, scalability requirements, regulatory

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Chibuzor Njoku, Genevieve Okafor, Ehisuoria E. Akhuemonkhan, Ifeoma Naibe, and Aniel K. Diala. "Leveraging data analytics for fraud detection: The future of financial risk mitigation and regulatory compliance." Computer Science & IT Research Journal 6, no. 2 (2025): 86–93. https://doi.org/10.51594/csitrj.v6i2.1859.

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The increasing complexity of financial fraud schemes has necessitated the adoption of advanced data analytics, AI-driven fraud detection models, and forensic accounting tools to strengthen corporate fraud prevention and regulatory compliance. Traditional fraud detection techniques have proven inadequate in identifying sophisticated financial crimes, prompting organizations to integrate predictive analytics, machine learning algorithms, and real-time transaction monitoring systems to mitigate fraud risks. This paper examines how data analytics enhances financial risk mitigation, the role of AI

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Pendyala, Santhosh Kumar. "Healthcare Value-Based Reimbursement: A Predictive Analytics and Machine Learning Framework for Cost Optimization and Quality Improvement." International Journal of Advanced Robotics and Automation 7, no. 1 (2024): 1–9. https://doi.org/10.15226/2473-3032/7/1/00143.

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Value-based reimbursement (VBR) models are revolutionizing healthcare by prioritizing quality outcomes and cost efficiency over service volume. However, their success hinges on the effective integration of healthcare data, advanced predictive analytics, and machine learning (ML) models to address cost forecasting, risk stratification, and compliance with HEDIS (Healthcare Effectiveness Data and Information Set) measures. This study proposes a robust framework leveraging cloud platforms, AI-driven analytics, and scalable data integration solutions to address these needs. Utilizing tools like AW

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Katta, Bujjibabu. "Leveraging AI, ML, and LLMs for Predictive Trade Analytics and Automated Metadata Management." European Journal of Computer Science and Information Technology 13, no. 30 (2025): 78–92. https://doi.org/10.37745/ejcsit.2013/vol13n307892.

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The integration of Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) has revolutionized trade data analytics and metadata management within cloud environments. The implementation of advanced predictive models, coupled with sophisticated cloud architectures, enables organizations to process vast amounts of heterogeneous data while delivering real-time insights for strategic decision-making. The architecture encompasses multiple layers of data processing, including event-driven systems for trade pattern recognition, automated metadata extraction, and intellige

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Mani Kiran Chowdary Katragadda. "Innovations in predictive analytics and banking systems integration." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 780–87. https://doi.org/10.30574/wjaets.2025.15.1.0273.

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This technical article examines the transformative impact of predictive analytics and banking systems integration on the financial sector, with a particular focus on two exemplary implementations: PenFed Credit Union's PANGEN Project for credit card processing and the International Finance Corporation's iPortal and iDesk applications. This article explores how these innovations enhance core banking functionalities through advanced risk assessment algorithms, personalized credit offerings, cloud-based architectures, and API-driven integration. Additionally, the article investigates how AI-drive

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Ruthvik Uppaluri. "Securing Cloud-Based Financial Systems with AI-Powered Predictive Analytics." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2695–705. https://doi.org/10.32628/cseit251112293.

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This research aims to discover the impact that incorporates predictive analytics technology that delivers an AI concept in secure cloud financial systems. These systems use machine learning algorithms, behavioural analytics and neural networks to detect threats, provide faster response and control frauds. This study shows that threat detection has increased in accuracy, false positives have decreased, and reaction time has improved along with qualitative cost savings recognized in the compliance and fraud prevention activities. AI brings value to cybersecurity; it has some drawbacks, which enc

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Rodrigues, Nilima James. "Predictive Analytics and Artificial Intelligence: Advancing Business Analytics in the Medical Devices Industry." European Journal of Computer Science and Information Technology 13, no. 41 (2025): 75–90. https://doi.org/10.37745/ejcsit.2013/vol13n417590.

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Predictive analytics and artificial intelligence are transforming business processes across the medical device industry, enabling more sophisticated decision-making and operational excellence. This content explores key applications of these technologies across financial planning, demand forecasting, customer analytics, and supply chain management domains. The integration of advanced algorithms with domain-specific data streams allows medical device manufacturers to anticipate market shifts, optimize inventory positions, personalize customer engagement, and build resilient supply networks. Whil

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Manikyala, Aditya, Rajasekhar Reddy Talla, Pavan Kumar Gade, and Satya Surya MKLG Gudimetla Naga Venkata. "Implementing AI in SAP GTS for Symmetric Trade Analytics and Compliance." American Journal of Trade and Policy 11, no. 1 (2024): 31–38. https://doi.org/10.18034/ajtp.v11i1.733.

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SAP Global Trade Services (GTS) uses AI to improve symmetric trade analytics and compliance management. The main goal is to examine how machine learning, natural language processing, and predictive analytics may enhance global trade compliance accuracy, flexibility, and efficiency. Secondary data, including peer-reviewed academic publications, industry reports, and case studies, is analyzed to assess SAP GTS AI integration. Significant results show that AI automates risk assessments, detects abnormalities, and adapts to real-time regulatory changes, improving compliance. AI's symmetric trade a

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Guan, Ran. "Future Era of Accountants under the Impact of AI." SHS Web of Conferences 218 (2025): 03028. https://doi.org/10.1051/shsconf/202521803028.

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The integration of Artificial Intelligence (AI) in accounting is transforming the profession by automating tasks such as fraud detection, financial forecasting, and risk assessment, enhancing efficiency and accuracy. As AI reshapes the industry, accountants must develop expertise in data analytics, predictive modeling, and cybersecurity to remain competitive. CPA Canada and AICPA have incorporated AI governance and digital risk management into certification programs to equip accountants for this shift. The Big Four accounting firms—Deloitte, PwC, KPMG, and EY—are leading AI adoption, implement

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Adetoro, Adeyanju. "Data Analytics and Machine Learning: Revolutionizing Fire Safety and Compliance for U.S. Fire Departments." International Journal of Scientific and Management Research 04, no. 04 (2021): 151–67. http://dx.doi.org/10.37502/ijsmr.2021.4411.

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This study explores the transformative impact of data analytics and machine learning on fire safety and compliance for U.S. fire departments. Leveraging advanced techniques such as predictive modeling, risk assessment, and geographic information systems (GIS), the research highlights how these tools can enhance decision-making, optimize resource allocation, and improve fire prevention and response strategies. The study identifies key correlations between fire incidents and factors such as weather conditions, building characteristics, and prevention measures, emphasizing the role of data-driven

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Olanipekun Ph.D, Comfort Temidayo. "Revolutionizing Audit Quality Process: The Dynamic Influence of Big Data Analytics on the Digital Transformation of Deposit Money Banks in Nigeria." International Journal of Research and Innovation in Social Science IX, no. IV (2025): 6030–45. https://doi.org/10.47772/ijriss.2025.90400432.

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This study investigates the impact of Big Data analytics on audit quality within Nigerian deposit money banks, focusing on risk assessment, fraud detection, predictive analytics, and compliance monitoring. The research method employed in this study is a cross-sectional survey research design. This design involved gathering data through structured questionnaires distributed among auditors and professionals working within Nigerian deposit money banks. The sample size of 384 respondents was determined using the Cochran formula, with participants selected through purposive and simple random sampli

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Raja Wahab, Raja Azhan Syah, and Azuraliza Abu Bakar. "Digital Economy Tax Compliance Model in Malaysia using Machine Learning Approach." Sains Malaysiana 50, no. 7 (2021): 2059–77. http://dx.doi.org/10.17576/jsm-2021-5007-20.

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The field of digital economy income tax compliance is still in its infancy. The limited collection of government income taxes has forced the Inland Revenue Board of Malaysia (IRBM) to develop a solution to improve the tax compliance of the digital economy sector so that its taxpayers may report voluntary income or take firm action. The ability to diagnose the taxpayer's compliance will ensure the IRBM effectively collects the income tax and gives revenues to the country. However, it gives challenges in extracting necessary knowledge from a large amount of data, leading to the need for a predic

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Elebe, Okeoghene, Chikaome Chimara Imediegwu, and Opeyemi Morenike Filani. "Predictive Analytics in Revenue Cycle Management: Improving Financial Health in Hospitals." Journal of Frontiers in Multidisciplinary Research 2, no. 1 (2021): 334–45. https://doi.org/10.54660/.ijfmr.2021.2.1.334-345.

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The escalating financial pressures on hospitals—driven by rising operational costs, complex reimbursement structures, and increasing patient financial responsibility—have intensified the need for innovative solutions in revenue cycle management (RCM). Predictive analytics, leveraging machine learning, artificial intelligence (AI), and advanced statistical techniques, has emerged as a transformative tool for enhancing financial performance in healthcare organizations. Thisexplores the pivotal role of predictive analytics in optimizing RCM processes to improve hospitals’ financial health. By ana

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Shivakrishna Bade. "The Role of MLOps in Healthcare: Enhancing Predictive Analytics and Patient Outcomes." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1507–15. https://doi.org/10.32628/cseit25112501.

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This comprehensive article explores the transformative role of Machine Learning Operations (MLOps) in healthcare, focusing on its impact on predictive analytics and patient outcomes. The article examines how healthcare organizations leverage MLOps frameworks to enhance model deployment, maintain regulatory compliance, and improve clinical decision-making processes. The article investigates the evolution of machine learning in healthcare, analyzing core components of healthcare MLOps implementation, including data pipeline management, model development, and monitoring systems. The article also

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Zhao, Yue. "Empowering Sustainable Finance: The Convergence of AI, Blockchain, and Big Data Analytics." Advances in Economics, Management and Political Sciences 85, no. 1 (2024): 267–73. http://dx.doi.org/10.54254/2754-1169/85/20240925.

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This paper explores the transformative impact of artificial intelligence (AI), blockchain technology, and big data analytics on the sustainable finance sector. These technologies are driving significant advancements in decision-making, regulatory compliance, socially responsible investing (SRI), transparency, efficiency, risk management, financial inclusion, and the identification of sustainable growth opportunities. AI enhances predictive analysis and automates ESG compliance, fostering informed investment strategies and ensuring adherence to sustainability standards. Blockchain introduces un

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Masengu, Reason, Chenjerai Muchenje, and Benson Ruzive. "Leveraging predictive analytics to enhance food safety risk management in supply chains: A conceptual framework." Journal of Infrastructure, Policy and Development 9, no. 1 (2025): 10114. https://doi.org/10.24294/jipd10114.

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Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply c

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Eyitayo Raji, Tochukwu Ignatius Ijomah, and Osemeike Gloria Eyieyien. "Data-Driven decision making in agriculture and business: The role of advanced analytics." Computer Science & IT Research Journal 5, no. 7 (2024): 1565–75. http://dx.doi.org/10.51594/csitrj.v5i7.1275.

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Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarl

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Meda, Raviteja. "Machine Learning Models for Quality Prediction and Compliance in Paint Manufacturing Operations." International Journal of Engineering and Computer Science 8, no. 12 (2019): 24993–11. https://doi.org/10.18535/ijecs.v8i12.4445.

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In recent years, the application of machine learning models in industrial realms has proliferated, introducing groundbreaking methodologies for quality assurance and compliance, particularly within paint manufacturing operations. This paper delves into the intricate landscape of predictive modeling for quality control in paint manufacturing, underscoring the potential of machine learning algorithms to enhance efficiency and precision in this sector. By harnessing various data-driven techniques, the study explores a multifaceted approach that unifies diverse datasets, enabling the accurate pred

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Aishwarya Umesh Pai. "Integrated healthcare predictive analytics framework: From patient data to clinical insights." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1285–97. https://doi.org/10.30574/wjaets.2025.15.3.1031.

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This article examines the transformative role of machine learning in predictive healthcare analytics, exploring how advanced computational techniques are revolutionizing healthcare delivery through proactive rather than reactive approaches to patient management. The article systematically investigates the methodological foundations of health prediction, including regression techniques, classification approaches, deep learning architectures, and ensemble methods, evaluating their relative strengths and implementation considerations across various clinical contexts. Key clinical applications are

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Shravan Kumar Joginipalli. "Predictive analytics for catastrophic risk management: Leveraging telematics and IoT data in property insurance." International Journal of Science and Research Archive 5, no. 2 (2022): 387–91. https://doi.org/10.30574/ijsra.2022.5.2.0076.

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Catastrophic risk management in property insurance demands proactive strategies to mitigate losses from natural disasters such as hurricanes, wildfires, and floods. Traditional methods often lack real-time data integration, leading to delayed responses and suboptimal risk assessments. This paper proposes a predictive analytics framework that leverages telematics and IoT data to enhance catastrophic risk prediction and management. By integrating real-time sensor data, historical weather patterns, and geographic information systems (GIS), the framework employs machine learning models to forecast

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Researcher. "TRANSFORMING HEALTHCARE THROUGH DATA ENGINEERING, PREDICTIVE ANALYTICS, AND AI MODELS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1710–18. https://doi.org/10.5281/zenodo.14265470.

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This article explores how artificial intelligence, predictive analytics, and data engineering have revolutionized contemporary healthcare systems. It investigates how these integrated technologies transform clinical decision-making procedures, operational effectiveness, and patient care delivery. The article examines several topics, such as the fundamentals of data engineering, predictive analytics, AI model architectures, the integration of real-time analytics, and clinical applications. With an emphasis on technical difficulties, data quality control, system integration, and regulatory compl

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Nafisat Temilade Popoola and Felix Adebayo Bakare. "Advanced computational forecasting techniques to strengthen risk prediction, pattern recognition, and compliance strategies." International Journal of Science and Research Archive 12, no. 2 (2024): 3033–54. https://doi.org/10.30574/ijsra.2024.12.2.1412.

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In an era defined by data-driven decision-making, advanced computational forecasting techniques have emerged as powerful tools for strengthening risk prediction, pattern recognition, and compliance strategies. These techniques leverage artificial intelligence (AI), machine learning (ML), and big data analytics to enhance accuracy, efficiency, and reliability in risk assessment across diverse industries. Traditional risk prediction models often rely on historical data and statistical methods, which, while effective, struggle to capture complex, non-linear patterns in evolving datasets. Advanced

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Researcher. "How to Implement Predictive Analytics in the Cash Management Process of Small and Medium Banks." International Journal of Computer Science and Information Technology Research (IJCSITR) 6, no. 2 (2025): 9–27. https://doi.org/10.5281/zenodo.15009771.

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<em>Predictive analytics is transforming cash management for small and medium banks (SMBs) by enhancing forecasting accuracy, optimizing liquidity management, and improving regulatory compliance. This paper explores how predictive analytics leverages historical data, machine learning models, and real-time analysis to provide actionable insights into cash flow trends. Key benefits include reduced liquidity risks, improved fund allocation, enhanced fraud detection, and better customer service. By integrating predictive models, SMBs can make data-driven decisions that enhance operational efficien

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Venkat Mounish Gundla. "Navigating privacy and compliance in healthcare analytics: Core concepts explained." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1029–36. https://doi.org/10.30574/wjarr.2025.26.2.1696.

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Healthcare analytics has emerged as a transformative force in modern medicine, with the global predictive analytics market projected to reach substantial growth by the early part of the next decade. This remarkable expansion occurs within a complex regulatory environment designed to protect sensitive patient information while enabling valuable insights. The intersection of healthcare data, advanced analytics, and regulatory compliance presents unique challenges for practitioners, particularly those new to the field. This article provides a comprehensive foundation for understanding the core co

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Mathew, Alex. "The 5 Cs of Cybersecurity and its Integration with Predictive Analytics." International Journal of Computer Science and Mobile Computing 12, no. 1 (2023): 47–50. http://dx.doi.org/10.47760/ijcsmc.2022.v12i01.006.

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This research paper explores the 5 Cs of the Cybersecurity Framework and how predictive analytics can be used to enhance its effectiveness. Change, compliance, cost, continuity, and coverage are the 5 Cs of cybersecurity, which are crucial factors that must be taken into account to secure the security of an organization's data and systems. Predictive analytics is a powerful tool that can provide organizations with advanced warnings of potential threats and enable them to take proactive measures to mitigate them. The proposed algorithm for integrating predictive analytics with the 5 Cs of Cyber

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Wokoma, Esther Mmabong, and Patrick Adeel. "Real-Time Predictive Analytics in Multi-Phase Flow Metering for Offshore Pipelines: A Machine Learning Approach." International Journal of Petroleum and Gas Exploration Management 7, no. 1 (2024): 34–54. http://dx.doi.org/10.37745/ijpgem.15/vol7n13454.

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This research investigates the deployment of real-time predictive analytics in multi-phase flow metering systems for offshore pipelines, with a focus on Nigeria’s oil and gas industry. Offshore operations in Nigeria, a major global oil producer, are often confronted with challenging environmental conditions and the complexities of multi-phase flows—simultaneous flows of oil, water, and gas within pipelines. These factors complicate the accuracy and efficiency of traditional flow measurement systems, leading to potential operational inefficiencies, heightened environmental risks, and costly reg

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Pendyala, Santhosh Kumar. "Advanced Healthcare Data Analytics: A Cloud- AI Integrated Framework for Enhanced Clinical Outcomes." International Journal of Advanced Robotics and Automation 7, no. 1 (2024): 1–8. https://doi.org/10.15226/2473-3032/7/1/00144.

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The integration of cloud computing and artificial intelligence (AI) in healthcare data analytics has revolutionized patient care by enabling realtime, data-driven decision-making. Recent studies demonstrate significant improvements in predictive diagnostics and treatment personalization through such integrations. For instance, the implementation of AI-driven analytics within cloud infrastructures has led to a 72.3% enhancement in data processing efficiency, facilitating timely clinical interventions. Moreover, the adoption of machine learning algorithms in cloud-based systems has achieved pred

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Adeleke Damilola Adekola and Samuel Ajibola Dada. "Harnessing predictive analytics to enhance medication adherence: A strategic model for public health impact." Open Access Research Journal of Life Sciences 8, no. 2 (2024): 008–16. http://dx.doi.org/10.53022/oarjls.2024.8.2.0034.

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This review paper explores the critical role of predictive analytics in enhancing medication adherence, a significant challenge impacting global public health and healthcare systems. Medication non-adherence is linked to increased healthcare costs, poor health outcomes, and reduced quality of life for patients. By leveraging predictive analytics, healthcare providers can identify patients at risk of non-compliance, enabling targeted interventions that address individual barriers to adherence. The paper outlines a strategic framework for integrating predictive analytics into healthcare systems,

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