Academic literature on the topic 'Predictive Compliance Analytics'
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Journal articles on the topic "Predictive Compliance Analytics"
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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 analytical tools, Pega systems not only enhance SLA compliance but also significantly reduce the potential operational and reputational risks associated with SLA failures.
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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 large volumes of structured and unstructured data to provide actionable insights. Predictive analytics leverages machine learning algorithms, statistical models, and real-time data analysis to identify potential risks and ensure adherence to regulatory frameworks proactively. This paper provides a comprehensive examination of the role predictive analytics plays in automating key aspects of risk management and regulatory compliance in the U.S. financial sector. It explores how predictive models can be used to forecast risks, detect anomalies, and enhance decision-making processes, enabling institutions to anticipate and address risks before they manifest into significant issues. Additionally, the paper reviews existing literature on predictive analytics, highlighting key advancements in its application within financial institutions, particularly in areas such as credit risk assessment, fraud detection, and compliance reporting. To further illustrate the effectiveness of predictive analytics, the paper includes a detailed case study of its implementation in a leading U.S. financial institution. The case study showcases how predictive analytics has optimized risk management workflows, reduced compliance costs, and mitigated potential risks by providing early warnings of regulatory breaches and operational inefficiencies. Through the application of predictive analytics, the institution was able to achieve greater accuracy in risk forecasting, improve regulatory reporting, and streamline internal compliance processes. The research also delves into the broader benefits of predictive analytics, such as enhanced operational efficiency, improved resource allocation, and cost reduction. Moreover, it discusses the challenges associated with implementing predictive analytics, including data integration, model accuracy, and the need for continuous updates to account for changing regulatory landscapes and market conditions. The paper concludes with recommendations for financial institutions looking to adopt predictive analytics, emphasizing the importance of robust data governance frameworks, cross-functional collaboration, and investment in advanced technological infrastructure to maximize the potential of predictive analytics in risk management and regulatory compliance. This research provides valuable insights into how predictive analytics can transform the risk management and compliance landscape for U.S. financial institutions, offering a forward-looking solution to one of the most pressing issues in the industry today.
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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 preprocessing, and ethical considerations to optimize testing protocols and enhance operational efficiency.
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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 given to the workstation booking system.
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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 risks, detect anomalies, and predict potential compliance violations before they materialize. This enables organizations to proactively address issues, reducing the risk of non-compliance and regulatory penalties. Automation tools streamline repetitive compliance tasks such as data collection, transaction monitoring, and regulatory report generation, ensuring accuracy and timeliness while freeing up resources for more strategic activities. By integrating predictive analytics with automation, institutions can achieve more comprehensive and agile compliance programs that automatically adapt to regulatory changes and evolving risks. This also discusses the benefits of these technologies, including improved accuracy, cost savings, and enhanced regulatory confidence. However, challenges such as data quality, technological integration, and navigating complex multi-jurisdictional regulations are also addressed. Best practices for successful implementation, including regular testing of predictive models, collaboration between compliance and IT teams, and ensuring real-time monitoring frameworks, are provided. Looking ahead, this highlights future trends in predictive analytics, such as the use of AI and machine learning, and the potential of blockchain for real-time compliance reporting. Ultimately, the integration of predictive analytics and automation tools represents a significant opportunity for institutions to optimize their compliance functions and stay ahead of regulatory demands.
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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 advanced statistical algorithms, organizations can gain valuable insights into consumer behavior, market dynamics, and operational processes. In the retail industry, predictive analytics facilitates customer segmentation for targeted marketing campaigns and optimizes inventory management through demand forecasting. These initiatives result in increased revenue, reduced costs, and improved customer satisfaction. In the healthcare sector, predictive analytics aids in disease prediction and prevention, enabling early detection of health risks and proactive interventions. Additionally, predictive models optimize hospital resource management, leading to enhanced operational efficiency and patient outcomes. Financial services leverage predictive analytics for credit risk assessment, fraud detection, and personalized financial services. By accurately assessing creditworthiness, detecting fraudulent activities, and offering tailored products, financial institutions mitigate risks, improve regulatory compliance, and enhance customer satisfaction. In the manufacturing sector, predictive analytics is utilized for predictive maintenance and supply chain optimization. Predictive maintenance reduces downtime and maintenance costs by predicting equipment failures, while supply chain optimization improves sourcing, production, and distribution processes, resulting in streamlined operations and increased profitability. Overall, the integration of predictive analytics across industries fosters sustainability by enabling businesses to make informed decisions, optimize resources, mitigate risks, and meet evolving customer needs. These case studies highlight the transformative impact of predictive analytics in building sustainable business models and driving long-term success.. Keywords: Business Model, Predictive Analysis, Case Studies, Industries.
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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 analysis, time series forecasting, and machine learning algorithms, highlighting their role in sales forecasting, stock market prediction, credit risk assessment, and customer churn prediction. Additionally, we examine challenges such as data quality issues, model complexity, and regulatory compliance, and discuss emerging trends such as the integration of artificial intelligence, real-time analytics, and ethical AI practices. By embracing these trends and leveraging advanced analytics techniques, organizations can enhance their predictive capabilities, drive strategic decision-making, and unlock new opportunities for value creation in the dynamic landscape of finance and business.
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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 regulations. Data analytics empowers financial institutions to analyze vast amounts of transactional data, identifying suspicious patterns and anomalies with greater precision. Machine learning algorithms and artificial intelligence (AI) further enhance these capabilities by automating risk assessments, reducing false positives, and improving decision-making processes. Through predictive analytics, banks can anticipate emerging threats, adapting their AML strategies proactively to counter new money laundering techniques. A key advantage of data-driven AML approaches is the ability to streamline compliance processes. By automating Know Your Customer (KYC) procedures and cross-referencing data from multiple sources, banks can efficiently verify customer identities and monitor for unusual behavior. Additionally, the adoption of data analytics improves reporting accuracy, ensuring compliance with stringent regulatory frameworks such as the Financial Action Task Force (FATF) and the Bank Secrecy Act (BSA). This review highlights the transformative role of data analytics in enhancing AML efforts, emphasizing the importance of real-time data integration, predictive modeling, and automation. The shift from reactive to proactive AML approaches not only strengthens regulatory compliance but also fosters a culture of vigilance and risk management within banks. As financial institutions continue to embrace digital transformation, leveraging data analytics for AML will be crucial in combating financial crimes and maintaining compliance in an increasingly complex regulatory environment. Keywords: Anti-Money Laundering (AML), Data Analytics, Regulatory Compliance, Banks, Financial Crime, Machine Learning, Artificial Intelligence, Know Your Customer (KYC), Predictive Analytics, Risk Management.
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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.
Full textBook chapters on the topic "Predictive Compliance Analytics"
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Sykes, Edward R., Jinhe Zhang, and Uri Sevilla. "Assisting Personal Support Worker’s e-Training with AI Prediction." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90341-0_13.
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Abstract The increasing need for effective caregiver training, particularly for Personal Support Workers, has led to the development of innovative e-training platforms. This study explores the application of advanced ML models to predict training outcomes and identify at-risk learners early in the process. The primary goal is to improve training completion rates while ensuring compliance with industry standards. We employed a range of ML models, including Decision Trees, Random Forest, Support Vector Machines, Neural Networks, to predict the likelihood of successful course completion using a dataset comprising over 27 million user interaction records. Feature engineering was used to extract key metrics such as module and lesson completion ratios. The results indicate that the Multilayer Perceptron model performed best, achieving an AUC score of 0.99, while K-NN also demonstrated strong performance with an AUC of 0.98. Key features such as module completion ratio and temporal progress were found to be significant predictors of training success. These findings suggest that integrating predictive analytics into e-training platforms can significantly enhance the effectiveness of PSW certification processes, ultimately supporting the growing demand for skilled caregivers in healthcare.
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Eswaran, Ushaa, Vivek Eswaran, Keerthna Murali, and Vishal Eswaran. "Predictive Healthcare Analytics." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5893-1.ch009.
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The integration of digital twin technology with healthcare systems promises to revolutionize clinical decision-making and patient outcomes in Healthcare 6.0. This chapter explores predictive healthcare analytics' role in preventive care, resource optimization, and patient-centered outcomes. It examines theoretical foundations, methodologies like machine learning, and real-world applications, highlighting predictive maintenance and risk stratification. Ethical considerations and regulatory compliance are emphasized, with a look at future trends. Ultimately, the chapter serves as a guide for stakeholders navigating predictive healthcare analytics in Healthcare 6.0, advocating for proactive, data-driven decision-making and improved patient outcomes.
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Panchapakesan, Ashok, Harishchander Anandaram, Lakshmi Sridevi, et al. "Enhancing Audit Effectiveness Through Strategic Data Analytics." In Advances in Finance, Accounting, and Economics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8186-1.ch009.
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The integration of advanced data analytics into internal audit processes represents a transformative approach to organizational risk management. This exploration examines data analytics methodologies within audit frameworks, addressing technological innovation, operational efficiency, and compliance. Data analytics enables internal audit departments to transition from retrospective, sample-based reviews to comprehensive, real-time risk assessment and predictive modelling. Analyzing applications across financial services, healthcare, technology, and manufacturing reveals consistent benefits. Implementation challenges include technological infrastructure requirements, skill set gaps, data quality concerns, and complex regulatory landscapes. Emerging trends like artificial intelligence, machine learning, and predictive analytics promise to revolutionize internal audit capabilities. Future opportunities focus on developing adaptable data analytics frameworks that can dynamically respond to evolving technological and regulatory environments.
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Nikam, Sarika Tanaji, Babasaheb Jadhav, and Mily Lal. "Predictive Maintenance for Medical Equipment Using AI-Powered Digital Twins." In Accelerating Product Development Cycles With Digital Twins and IoT Integration. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-2028-1.ch007.
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With AI and Digital Twin technology, the way medical equipment maintenance is transformed into reliability improvement, minimization of downtime, and an enhanced life span for critical healthcare assets, the application will provide virtual models of how medical devices function and perform, with the IoT sensor data analyzed using AI algorithms to predict and prevent failures. This chapter provides the methodology for building digital twins, data acquisition through IoT sensors, and AI-based predictive analytics for predicting maintenance needs to prevent sudden breakdowns and high costs of repair. Results show that AI-based digital twins can achieve a high degree of prediction accuracy while optimizing maintenance schedules and reducing downtime and maintenance costs considerably. Comparative analysis also shows superior operational efficiency and very stringent healthcare compliance. This chapter will give healthcare organizations practical advice on implementing predictive maintenance for consistent performance and superior patient care.
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Yusof, Mohd Asyraf bin. "Waqf-Driven Inclusive Prosperity Exploring the Intersection of Islamic Finance, Fintech, and Sustainable Development Goals." In Advances in Finance, Accounting, and Economics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5653-1.ch012.
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Waqf-driven Inclusive Prosperity: Exploring the Intersection of Islamic Finance, Fintech, and Sustainable Development Goals (SDGs) examines how fintech innovations and AI-driven tools are advancing waqf management to foster inclusive economic growth and support the SDGs. The chapter explores the Waqf Analytics Platform for predictive analytics, resource optimization, and impact assessment, alongside fintech advancements such as AI-based fundraising, smart contracts, and digital platforms. It addresses challenges like data privacy, Shariah compliance, and implementation costs, and identifies future research opportunities, including Shariah-compliant AI algorithms and AI blockchain integration. The chapter offers insights into how these technologies can drive waqf initiatives and contribute to sustainable development.
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Jagtap, Vaidehi, and Rudraprasad Epilli. "AI IN FINANCIAL RISK MANAGEMENT." In AI-Powered HR Finance: Transforming Workforce Management and Financial Strategies in the Digital Age. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. https://doi.org/10.58532/nbennurptch11.
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Artificial Intelligence (AI) is transforming financial risk management by enhancing predictive accuracy and operational efficiency. AI-driven credit risk assessment leverages machine learning to evaluate creditworthiness, while predictive analytics offers valuable insights into potential financial risks. Natural Language Processing (NLP) extracts key information from financial documents, aiding in risk identification. Investment risk analysis and liquidity risk management are improved through predictive modeling and AI-driven analytics. Stress testing simulates extreme economic conditions, evaluating financial stability. Regulatory risk mitigation and market anomaly detection ensure compliance and highlight irregular activities. Behavioral risk analysis helps monitor risky practices, and AI enhances data security in transactions. Overall, AI provides a robust toolkit for comprehensive risk management in finance.
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Younus, Muhammad, Halimah Abdul Manaf, Achmad Nurmandi, et al. "The Role of E-Government in Mitigating Tax Evasion Through Behavioral Profiling of Non-Compliant Taxpayers." In Modeling and Profiling Taxpayer Behavior and Compliance. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0422-9.ch012.
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Tax evasion poses significant challenges to governments, undermining revenue collection and trust in public institutions. E-Government platforms, leveraging advanced digital tools and data analytics, offer a promising solution to address this issue by enabling efficient monitoring and profiling of taxpayer behavior. This study explores the role of E-Government systems in mitigating tax evasion through the behavioral profiling of non-compliant taxpayers. By integrating machine learning algorithms and anomaly detection techniques, the research identifies patterns of non-compliance and develops predictive models for early detection of evasion risks. Furthermore, the study examines the psychological and socio-economic factors influencing taxpayer behavior, emphasizing the role of trust, transparency, and system usability in fostering voluntary compliance. The findings underscore the importance of data-driven policy interventions and ethical considerations in behavioral profiling, offering a robust framework for enhancing tax compliance and promoting fiscal transparency.
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Islam, Muhammad Idreesul, Khair Ul Nisa, Sabeha Mufti, Syed Immamul Ansarullah, Sheikh Ikhlaq, and Tyba Yousuf. "Artificial Intelligence in Tax Compliance." In Modeling and Profiling Taxpayer Behavior and Compliance. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0422-9.ch011.
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This chapter examines tax administration systems that utilizes artificial intelligence (AI) technologies including predictive analytics and natural language processing across multiple nations such as the United States, Europe, Australia, and India. These tools improve tax efficiency by reducing administrative costs and ensuring accurate compliance. However, the implementation of AI introduces critical ethical concerns regarding algorithmic fairness, data privacy, and equitable outcomes. These challenges can be addressed through explainable AI (XAI), ethical standards, and robust regulatory frameworks. The chapter describes the differences between AI adoption in developed nations and less developed countries alongside recommendations for local use and worldwide teamwork. Tax systems that use AI technology to serve more people become more transparent as governments learn how to handle its disadvantages. Furthermore, the chapter outlines upcoming trends like blockchain and quantum computing and presents clear steps tax authorities should take to use AI responsibly in tax operations.
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Sareddy, Mohan Reddy, and Shakir Khan. "AI Climate Toolkit for Predictive Analytics, Risk Mitigation, Ecosystem Restoration, and Sustainable Urban Future." In Transforming Business Through Digital Sustainability Models. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0608-7.ch012.
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Background: Some of the problems due to climate change are high temperatures, flooding, and pollution within urban centers. To enhance sustainable urban development, AI Climate Toolkit addresses these through risk reduction, ecosystem restoration, and predictive analytics. Methods: In terms of maximizing resources, reduction of hazards, and the preservation of biodiversity, it employs a combination of datasets and maps, Internet of Things sensors, and machine learning in its toolkit. Results: The toolkit's ability to enhance the resiliency of urban climate was evident from the prediction accuracy of 0.05°C, carbon sequestration of 2.2 kg/m2, and policy compliance at 93%. The toolbox offers a way to sustainable growth of the urban area, even though scale and equity remain challenges.
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Pasupuleti, Murali Krishna. "Revolutionizing Healthcare: AI and Big Data for Predictive Analytics and Precision Medicine." In AI and Big Data for Predictive Healthcare Analytics. National Education Services, 2025. https://doi.org/10.62311/nesx/97922.
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Abstract: The integration of Artificial Intelligence (AI) and Big Data is revolutionizing healthcare by enabling predictive analytics and precision medicine, shifting the focus from reactive treatments to proactive, data-driven healthcare solutions. AI-powered machine learning models analyze vast datasets, including electronic health records (EHRs), genomic sequences, medical imaging, and real-time patient monitoring, to predict disease risks, personalize treatments, and enhance diagnostic accuracy. Precision medicine leverages AI to tailor therapies based on genetic profiles, lifestyle factors, and environmental influences, improving patient outcomes while minimizing adverse effects. Additionally, AI-driven drug discovery, robotic-assisted surgeries, and telemedicine innovations are accelerating medical advancements, reducing costs, and improving healthcare accessibility. Despite challenges related to data privacy, algorithmic bias, and regulatory compliance, AI continues to drive groundbreaking innovations in early disease detection, optimized treatment planning, and healthcare automation. As AI and Big Data evolve, the future of healthcare will be defined by intelligent, patient-centric, and highly efficient medical ecosystems, transforming the way healthcare is delivered worldwide. Keywords: AI in healthcare, Big Data analytics, predictive analytics, precision medicine, AI-driven diagnostics, medical imaging, machine learning in healthcare, genomics, pharmacogenomics, drug discovery, personalized medicine, healthcare automation, AI in telemedicine, robotic-assisted surgery, patient-centered AI.
Conference papers on the topic "Predictive Compliance Analytics"
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Malali, Nihar, and Sita Rama Praveen Madugula. "Predictive Analytics and Artificial Intelligence for Regulatory (RegTech) Compliance in the Financial Industry." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035220.
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Tandon, Pankaj. "Corrosion Data Management with Operational Excellence." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00302.
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Abstract This session presents a flexible data analytics and insights model with risk assessment for pipeline corrosion management with improved safety, and operational efficiency. We present a case study with integration of As-built, pipeline and corrosion and cathodic protection data. The risk model is an informed overlay on data integration with twin benefits of instant customizability and risk management. AI backed software capability provides a framework for actionable risk, with customized data driven assessments, predictive scheduling of remediation, preventative and mitigation methods, consistent with PHMSA models, The integrated software platform provides quantified return on integrity and maintenance supply chain spend with fiscal rigor. Data driven software solutions empower operators to proactively manage corrosion with increased safety, efficient compliance management, and improved function. The capability enables service vendors to derive increased efficiency in execution of the operator services, for competitive advantage. Join us to explore how Praemia software revolutionizes corrosion management along the operational excellence continuum.
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Parker, Keith, Trey Johnston, Alfonso Garcia, Dale Lindemuth, Stephen B. Gibson, and Christophe Baete. "Laying the Foundation for an Engineered and Integrated Approach to Pipeline External Corrosion Protection." In CONFERENCE 2022. AMPP, 2022. https://doi.org/10.5006/c2022-17962.
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Abstract With a growing and aging liquids asset base covering over 17,000 pipeline miles throughout the U.S. and Canada, as well as Enbridge’s move to a High-Reliability Organization (HRO), the Enbridge External Corrosion Prevention (ECP) team is working on a shift from a compliance and time-driven routine maintenance program to a predictive forecasting strategy. Coupled with advanced diagnostics and modeling, such an approach can provide useful information for Long-Range Forecasting (LRF). Utilizing a comprehensive in-line inspection (ILI) and direct examination (DE) program with state-of-the-art predictive technologies, sound engineering, and risk management practices, the Enbridge Pipeline Integrity ECP team is developing a unification of corrosion monitoring and mitigation strategies that will minimize and effectively manage external corrosion risks. The expected outcomes of such an approach are increased safety and reliability of the pipeline system along with improvements in operating efficiency. The efforts are consistent with general industry trends to capitalize more on extensive historical data and increased use of analytical tools including advanced diagnostics and modeling to help manage the relevant threats including DC interference, AC interference, and coating degradation among others. This paper will review the activities of this evolving initiative thus far and planned steps moving forward.
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Parthasarathy, Dhasarathy, Cecilia Ekelin, Anjali Karri, Jiapeng Sun, and Panagiotis Moraitis. "Measuring design compliance using neural language models: an automotive case study." In PROMISE '22: 18th International Conference on Predictive Models and Data Analytics in Software Engineering. ACM, 2022. http://dx.doi.org/10.1145/3558489.3559067.
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Anand, Sangeeta, and Sumeet Sharma. "LONG-TERM CARE INSURANCE (LTCI) SYSTEMS MODERNIZATION USING CLOUD-BASED DATA ANALYTICS." In 12th International Conference on Signal Processing. Academy & Industry Research Collaboration, 2025. https://doi.org/10.5121/csit.2025.151107.
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Changing demographics, growing healthcare expenses, and better standards for digital service supply have long-term care insurance (LTCI) firms under more and more pressure to modernise. Standard long-term care insurance plans fall short in managing claims, assessing risks, ensuring policy compliance, or providing consumers with decent customer service. Usually, several data storage, human procedures, and outdated technology lead to these issues. These restrictions deliver more specialised, premium treatment, increase running expenses, and complicate response. This essay explores how employing cloudbased data analytics could totally transform how these problems are addressed and offer a fresh approach for long-term care insurance to function. By enabling scalable and flexible configurations that speed up real-time data processing, simplify case management, and increase predictive analytics—all of which help to make case management possible—cloud technologies provide for By moving to cloud-native architectures, LTCI companies might cut operational expenses, improve choices, and provide better, more customer-centric experiences. Two quick modernising strategies are getting ready for a cloud migration and emphasising business stability, security, and cost control. The paper underlines the need of establishing robust data governance systems to guarantee adherence to HIPAA, other regulations, and data quality standards as well as safe handling and preservation of private policyholder data. Interoperability is crucial since it allows several systems to function without any issues. Among the systems within this group are EHRs, nursing networks, statistics databases, and claims handling engines. LTCI must have advanced data analytics abilities—including predictive modelling for risk classification, fraud detection technology, and sentiment analysis for client comments—if it is to effectively modernise. These technologies set the best rates, enable insurance firms to identify high-risk individuals ahead of time, expedite the claims process, and create customised treatment recommendations based on historical performance.
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Klein, Levente J., Ted van Kessel, Dhruv Nair, Ramachandran Muralidhar, Hendrik Hamann, and Norma Sosa. "Monitoring Fugitive Methane Gas Emission From Natural Gas Pads." In ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems collocated with the ASME 2017 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ipack2017-74191.
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Identifying fugitive methane leaks can improve predictive maintenance of the extraction process, can extend gas extraction equipment lifetime, and eliminate hazardous work conditions. We demonstrate a wireless sensor network based on cost effective and robust chemi-resistive methane sensors combined with real time analytics to identify leaks from 2 scfh to 1000 scfh. The chemi-resistive sensors were validated to have a sensitivity better than 1 ppm in methane plume detection. The real time chemical sensor and wind data is integrated into an inversion models to identify the location and the magnitude of the methane leak. This integrated sensing and analytics solution can be deployed in outdoor environment for long term monitoring of accidental methane plume emissions, generate recommendations about fixing them, and ensure compliance with local government regulations.
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Ziakkas, Dimitrios, Konstantinos Pechlivanis, and Debra Henneberry. "The Implementation of AI in the eVTOL Safety Management Systems." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006501.
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The emergence of electric Vertical Take-Off and Landing (eVTOL) aircraft represents a transformative evolution in urban mobility, promising sustainable and efficient air transportation. However, the integration of eVTOLs into high-density urban environments introduces new safety challenges that require advanced Safety Management Systems (SMS). Traditional SMS frameworks, which rely on deterministic models and human-centric decision-making, are insufficient for managing the complexity of eVTOL operations. The integration of Artificial Intelligence (AI) into SMS offers a proactive approach to risk assessment, predictive maintenance, and human-machine interaction, ensuring enhanced operational safety and regulatory compliance. This study explores the role of AI in augmenting SMS for eVTOL operations, focusing on predictive analytics, human-machine interface (HMI) enhancements, and real-world applications from leading eVTOL manufacturers such as Joby Aviation and Lilium. AI-driven predictive analytics enable real-time risk detection and mitigation, improving component reliability and reducing maintenance-related failures. Enhanced HMI tools facilitate adaptive decision-making, reducing cognitive workload for pilots and optimizing safety-critical interactions between human operators and automated systems. Case studies demonstrate that AI-integrated SMS frameworks improve emergency response times, enhance situational awareness, and support the continuous evolution of safety protocols in eVTOL aviation. The findings of this study have significant implications for policy development, training programs, and collaborative innovation. Regulatory agencies such as the FAA and EASA must establish AI-driven safety regulations to ensure compliance while fostering technological advancements in urban air mobility. Training programs must be restructured to incorporate AI-based learning methodologies, preparing pilots and maintenance personnel for AI-enhanced workflows. Collaboration between AI developers, aviation regulators, and eVTOL manufacturers is essential to establishing standardized AI-driven safety management practices.As urban air mobility continues to expand, AI-driven SMS will play a critical role in ensuring the safety, efficiency, and scalability of eVTOL operations. The integration of AI into SMS provides a pathway for predictive, data-driven risk management, enabling a future where eVTOLs operate seamlessly within the global aviation ecosystem. Future research should focus on refining AI decision-making algorithms, improving real-time safety interventions, and developing regulatory frameworks that ensure the safe deployment of AI-driven eVTOL technologies.
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Rawahi, Sara Al. "Intelex Mobile App Application (Daily Vehicle Check List)." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2025. https://doi.org/10.2118/225119-ms.
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Abstract In the oil and gas industry, ensuring the safety, reliability, and compliance of fleet vehicles is critical to operational success. Traditional paper-based vehicle inspection systems are often inefficient, prone to errors, and difficult to manage, leading to compliance gaps, increased administrative burdens, and potential safety risks. To address these challenges, Daleel Petroleum implemented a QR code-based vehicle inspection system using the INTELEX mobile application transitioning from manual processes to a digital platform. This innovative approach streamlined inspections, enhanced record management, and improved compliance with the OPAL Road Safety Standard and Daleel Requirements (Oman Society for Petroleum Services, 2023). The system leverages unique QR codes assigned to each vehicle, enabling inspectors to scan codes and access digital checklists instantly. The workflow includes QR code scanning, checklist completion, real-time submission, automatic record logging, and notifications for corrective actions. Key enhancements such as offline functionality, customizable fields, and comprehensive training programs were introduced to ensure smooth adoption and usability across diverse operational contexts. Results demonstrated significant improvements, including a 50% increase in vehicle inspections, reduced data entry errors, and enhanced compliance tracking. The system's real-time reporting and proactive maintenance capabilities contributed to improved fleet safety and operational efficiency. Challenges such as technological adaptation and network dependency were addressed through training and offline functionality, ensuring seamless implementation. Future optimizations will focus on predictive maintenance algorithms, advanced data analytics, and integration with vehicle telemetry data, further enhancing fleet management practices. This study highlights the transformative impact of digital solutions in vehicle inspections, offering valuable insights for organizations seeking to modernize their fleet management processes, ensure regulatory compliance, and foster a culture of safety and accountability.
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"The Next Frontier: Future Research Trends in Artificial Intelligence and Machine Learning for Legal Applications." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/heyw2341.
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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the legal domain has marked a transformative phase, enhancing operational efficiencies and decision-making processes. This paper explores the next frontier in the evolution of these technologies within legal practices, emphasizing future research directions and emerging trends. It investigates current applications and their impact on the legal field, such as predictive analytics for case outcomes, natural language processing for document analysis, and automation of routine legal tasks. The study also identifies major challenges that impede the adoption of AI and ML, including issues related to data privacy, regulatory compliance, and institutional resistance. Through analysis of various case studies, this paper offers insights into successful implementations and comparative assessments across different legal systems. Finally, it proposes future research opportunities that include cross-disciplinary approaches, enhancement of predictive models, and integration with other cutting-edge technologies such as Blockchain and the Internet of Things (IoT). The findings aim to provide a comprehensive guide for future initiatives and research that could further transform the legal landscapes.
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Marshall, Michael, and Martin Fingerhut. "Reframing PSMS in the Context of Operational Risk Management and ESG Sustainability." In 2022 14th International Pipeline Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/ipc2022-87773.
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Abstract Current Environmental, Health, and Safety (EHS) and Asset Integrity/Performance Management (AIM/APM) platforms fail to properly aggregate data from multiple Pipeline Safety Management System (PSMS) workflows into a single database which can be queried to inform real-time, risk-based analyses and decision-making relative to profitability impacts. With predictive analytics at the core of an asset integrity and PSMS framework intent upon achieving holistic, enterprise-wide visibility and accountability for Environmental, Social, and Governance (ESG) program effectiveness and sustainability, this paper proposes an ideal solution designed around the following core functions: • Reframing EHS and AIM/APM in the context of Operational Risk Management (ORM) by normalizing data relative to performance and process-related parameters • Categorizing, prioritizing, and risk ranking incidents by economic impact (specifically lost production), enabling problem solving teams to resolve high value deep-dive systemic problems • Satisfying the need for a one-stop system that has the highly interlinked EHS, compliance and enterprise risk management systems all in one framework • Linking to data historians like OSIsoft PI to “give voice to equipment” for predictive analytics as necessitated by today’s digital transformation movement • Featuring incident investigation, reporting and failure modes decision support functionality based on industry best practice standards including API 754 Process Safety Performance Indicators for Refining and Petrochemical Industries, which is also directly applicable to petroleum pipeline industry operating systems and processes where loss of containment may occur • Offering a highly configurable user interface for key performance indicator (KPI) trending, reporting, alerts/notifications, action planning and follow-up
Reports on the topic "Predictive Compliance Analytics"
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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.
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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and enable self-adjusting governance mechanisms in Decentralized Autonomous Organizations (DAOs). Additionally, AI enhances blockchain consensus mechanisms, fraud detection, and risk assessment in Decentralized Finance (DeFi) applications. Privacy-preserving technologies such as zero-knowledge proofs (ZKPs) and quantum-resistant cryptography strengthen the security and confidentiality of AI-driven smart contracts. This research explores the convergence of AI and blockchain, examining how intelligent smart contracts can automate legal compliance, enforce dynamic contract logic, and optimize transaction fees while maintaining transparency and decentralization. By integrating AI-driven decision-making, automated dispute resolution, and scalable execution models, this study provides a comprehensive framework for secure, efficient, and intelligent decentralized governance. Keywords: AI-powered smart contracts, blockchain automation, decentralized governance, reinforcement learning, fraud detection, decentralized finance (DeFi), zero-knowledge proofs, quantum-resistant cryptography, DAO optimization, legal compliance automation.
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.
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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).
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EVALUATION OF LOCAL-PLATE BUCKLING COEFFICIENT FOR THE DESIGN OF COLD-FORMED STEEL-LIPPED CHANNEL CROSS SECTIONS: NUMERICAL SIMULATIONS AND DESIGN RECOMMENDATIONS. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.1.4.
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Recent advancements in design guidelines for cold-formed steel members focus on enhancing the prediction of nominal strengths under various loading conditions. This improvement is achieved through precise accounting for local plate buckling behavior. Nevertheless, the Effective-Width Method (EWM), aligned with current design standards, estimates a lower structural capacity for cold-formed steel members. Assuming buckling precedes the yielding of cross-sections and considering no interactive restraint between adjacent elements, conservative predictions of member strengths are derived. To address this issue, this paper introduces a numerical investigation involving several lipped channel cross-sections with varying web height-to-flange width ratios, intending to assess the local plate buckling coefficient (k-value). Initially, validating a shell finite-element model against test results establishes benchmark strengths for the considered cross-sections. Subsequently, analytical solutions for calculating the k-value are presented and compared with those obtained from numerical solutions. Interactions between cross-sectional adjacent elements are examined, leading to a proposed refined EWM compliant with AISI standards. Finally, a reliability analysis is performed to illustrate the accuracy and reliability of the proposed design method. This research highlights the significance of accurately considering the restraining effect between sectional sub-elements and the importance of boundary conditions influencing the plate buckling coefficient.