Process Compliance Research Articles

Impact and Policy Implications of Transition Education Practices on IDEA Indicator 13 Scores and Indicator 14 Outcomes

Federal special education policies require state departments of education to collect data to measure progress in attaining state performance plan goals, with Indicators 13 and 14 measuring the Individual Education Program (IEP) transition process and post-high school student outcomes. Research suggests that higher transition indicator scores result in better post-school outcomes, but no research has yet demonstrated the impact transition education practices have upon indicator scores. This study identified transition education practices that impacted one state’s transition Indicators 13 (transition IEP process compliance scores) and 14 (post-high school outcomes). Eighty-three secondary special educators from 36 districts identified the extent their students engaged in various evidence-based transition practices. Hierarchical cluster analysis identified five transition education themes. Multivariate analysis of variance revealed few statistically significant results and several meaningful effect sizes indicating specific transition education practices that impacted Indicator 14 outcomes. Districts with students actively involved in IEP meetings had higher Indicator 13 scores and 14 outcomes. Districts with high to moderate number of students with paid jobs and money management experience had higher Indicator 14 outcomes. Districts that taught students to set postsecondary goals had higher Indicator 14 outcomes. Other results are described and policy implications discussed.

SecOnto: Ontological Representation of Security Directives

The current digital landscape demands robust security requirements and, for doing so, the institutions enact complex security directives to protect the citizens and the infrastructures, particularly in the European Union. These directives aim to safeguard data and harmonise security across the European region, and institutions must navigate this evolving legal landscape in order to implement and keep up-to-date the prescribed security measures.However, understanding and implementing these directives towards full compliance can be difficult and expensive. Ontological representation can be employed to represent and operationalise such security directives, ultimately contributing to the effectiveness and efficiency of the compliance process. Ontologies in fact promote a structured approach to represent knowledge, making the applicable directives more simply understandable by humans and more readily processable by machines.This article introduces SecOnto, a novel methodology for representing security directives as ontologies. SecOnto breaks down the process of transforming the juridical language of modern security directives into full-fledged ontologies by means of five semi-automated steps: Preprocessing, Interpretation, Structuring, Representation and Verification. Each step is described and validated by means of operational examples based upon Directive 2022/2555 of the European Parliament and of the Council of the European Union on security of network and information systems, better known as NIS 2.

Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks

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.

Assessing the Costs of Intravenous Push Waste in Intraoperative Areas Through Observation: A Multi-site Study.

The costs associated with proper disposal, management, and regulatory compliance of controlled substances in healthcare systems are substantial. In the context of the current opioid crisis, and given the high abuse potential of controlled substances, it is imperative that waste is minimized and waste procedures are followed to ensure safe disposal of controlled substances. This study aims to quantify the costs associated with fentanyl, hydromorphone, morphine, midazolam, and ketamine waste in intraoperative areas through a multi-site observational analysis. The study used an observational design across various hospital procedural and post-procedural units in the Southwest Florida region of the United States. Automated and non-automated workflows for wasting controlled substances were compared. As with a previous study conducted by Hertig et al., waste was evaluated as (1) the quantity (mg/μg) of medication disposed defined as 'pharmaceutical waste' or 'product waste' (PW); and (2) workforce time associated with the waste disposal process defined as 'workforce time waste' (WTW). Secondary measures include workforce costs associated with the waste disposal process. The product waste analysis was conducted between October and December 2023. The workforce time waste analysis was examined over a 10-day period in January and February 2024. A yearly extrapolation model was applied to cost data. The findings revealed substantial costs linked to both PW and WTW, emphasizing the financial burden of controlled substance waste. Study data validated previous literature describing the extent of fentanyl, hydromorphone, and morphine waste while documenting significant amounts of midazolam and ketamine waste. The combined annual waste cost for the two study hospitals was estimated at US$56,557, with workforce time accounting for 36%-50% of this total cost. This study provides vital insights into the financial and operational impact of medication waste in procedural and post-procedural areas, supporting ongoing efforts to minimize waste, ensuring the safe and effective use of controlled substances. Future research should explore the impact of medication waste across diverse healthcare settings and the cost implications associated with pharmacy professionals in the waste compliance process.

Regulatory Compliance in Financial Services: How Software Can Simplify Adherence to Regulations

The financial services industry's growing complexity in terms of regulatory requirements has made compliance a top priority for organizations across the globe. With the compliance requirements implemented by regulators becoming more stringent, financial institutions are continuously opting for software products to enable and support their compliance processes. This research explores the role that software plays in helping financial institutions comply with regulatory requirements, focusing on specific categories of compliance software, their operational competencies, and the benefits each category offers. It also discusses the difficulties encountered by financial institutions in implementing these tools, as well as the future of RegTech. This research clearly explains the added value of embedding technology in the evolving world of regulation by exploring the potential for streamlining efficiency, accuracy, and transparency through software solutions in fulfillment of compliance needs.

An improved cloud-based business process compliance management system using a user-centered approach

An improved cloud-based business process compliance management system using a user-centered approach

IMMIGRATION AS A SERIOUS LEGAL PROBLEM IN RELATIONS BETWEEN THE REPUBLIC OF NORTH MACEDONIA AND THE EUROPEAN UNION

The Republic of North Macedonia, as the central part of the Balkan Peninsula, plays an important role in the transit of people, goods and services. Taking advantage of this position represents a great challenge. A recent example for this is the immigration crisis and activation of immigration transit routes. The legislation of the Republic of North Macedonia, especially the Law on Asylum and temporary protection is regulating all issues that outcome from the crisis. The law is balanced with international norms based on procedures of gaining and abolition of the right to asylum and subsidiary protection, as well as determining rights and obligations that outcome from the acquired status. This work is elaborating the main characteristics of the compliance process of Republic of North Macedonia regulations with the European Union in the context of immigration. During the research, were used all documents and legal correspondence between the European Union and the Republic of North Macedonia. The objective of this work is to present the progress and difficulties during the compliance process of the regulations of the Republic of North Macedonia with the EU, with the aim to provide direct conclusions about improvement of certain regulations.

A platform-based Natural Language processing-driven strategy for digitalising regulatory compliance processes for the built environment

The digitalisation of the regulatory compliance process has been an active area of research for several decades. However, more recently the level of activities in this area has increased considerably. In the UK, the tragic incident of Grenfell fire in 2017 has been a major catalyst for this as a result of the Hackitt report’s recommendations pointing a lot of the blame on the broken regulatory regime in the country. The Hackitt report emphasises the need to overhaul the building regulations, but the approach to do so remains an open research question. Existing work in this space tends to overlook the processing of actual regulatory documents, or limits their scope to solving a relatively small subtask. This paper presents a new comprehensive platform approach to the digitalisation of the regulatory compliance processing. We present i-ReC (intelligent Regulatory Compliance), a platform approach to digitalisation of regulatory compliance that takes into consideration the enormous diversity of all the stakeholders’ activities. A historical perspective on research in this area is first presented to put things in perspective which identifies the challenges in such an endeavour and identifies the gaps in state-of-the-art. After enumerating all the challenges in implementing a platform-based approach to digitalising the regulatory compliance process, the implementation of some parts of the platform is described. Our research demonstrates that the identification and extraction of all relevant requirements from the corpus of several hundred regulatory documents is a key part of the whole process which underlies the entire process from authoring to eventually compliance checking of designs. Some of the issues that need addressing in this endeavour include ambiguous language, inconsistent use of terms, contradicting requirements and handling multi-word expressions. The implementation of these tools is driven by NLP, ML and Semantic Web technologies. A semantic search engine was developed and validated against other popular and comparable engines with a corpus of 420 (out of about 800) documents used in the UK for compliance checking of building designs. In every search scenario, our search engine performed better on all objective criteria. Limitations of the approach are discussed which includes the challenges around licensing for all the documents in the corpus. Further work includes improving the performance of SPaR.txt (the tool created to identify multi-word expressions) as well as the information retrieval engine by increasing the dataset and providing the model with examples from more diverse formats of regulations. There is also a need to develop and align strategies to collect a comprehensive set of domain vocabularies to be combined in a Knowledge Graph.

Exploring The Impact of Internet of Things (Iot) Technologies on Tax Compliance: Opportunities, Challenges and Policy Implications

The proliferation of Internet of Things (IoT) technologies has revolutionized various aspects of modern life, including business operations and government services. This research paper delves into the intersection of IoT technologies and tax compliance, aiming to elucidate the opportunities, challenges, and policy implications arising from their integration. Through a comprehensive review and analysis of existing literature, this paper seeks to provide insights into the transformative potential of IoT in tax compliance, while also highlighting potential hurdles and offering policy recommendations. IoT technologies offer a myriad of opportunities to enhance tax compliance processes for both taxpayers and tax authorities. The ability of IoT devices to collect real-time data and automate various tasks can streamline recordkeeping, improve accuracy in reporting, and enable proactive compliance measures. For taxpayers, IoT-enabled systems can simplify tax documentation, minimize errors, and provide timely reminders for compliance deadlines. Likewise, tax authorities can leverage IoT data for better monitoring, enforcement, and risk assessment, thus enhancing overall tax administration efficiency. However, the integration of IoT in tax compliance also presents notable challenges. Privacy and data security concerns emerge as paramount considerations, given the sensitive nature of financial information involved in taxation. The vast amount of data generated by IoT devices also raises questions regarding data governance, ownership, and liability. Moreover, the digital divide may exacerbate existing disparities in access to IoT technologies, potentially marginalizing certain segments of the population and hindering equitable tax compliance. In light of these opportunities and challenges, policymakers must carefully design regulations and guidelines to harness the benefits of IoT while mitigating associated risks. Clear standards for data protection, encryption, and user consent are essential to safeguard taxpayer privacy and instill trust in IoT-enabled tax compliance systems. Additionally, implementation of measures to bridge the digital divide and ensure equitable access to IoT technologies, thereby promoting inclusivity in tax compliance efforts. Furthermore, tax authorities need to develop robust frameworks for data governance, encompassing data collection, storage, sharing, and usage. Collaboration between public and private sectors is crucial to foster innovation in IoT-based tax compliance solutions while adhering to regulatory requirements and ethical principles. In conclusion, the integration of IoT technologies in tax compliance holds immense potential to revolutionize the way in which taxes administered and enforced. However, realizing this potential necessitates a holistic approach that addresses technical, legal, and socio-economic considerations. By embracing innovation while safeguarding privacy and inclusivity, governments can harness the transformative power of IoT to foster more efficient, transparent, and equitable tax systems.

The Impact of AI on Real-Time Payments (RTP) for Small and Medium-Scale Enterprises' Performance in the United States of America

This study evaluated the impact of Ai on real time payment (RTP) for small and medium scale enterprises performance in United States of America with the aim of answering the research questions on how AI technologies adopted by US SMEs have facilitated access of payment rate, the extent at which AI technology impact the performance of US SMEs using a literature review approach. Findings from the analysed data confirmed that the rate of digital payments transactions in the US is growing on a fast rate which was also confirmed by recent empirical studies conducted after 2020 period stressing that the adoption of AI technologies by U.S. SMEs significantly impacts their access to payment rates particularly during the periods after the pandemic. This study also revealed that the impact of AI on the performance of SMEs in the United State appears to be unstable with the sampled periods of 2019 to 2024 with larger percentage of the sampled respondents in 2024 indicating that their business was in a favourable condition. On the final note, the findings revealed that the integration of artificial intelligence (AI) technology significantly enhances risk management for small and medium-sized enterprises (SMEs) in the US majorly in terms of influencing informed decision making, operational efficiency and response to market fluctuations. This study thereby recommended that SMEs in US should keep adopting AI technology for transaction processing, decision making, market forecast, fraud detection, compliance processes, credit risk management, data quality monitoring and operational resilience.

The impact of artificial intelligence on regulatory compliance in the oil and gas industry

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

Addressing Regulatory Risks in Fintech through Decentralized Technologies

The fintech industry, characterized by rapid innovation and disruption of traditional financial services, faces significant regulatory challenges that can hinder its growth and sustainability. Managing these regulatory risks is crucial to ensuring compliance and maintaining trust within the industry. Decentralized technologies, particularly blockchain, have emerged as potential solutions for enhancing regulatory compliance through increased transparency, security, and automation. This study aims to analyze the role of decentralized technologies in addressing regulatory risks in the fintech sector. Utilizing the SmartPLS method for data analysis, the research examines the relationships between decentralized technology adoption, compliance automation, and regulatory risk mitigation. The findings reveal that decentralized technologies significantly reduce regulatory risks by automating compliance processes and enhancing transparency. These insights offer valuable implications for fintech companies and regulators, suggesting that the integration of decentralized technologies can be a strategic approach to managing regulatory challenges in the industry.

CRITICAL FACTORS AFFECTING THE SUCCESS OF SHARI‘AH GOVERNANCE: INSIGHTS FROM ISLAMIC FINANCIAL INSTITUTIONS

This study examines the critical role of Shariah governance, particularly the impact of Shariah supervisory boards (SBBs) within the corporate governance framework of Islamic financial institutions (IFIs). SSBs, as critical components of Shariah governance mechanisms, significantly influence Shariah compliance processes within IFIs. This study explores the unique systemic risks inherent in the Islamic financial environment, focusing on the complexities and elements defining SSB legitimacy. The IFSB recommends four essential components for Shariah governance systems, emphasizing the importance of SSBs. However, SSBs face challenges balancing SharÊ‘ah adherence and market dynamics, highlighting a conflict between business objectives and Shariah rules. This study acknowledges the limited strength of Shariah governance, advocating for universally adopted standards to address issues such as conflicts of interest within SSBs and the need for diversified skill sets. This paper assesses the effectiveness of Shariah governance and SSBs in IFIs. For this purpose, a survey was conducted among middle and senior-level managers and employees employed in IFIs or associated regulatory entities across diverse nations. The results highlight the critical role of SSB members, specific standards for board nominations, and the moderate influence of stakeholders' interests on Shariah governance success. Shareholders’ and customers’ interests show no significant correlation with Shariah governance success.

An Analysis of Managing the Cloud: Obstacles and Solutions for Efficient Administration and Protection

Cloud management offers possibilities and difficulties to enterprises looking for effective management and strong data security. This essay examines the challenges associated with cloud administration and suggests workarounds for these issues. Assuring data security, upholding legal compliance, cutting expenses, and overseeing intricate multi-cloud setups are important obstacles. The use of cloud-native monitoring and governance tools, the implementation of extensive data backup and recovery plans, the use of automation for resource provisioning and administration, and the implementation of strong security measures like encryption and access restrictions are all necessary for finding solutions. Organizations may improve the security of their important data assets and expedite their cloud administration procedures by tackling these challenges with suitable solutions. Ensuring data security is a major concern in cloud administration. Organizations are concerned about data breaches, illegal access, and industry regulatory compliance when they move critical data to the cloud. Organizations need to have strong security mechanisms like identity management, access restrictions, and encryption in place to lessen these risks. Furthermore, implementing a defense-in-depth strategy that incorporates several security tiers might improve cloud environments' overall resilience. Upholding legal requirements and industry norms is another challenge for cloud managers. To secure sensitive data and uphold client confidence, businesses in regulated sectors like healthcare and finance are subject to strict compliance regulations. Adherence to frameworks like HIPAA, GDPR, and PCI DSS, as well as thorough policies and frequent audits are necessary to achieve compliance in the cloud. Organizations may minimize the risk of non-compliance penalties and expedite compliance processes by putting governance tools and compliance automation systems into place. For enterprises using cloud resources, cost optimization is another major concern. Pay-as-you-go cloud services might result in overspending and financial restrictions due to improper resource utilization.

Best practices for business process automation description – a case study

ABSTRACT Organizations in competitive, regulated environments must enhance business processes for efficiency, quality, and compliance while minimizing risks and costs. Process automation solutions play a vital role in achieving these goals, though the variety of tool descriptions creates challenges for compatibility and interoperability. This hinders innovation and competitiveness. The adoption of standard specifications or widely accepted best practices for automation descriptions offers a solution. This research aims to identify a set of best practices to guide process-oriented organizations in evaluating their current automation practices, ensuring alignment and fostering improvements in business process automation.

How Iran’s Hospital Accreditation Program Meets the International Society for Quality in Health Care (ISQua)

Background: Hospital health service quality is a fundamental component of the health system. Hospital accreditation is a key approach to enhancing this quality, necessitating the correct implementation of processes. Objectives: This study aims to investigate the compliance of the executive processes of Iran’s hospital accreditation program with the requirements of the International Society for Quality in Health Care and to provide corrective solutions. Methods: This qualitative study uses an inductive content analysis technique. A checklist with seven axes was established by extracting and interpreting the requirements of the executive processes from the International Society for Quality in Health Care. By examining existing documents at the Ministry of Health’s Accreditation Office and conducting interviews with managers and experts in the field, challenges in the domain of the executive processes of Iran’s hospital accreditation program were identified using the relevant checklist. Corrective solutions were then gathered through semi-structured interviews with 19 experts, managers, and accreditation field specialists, categorized inductively, and presented using MAXQDA software. Results: Challenges in seven areas were identified, including governance; strategic, operational and financial management; risk management; human resources management; information management; survey and client management; and the granting of accreditation approval. Subsequently, solutions were categorized into five groups encompassing 26 concepts, focusing on reforming macro policies, addressing implementation challenges, fostering appropriate interactions, improving human resource management, and promoting evidence-based decision-making. Conclusions: The Iran hospital accreditation program’s adherence to International Society for Quality in Health Care standards is inadequate. Establishing a semi-autonomous, non-profit accreditation body that operates under government control and ensuring the independence of the accreditation office is essential for achieving higher compliance and receiving certification from International Society for Quality in Health Care.

The role of AI and emerging technologies in global trade compliance

Artificial intelligence (AI) in global trade compliance is evolving rapidly, notably in classification, with the potential to unlock productivity, expand expertise and improve speed, reliability and accuracy. Due to its potential, AI is met with high expectations and sometimes apprehension, as there is a lot of uncertainty about what constitutes AI and how it will affect global trade stakeholders. The aim of this paper is to demonstrate how AI will likely affect customs agencies, importers/exporters, service providers and compliance managers. By using examples and case studies, the paper explains: 1) the role of AI expert systems (ES) that use machine learning (ML) and natural language processing (NLP); 2) AI’s potential and limitations in sourcing compliance knowledge for streamlining and automating global trade compliance activities; 3) the importance of upskilling compliance managers and compliance teams to successfully deploy AI, mitigate risks and better manage the global trade compliance process; and 4) our six steps for successfully implementing AI in your global trade compliance department. You will learn how AI can be valuable in performing your job and meeting your goals, if it is kept within its range of capabilities. By such measures, you will be able to obtain the advantages presented by the use of AI, while mitigating its very present risks, some of which are known and many of which are unknown.

Three Issues of Data Compliance Governance in China Based on Case Analysis

With the rapid development of China’s digital economy and increasingly stringent global data protection regulations, corporate data security and compliance have become key factors that determine the competitiveness and sustainable development of enterprises. However, China’s data compliance governance still faces three main challenges: inadequate self-regulation of corporate data compliance, imperfect internal management system, and the risk of violations throughout the entire data lifecycle. To address these issues, China needs to comprehensively improve data security and compliance of Chinese enterprises by improving the system to strengthen corporate behavior guidance, intensifying internal compliance mechanisms, and identifying the compliance process for data processing. Based on the world’s most typical legal systems related to data security and corporate compliance, and drawing on local Chinese judicial cases, this paper provides an in-depth analysis of the current challenges faced by Chinese enterprises in data security compliance. Finally, the paper explores and proposes a comprehensive governance path. Through the implementation of the comprehensive measures proposed in this study, Chinese enterprises now have practical guidance in the increasingly complex data security environment, thereby promoting their stable and sustainable development. Keywords: Data Compliance, Chinese Enterprises, Corporate Governance, Information Security, Compliance

Towards an ontology for process compliance with the (machinery) legislations

Towards an ontology for process compliance with the (machinery) legislations

A compliance assessment system for Incident Management process

The Incident Management (IM) process is one of the core activities for increasing the overall security level of organizations and better responding to cyber attacks. Different security frameworks (such as ITIL and ISO 27035) provide guidelines for designing and properly implementing an effective IM process. Currently, assessing the compliance of the actual process implemented by an organization with such frameworks is a complex task. The assessment is mainly manually performed and requires much effort in the analysis and evaluation. In this paper, we first propose a taxonomy of compliance deviations to classify and prioritize the impacts of non-compliant causes. We combine trace alignment techniques with a new proposed cost model for the analysis of process deviations rather than process traces to prioritize interventions. We put these contributions into use in a system that automatically assesses the IM process compliance with a reference process model (e.g., the one described in the chosen security framework). It supports the auditor with increased awareness of process issues to make more focused decisions and improve the process’s effectiveness. We propose a benchmark validation for the model, and we show the system’s capability through a usage scenario based on a publicly available dataset of a real IM log. The source code of all components, including the code used for benchmarking, is publicly available as open source on GitHub.