Regulatory Compliance Research Articles

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

Green Logistics 5.0: a review of sustainability-oriented innovation with foundation models in logistics

PurposeThe paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous future. By harnessing the power of foundation models and incorporating sustainable principles, this paper can systematize the logistics industry’s environmental framework, increase its social responsibility and ensure its long-term economic viability.Design/methodology/approachGeneralizing environmental sustainability goals requires a multi-layered innovation approach incorporating corporate philosophy, products, processes and business models. In this paper, this comprehensive approach is not just a strategy but a necessity in the current global context. This paper uses the sustainability-oriented innovation (SOI) method, crucial for achieving explicit environmental, social and economic impacts.FindingsArtificial intelligence, especially foundation models, can contribute to green logistics by optimizing routes, reducing packaging waste, improving warehouse layouts and other functions presented in the paper. At the same time, they can also consider social, economic and governance goals.Research limitations/implicationsArtificial intelligence algorithms present challenges such as high initial investment, regulatory compliance and technological integration.Practical implicationsThe paper contains implications for developing environmentally sustainable logistics, which is currently one of the most significant challenges. The framework presented can apply to logistics companies.Originality/valueThis paper fulfills an identified need to study sustainability in logistics. The framework is entirely original and not present in the literature. It is essential to help design and implement innovative logistics approaches.

Data analytics as a driver of digital transformation in financial institutions

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

Comprehensive framework for data fusion in distributed ledger technologies to enhance supply chain sustainability

This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.

The Rise of New Banks: Disrupting the Traditional Banking Landscape

Neobanks are online - only, digital - only financial institutions. They are, to put it simply, non - existent. Customers may need to invest a significant amount of time and effort in typical bank transactions. Neobanks add an immersive digital layer to traditional banking, promising a smooth online experience. [1] Neo banks frequently provide a comprehensive array of financial services that may be accessed via their mobile applications or websites, such as checking and savings accounts, loans, payments, investments, and budgeting tools. Neo banks place a high priority on data privacy and security. [2]. Nonetheless, new banks continue to face difficulties with regulatory compliance. Opportunities might be found in developing user experience technology and providing creative, customised financial products. Neo banks are becoming more and more popular, but traditional banks won't likely disappear anytime soon because both may survive and meet distinct client needs in the changing banking environment. [2]

Geospatial data in AML risk management: A review of applications and best practices

This review explores the role of geospatial data in enhancing Anti-Money Laundering (AML) risk management, focusing on its applications, best practices, and strategic implications for organizations. Geospatial data, combined with advanced analytical tools, provides critical insights into transaction patterns, customer locations, and geographic risk factors, enabling financial institutions to detect suspicious activities more effectively. The paper examines how geospatial data integration supports compliance with regulatory frameworks, bolsters transaction monitoring systems, and facilitates enhanced due diligence processes. Through a systematic review of existing literature and case studies, this paper identifies key applications such as identifying high-risk jurisdictions, mapping transaction flows, and detecting cross-border money laundering schemes. Best practices are highlighted, including leveraging geospatial analytics in conjunction with machine learning algorithms, optimizing data governance, and ensuring interoperability across systems. The findings suggest that organizations adopting these practices are better equipped to combat money laundering by improving risk assessment, reducing false positives, and enhancing investigative efficiency. Additionally, the review discusses the challenges of data privacy, regulatory compliance, and the technical complexities associated with geospatial data integration. The paper concludes by outlining future prospects, recommending further advancements in real-time geospatial analytics, cross-sector collaborations, and the adoption of emerging technologies to strengthen AML frameworks. This review serves as a comprehensive guide for financial institutions seeking to incorporate geospatial data into their AML strategies, driving both compliance and operational efficiency.

The role of electronic data capture systems in clinical trials: Streamlining data integrity and improving compliance with FDA and ICH/GCP guidelines

This study investigates the transformative role of Electronic Data Capture (EDC) systems in clinical trials, emphasizing their impact on data integrity, compliance with regulatory standards, and the efficiency of trial processes. The purpose of the research was to critically analyze how EDC systems align with FDA and ICH/GCP guidelines, addressing the complexities of modern clinical trials. By conducting a detailed literature review, the study explores the benefits, challenges, and technological advancements associated with EDC systems. Key findings highlight the significant advantages of EDC systems in enhancing data accuracy, real-time monitoring, and facilitating regulatory compliance through secure data management and audit trails. The study also identifies several challenges, including high implementation costs, data privacy concerns, and integration issues with other digital trial technologies. Technological advancements, such as artificial intelligence, blockchain, and decentralized trial models, are explored as promising solutions to these limitations, offering new avenues for optimizing clinical trial management. The study concludes that EDC systems have revolutionized clinical trials, streamlining data collection and analysis while ensuring compliance with stringent regulatory requirements. It recommends continued investment in EDC technologies, enhanced training for trial staff, and the integration of cutting-edge innovations to ensure future clinical trials' success.

The impact of ISO security standards on enhancing cybersecurity posture in organizations

The increasing frequency and sophistication of cyber threats have made organizations need to adopt robust cybersecurity frameworks. ISO security standards, particularly the ISO/IEC 27000 series, play a critical role in enhancing organizations' cybersecurity posture worldwide. These standards provide a systematic approach to managing sensitive information, ensuring its confidentiality, integrity, and availability. ISO/IEC 27001, which focuses on establishing an Information Security Management System (ISMS), is widely recognized for its ability to help organizations identify, manage, and mitigate cybersecurity risks. By adopting ISO standards, organizations benefit from improved risk management, enhanced incident response capabilities, and stronger alignment with regulatory compliance requirements, such as GDPR and HIPAA. In addition, ISO security standards promote a security-first culture within organizations, fostering greater employee awareness and encouraging the consistent implementation of best practices across departments and regions. The adoption of standards like ISO/IEC 27001 (Information security, cybersecurity and privacy protection), ISO/IEC 27018 (Code of practice for protection of personally identifiable information (PII) in public clouds acting as PII processors), ISO/IEC 27017 (code of practice for information security controls based on ISO/IEC 27002 for Cloud services), ISO/IEC 27015 (Information security management guidelines for financial services) ISO/IEC 27002 (Information security, cybersecurity and privacy protection - Information security controls), and ISO/IEC 27701 (Security techniques- Extension to ISO/IEC 27001 and ISO/IEC 27002 for privacy information management – requirements and guidelines) has demonstrated significant improvements in data protection, especially in industries handling sensitive personal or financial data. Despite their benefits, implementing ISO standards poses challenges, such as resource constraints, scalability, and the need for continuous updates. As the threat landscape evolves, ISO security standards will remain integral to developing a proactive cybersecurity strategy, integrating with emerging technologies such as artificial intelligence and IoT. The global adoption of these standards reflects their pivotal role in securing the digital infrastructure of modern organizations.

Leveraging data mining and cybersecurity techniques to enhance algorithmic trading performance and forensic investigations in financial markets

In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.

Detection of Soybean GMO Events Using Two Multiplex Droplet Digital PCR Assays.

Detection methods for GMO events are required because of regulatory compliance requirements. Efficient detection and quantification of GMO events saves time and resources. Multiplex digital PCR (dPCR) allows detection and quantification of more than one GMO events at the same time. The study used two tetraplex droplet digital PCR (ddPCR) assays for the detection of 19 soybean GMO events. Two Multiplex dPCR assays were developed and optimized for the detection of 19 soybean GMO events. The first tetraplex ddPCR assay contained four element-specific targets commonly found in GMO plants (P-35S, T-nos, tE9 and Pat). The second event-specific tetraplex ddPCR assay targeted four soybean GMO events that are not detected with the element-specific tetraplex ddPCR (CV127, DP305423, MON87701 and MON87751). The element-specific tetraplex ddPCR assay detected all the expected 15 soybean GMO events. The element-specific tetraplex ddPCR assay also detected selected soybean GMO events at 0.01% level. The event-specific tetraplex ddPCR assay was successfully used to quantify the four soybean GMO events at 0.1, 1, 2 and 5% levels. The event-specific tetraplex ddPCR assay also detected the four soybean GMO events at 0.01% level. : The two tetraplex ddPCR assays can be used for the detection of 19 soybean GMO events. Element-specific tetraplex ddPCR assay was used to detect 15 soybean GMO events, and event-specific tetraplex ddPCR assay was used to detect and quantify four soybean GMO events that are not detected by the element-specific ddPCR assay.

Impact of AI on cybersecurity and security compliance

Artificial Intelligence (AI) is transforming the field of cybersecurity and reshaping security compliance practices. As cyber threats become increasingly complex, AI offers powerful tools for identifying, mitigating, and preventing attacks in real-time. AI-driven systems excel at processing vast amounts of data, detecting anomalies, and identifying patterns that traditional security systems might miss. Machine learning and deep learning algorithms enhance the ability to predict potential threats, reducing response time and improving the accuracy of threat detection. These capabilities make AI a valuable asset in addressing sophisticated threats such as zero-day vulnerabilities, advanced persistent threats (APTs), and ransomware attacks. In the realm of security compliance, AI plays a pivotal role by automating routine tasks such as monitoring, auditing, and reporting. This reduces the burden on organizations to manually enforce regulatory standards, leading to more efficient compliance with frameworks like GDPR, HIPAA, and PCI-DSS. AI can continuously assess systems to ensure that they meet compliance requirements, enhancing the ability to detect and respond to violations. Furthermore, AI contributes to governance by helping organizations develop robust security policies, track compliance metrics, and streamline incident response. However, the integration of AI in cybersecurity also presents challenges, including adversarial AI, data privacy concerns, and transparency in AI decision-making processes. Additionally, AI-driven attacks are an emerging threat that necessitates further research and regulation. Despite these challenges, the future of AI in cybersecurity and compliance looks promising, with advancements in predictive analytics, quantum computing, and autonomous security systems poised to further revolutionize the field. As AI technologies evolve, they will continue to play a critical role in fortifying cybersecurity defenses and ensuring regulatory compliance across industries.

Optimizing contract negotiations in clinical research: Legal strategies for safeguarding sponsors, vendors, and institutions in complex trial environments

This paper examines the complexities of clinical trial contract negotiations, focusing on legal strategies and technological innovations that safeguard the interests of sponsors, vendors, and institutions. In an increasingly globalized and digital research environment, contract negotiations are pivotal to ensuring compliance, protecting intellectual property, and mitigating legal risks. The study’s purpose was to explore key components of clinical trial contracts, address the challenges involved, and assess future trends, particularly in light of evolving regulatory frameworks and technological advancements. Through a comprehensive review of legal literature and emerging technologies, the study identified crucial aspects of contract negotiations, including risk allocation, indemnification, intellectual property rights, and data privacy. Special attention was given to the role of technology—such as AI, blockchain, and contract management platforms—in streamlining the negotiation process. These tools have proven effective in reducing errors, enhancing collaboration, and ensuring regulatory compliance. The main findings revealed that while traditional contract structures remain integral, the adoption of digital tools and a patient-centric approach are critical in addressing new challenges posed by decentralized trials and cross-border collaborations. The study concluded that stakeholders must embrace flexible contract frameworks, incorporate robust data protection measures, and leverage technology to optimize negotiations. Recommendations include the integration of advanced analytics for decision-making, the adoption of blockchain for contract security, and a heightened focus on patient rights in contract structures to ensure ethical conduct and compliance in clinical research.

WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images

Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally.

Nist Aal Adaptive Security Framework

In our digitally evolving security world, adaptive and risk - based authentication methods are the key countermeasures responding to the current rising cyber risks. The National Institute of Standards and Technology (NIST) Authenticator Assurance Levels (AAL) framework offers a specific guidance on the implementation of adaptive security measures. The study is designed to put the "NIST AAL Adaptive Security Framework" into practice by using technologies such as Ping Federate and Ping Davinci systems for the timely risk analysis of IT environments and advanced MFA approaches against emerging threats. AAL model can define authentication procedures customized as per the level of risk, requirements and compliance regulations of the organization. It is essential to classify authentication into separate assurance level (AAL1, AAL2 and AAL3) as it helps to make modifications according to contextual factors like perceived risk and user's behaviour. This is vital as it enhances security while it optimizes user experience. This study examines the proposed framework’s efficacy in reducing cyber - threats and strengthening security posture of organizations.

The Role of Machine Learning in Enhancing Corporate Financial Planning

Aim: To examine the role of machine learning in enhancing corporate financial planning. Problem Statement: In the past, corporate financial planning relied on statistical models and expert judgment which are often constrained by predefined assumptions and historical data. By so doing, they usually struggled to handle the non-linear and dynamic nature of financial markets causing limitations in their responsiveness and predictive power. Significance of Study: There is need to incorporate machine learning in corporate financial planning to provide a transformative technique via the introduction of advanced algorithms which are capable of analyzing diverse and vast datasets to uncover sophisticated relationships and patterns. Methodology: Recent relevant published articles in the area of machine learning in enhancing corporate financial planning were consulted. Relevant articles were sourced from the internet using the Google search engine. The abstract of the consulted published articles were thoroughly examined to study their relevance to the subject matter. Discussion: This review article examines the role of machine learning in enhancing corporate financial planning. It was found that machine learning techniques have crucial roles to play in the context of corporate financial planning via decision-making improvement, risk management, operational efficiency enhancement, fraud detection and enabling personalized experiences. Also, Machine Learning technologies have a wide variety of uses in corporate financial planning which enable banks and insurance companies in delivering tailored experiences that give their customers some preferences. Common identified machine learning approaches in corporate financial planning include predictive analytics, natural language processing, computer vision, reinforcement learning and anomaly detection approaches. Major areas of machine learning application include dynamic pricing and offers; regulatory compliance; personalized customer service; predictive analytics for customer retention; personalized product recommendations and so on. Furthermore, the implementation of Machine Learning (ML) in corporate financial planning and financial services industry provides many chances for enhancing customer experiences, improving efficiency and driving innovation. However, ML poses numerous challenges which should be addressed to attain the complete potential of these technologies. Conclusion: As technology continues to advance, financial institutions will need to route the challenges of bias mitigation, transparency and regulatory compliance while the ML full potential is equally leveraged. Also, machine learning has the potential to transform corporate financial planning via the provision of more adaptive, accurate and real-time insights.

Architecting Resilience : A Framework for Secure and Compliant Healthcare IT Infrastructures

Healthcare information technology (IT) systems face unique challenges in maintaining data security and regulatory compliance while supporting critical patient care functions. This article provides a comprehensive analysis of the complex interplay between data protection measures and compliance requirements in the healthcare sector. We examine key components of data security, including encryption, data integrity measures, and secure transfer protocols, with a specific focus on their application to sensitive patient information. The impact of regulations such as HIPAA on system design and maintenance is explored, offering insights into the alignment of IT practices with evolving standards. Through a review of current literature and industry best practices, we present strategies for risk management, employee training, and the implementation of technical controls that address both security and compliance needs. Emerging trends, including cloud computing, the Internet of Medical Things (IoMT), and artificial intelligence in healthcare security, are discussed to provide a forward-looking perspective. This article contributes to the ongoing dialogue on balancing innovation with security in healthcare IT, offering practical recommendations for healthcare organizations to enhance their data protection measures while ensuring regulatory compliance.

Cloud Migration Analysis for Core System Infrastructure in Financial Services: Study Case PT XYZ

Currently, cloud computing adoption is being utilized in the financial sector for its flexibility and innovative technologies. This study assesses PT XYZ's readiness to migrate its core systems to a cloud environment, which is crucial for maintaining competitive agility and robustness in Indonesia's financial services sector. Focusing on the diffusion of innovations (DOI) model and the technology-organization-environment (TOE) framework, the research examines ten factors influencing PT XYZ's migration strategy. The analysis identifies challenges related to PT XYZ's aging on-premises infrastructure, including server procurement delays and maintenance issues, which impact operational efficiency. Through semi-structured interviews with key IT personnel, this study explores perceptions of the benefits and challenges of cloud computing, as well as regulatory compliance, aiming to guide PT XYZ toward a successful cloud migration that ensures continuity, compliance, and enhanced operational capabilities. The findings contribute to understanding the relative advantages, such as cost savings and security concerns, complexity, compatibility, technology readiness, top management support, firm size, competitive pressure, and regulatory factors. PT XYZ’s decision to migrate aligns with all the factors for cloud computing adoption, with the relative advantage, complexity, compatibility, technological readiness, and top management support being high impacts on the migration decision, while regulatory compliance presents a challenge for the financial services industry in Indonesia].

Innovative Application of Big Data Technology in New Media Personalized Service

With the rapid development of big data technology, its application in the field of new media is becoming more and more extensive, especially in personalized service shows great potential. This paper discusses in depth the application, challenges and future development of big data technology in new media personalized service. The following conclusions are drawn: (1) Big data technology plays a key role in new media personalized service Big data technology can construct a refined user profile, realize personalized content recommendation, and improve user satisfaction and loyalty by collecting, analyzing and utilizing user behavioral data on new media platforms. (2) Big data technology faces multiple challenges in new media personalized services. Although big data technology shows great potential in new media personalized service, it still faces challenges in data privacy and security, data quality and accuracy, technology update and algorithm optimization, as well as regulatory compliance and ethics. (3) Innovative applications promote the development of big data technology in new media personalized services. With the continuous innovation and application of big data technology, new media personalized services are also developing and improving.

Analysis of changes in HSBC’s income statement between 2022-2023

The banking industry is a cornerstone of the world wide economy, primarily due to the unique nature of its primary product—money. This sector has long held a central role within society and continues to attract considerable interest from potential investors. However, investing in the banking industry is not without its challenges. Market volatility, regulatory compliance costs, and geopolitical uncertainties can profoundly impact the industry’s profitability and stability. Being among the biggest banks in the world, HSBC provides financial services such as personal banking, wealth management, and corporate finance in over 60 countries. This is an excellent example of an analysis of the banking sector. The essay lists changes in HSBC’s income statement between 2022 and 2023 and analyses them using vertical and horizontal analysis, ratio analysis, segment reporting analysis, and benchmarking. Through these analyses, the essay aims to see HSBC’s capacity to face banking crises and provide a few suggestions for investors.

The Effect of Attitudes on Behavior, Subjective Norms, Behavioral Control, Behavioral Intentions on Lecturer Behavioral: The Moderation Role of Compliance with Regulations and Visionary Leadership in LLDIKTI Region VI Central Java Indonesia

Phenomenon, Private University at LLDIKTI VI Central Java (1) There is inequality in the quality of education which indicates that the increase in human resources has not been evenly distributed in all areas of the city/district. (2) The behavior of lecturers in carrying out tridharma activities is still not optimal. This research is based on the existence of empirical GAP and theoretical GAP regarding the limitations of TPB theory, so this research closes this gap by adding the variable compliance with regulations and visionary leadership as a moderator, which is combined into the Theory of Planned Behavior model to make this variable as research novelty. The aim of the research is to prove and analyze the influence of behavioral attitudes and norms subjective, behavioral control towards lecturer behavior through behavioral intentions and prove and analyze the role of regulatory compliance and leadership visionary in moderating influence of behavioral intentions to behavior lecturer. Study​ this is causal explanatory with a sample of 370 lecturers in LLDIKTI Region VI Central Java in 2022. Data collection techniques use questionnaires, data analysis uses SEM. Results research (1) behavioral attitudes influential significant positive to behavioral intentions. (2) behavioral attitude has no effect on lecturer behavior. (3) norms subjective influential significant positive effect on behavioral intentions. (4) norms subjective influential significant positive effect on lecturer behavior. (5) control behavior influential significant positive effect on behavioral intentions. (6) control behavior influential significant positive effect on lecturer behavior. (7) behavioral intention influential significant positive effect on lecturer behavior. (8) compliance on regulation moderate influence of behavioral intentions towards lecturer behavior. (9) leadership visionary moderate influence of behavioral intentions towards lecturer behavior.