Technical Analysis: Fintech Modernization and Social Trust

Event-driven architecture (EDA) has emerged as a transformative paradigm in modern software systems, revolutionizing how organizations handle real-time data processing and system scalability.This comprehensive article examines recent innovations in EDA that have significantly enhanced system performance and reliability Rincy Soman https://iaeme.

This study examined how workplace safety compliance affects organisational sustainability of oil and gas servicing companies in South-South Nigeria. It has a particular focus on understanding the role of regulatory compliance for safety and safety training in achieving both economic and social sustainability. Using cross-sectional survey research approach, data was collect from management and operational staff at selected firms and it used multiple regression to analyse the data. The results found that safety compliance in terms of regulations and safety training significantly contribute to the economic sustainability of such companies, accounting for around 52% of the variation. Similarly, both regulatory safety compliance and safety training had strong positive effects on social sustainability, and can explain around 55% of the variation. In conclusions, the findings point out that organisations with robust safety policies and compliance to regulations and continuous training of employees will be more likely to enjoy an operational stability, profitability, and better relationship with stakeholders in the long term. Based on the results of the study, it is recommended that the oil and gas servicing companies institutionalise continuous safety education and regular retraining programmes to enhance worker competence and awareness. In addition, regulatory bodies should increase monitoring and make compliance certification and incentives tied to the demonstrated safety performance of firms. By incorporating safety code into corporate strategy, it is possible in the case of the oil and gas industry for sustainable growth and social responsibility to both continue mutually.

The financial services industry is experiencing a profound transformation through the adoption of asynchronous cloud computing technologies. This comprehensive article examines how cloud-native architectures are revolutionizing various financial sectors, from banking and payment systems to wealth management and security compliance. The article explores the fundamental architectural components that enable this transformation, including distributed processing systems, message queuing mechanisms, and event-driven architectures. Through detailed examination of sector-specific implementations, this article demonstrates how financial institutions leverage cloud technologies to enhance operational efficiency, improve customer experience, and maintain regulatory compliance while reducing costs. The article also addresses critical technical considerations and best practices in scalability, reliability, and security, providing insights into the successful implementation of cloud computing in financial services.

In the realm of digital advertising, trust and transparency are vital for sustainable and effective marketing strategies. However, the industry grapples with challenges such as ad fraud, data privacy violations, and opacity in ad placements. Blockchain technology emerges as a promising solution to decode trust and foster transparency in digital advertising. Blockchain, initially developed for cryptocurrencies like Bitcoin, offers a decentralized and immutable ledger, ensuring data integrity without intermediaries. Applied to digital advertising, blockchain can revolutionize transparency by providing a tamper-proof record of ad transactions. Smart contracts automate agreements, enhancing accountability and ensuring fair compensation. Key findings reveal the potential benefits of blockchain in digital advertising. Firstly, it enhances accountability and transparency by tracing advertising funds and verifying ad authenticity. Smart contracts automate payments, reducing ad fraud and increasing supply chain accountability. Secondly, blockchain mitigates ad fraud by providing transparent ad transaction records and leveraging decentralized identity verification to authenticate users. Thirdly, transparency fosters consumer trust and confidence, empowering informed decision-making and benefiting the entire ecosystem. However, challenges persist, including technical scalability, interoperability, and regulatory compliance. Adherence to data privacy regulations and integration with existing ad tech infrastructure are crucial considerations. Organizational and cultural barriers, along with industry fragmentation, hinder blockchain adoption. Future directions and opportunities lie in tokenization, decentralized identifiers, and zero-knowledge proofs, promising solutions to existing challenges. Collaboration among stakeholders is essential to develop standards and protocols, overcoming barriers to blockchain adoption. Continued research, innovation, and collaboration are necessary to realize the full potential of blockchain in promoting trust and transparency in digital advertising. DOI: https://doi.org/10.52783/eel.v14i2.1539

Increasing functionality and complexity of technical products is resulting in complex damage symptoms and causes during customer usage phase. Especially in the automobile industry, complex damage symptoms are often traceable to multiple damage causes. This increases the importance of structured field product observation and field data analysis. For the OEM, the goals are high levels of quality and reliability that can be attained by comprehensive analysis of product performance, design and reliability over the whole product life cycle. This includes: • After product launch into the market: Early and accurate identification of possible and actual damage causes by analysis of failures provides knowledge of product failure behavior. This can be attained by analyzing the occurring failures based on small amount of field damage data. The cost implications should also be assessed. • Product life cycle: Detailed mapping and analysis of the long-term product failure behavior and reliability These generates the basis for a targeted introduction of further actions, for example rectification of faults in the field, product optimization, or the initiation of concentrated development approaches/-strategies for failure prevention (e.g.: COP strategies). Currently no industrial standard for reliability analysis can fulfill the requirements of a comprehensive reliability analysis over the entire product life cycle. Based on these requirements the FDA concept was developed by the Department of Risk Management and Safety Engineering at the University of Wuppertal, in cooperation with OEMs of the automotive industry. Key aspects of the FDA concept are as follows: • Integration of several statistical and organizational methods in a comprehensive process • Mapping product reliability over whole product life cycle • Statistical analysis of failure data — for example, relating to identification of possible damage causes, production batch differences or climatic influences • Usage of qualitative data and information of the value added network regarding potential damage causes that can reduce the costs of the technical analysis • Conjunction of the determination of the regress rate and detection of the critical failures (product quality analysis) in a comprehensive approach. Currently different requirements — made by the OEMs — avoid the conjunction, e.g. sampling procedures and sample size • Optimization of analysis costs in terms of sampling procedure, sampling analysis and technical analysis with respect to high detection rate of damage causes The paper outlines the process of the FDA concept. Moreover, the paper presents advanced methods (such as the DCD algorithm, WCF approach, OMSP concept and RAW concept; cf. chapter 2.2 and 2.3) and industrial methods (like Weibull distribution and Eckel candidate methods) to ensure the key aspects. Finally, the paper outlines the use of value added networks to gain more information about the damage causes before performing the technical reliability analysis. Therefore the FDA concept supports the mapping, identification and description of complex damage causes of complex components and systems in the automotive industry with the goal of early failure detection in the field and preventive reliability control of subsequent product generations.

Preserving the privacy of transactions and ensuring the regulatory compliance of transactions are two important requirements for blockchain-based financial applications. However, these two requirements are somewhat contradictory. Techniques for protecting transaction privacy, such as data encryption and zero-knowledge proof, generally make it difficult to regulate and audit the transactions. In this paper, we propose a system named zkFabLedger which enhances both the privacy and the auditability of the classic permissioned blockchain platform Hyperledger Fabric. The proposed system utilizes commitments and non-interactive zero-knowledge proofs to hide the detailed information of transactions while enabling the endorsing peer nodes to verify the regulatory compliance of transactions. Transactions are recorded on table-structured ledgers, so that the regulator can perform complex auditing of transactions. Moreover, we utilize the ring signature scheme and the secret handshake protocol to ensure the anonymity of the transaction sender while enabling the regulator to trace the sender’s identity. Simulation results demonstrate that the proposed system can balance well between privacy, regulation and efficiency.

The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), and zero-knowledge proofs (ZKPs). By eliminating reliance on centralized authorities, our system enhances data privacy, reducing personally identifiable information (PII) disclosure by 80% while ensuring compliance with AML and GDPR regulations. The integration of zk-SNARKs enables trustless identity verification with an average proof generation time of 12.5 seconds, significantly reducing the 3-5 day verification period required by traditional systems. Smart contract-based KYC automation eliminates intermediaries, cutting compliance costs by 40% and reducing fraud risk by 60%. Through comparative analysis, we highlight that decentralized KYC improves security, cost-effectiveness, and scalability compared to traditional models. Performance evaluation confirms that transaction throughput remains within acceptable blockchain limits, with gas costs stabilized at 35,000-55,000 Gwei per verification request. Despite challenges in regulatory adaptation and zk-SNARK scalability, the proposed model demonstrates the feasibility of Web3-driven identity management for trustless, privacy-preserving, and compliant financial ecosystems.

The development of information technology has driven the need for stronger security systems and guaranteed privacy on web-based platforms. Traditional verification methods such as passwords and two-factor authentication are increasingly seen as insufficient in facing the growing complexity of cyber threats. Zero-Knowledge Proof (ZKP) emerges as an alternative solution that enables identity verification without revealing sensitive information. This research aims to implement ZKP technology in web-based systems to enhance user security and privacy. The method involves the implementation of a simple XOR-based ZKP algorithm to prove identity without disclosing personal data, as well as a three-step verification mechanism between the verifier and the prover. Implementation results show that the system can prevent attacks such as man-in-the-middle and replay attacks, while maintaining data confidentiality during the authentication process. This study also identifies challenges in system efficiency and verification process integrity and offers technical solutions to support broader ZKP integration in digital platforms. Thus, the use of ZKP in web-based systems holds great potential to improve user trust and security in the digital era.

The Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework offers a governance model for nuclear energy that foregrounds trust, equity, transparency, and stakeholder inclusion. This paper provides an empirical synthesis of Q-NPT by integrating quantitative and qualitative evidence from recent nuclear energy studies and situating the framework within global policy contexts. The findings indicate that legitimacy in nuclear governance depends not only on technical and regulatory compliance but also on social trust, distributive fairness, and active stakeholder inclusion. The analysis further demonstrates Q-NPT’s applicability to emerging technologies—including microreactors and blockchain-based fuel management—highlighting its adaptability to contemporary governance challenges. Together, these insights advance Q-NPT from conceptual articulation toward an evidence-informed, socially robust foundation for legitimate and ethical nuclear energy governance.

As mobile banking continues to grow at an exponential rate, the financial industry is faced with a critical challenge: How to keep user credentials secure without compromising on efficiency. Password-based authentication is still dominant but has major limitations which compromise both security and user experience. These systems are susceptible to the most common attack vectors such as phishing, malware and man-in-the-middle attacks, especially if users are using weak passwords or sharing passwords. Additionally, mobile devices have limited input interfaces that are frequently sources of frustration and error. As a result, there is increasing interest in other more secure and convenient alternatives such as biometric and multi-factor authentication (MFA) to mitigate the inherent weaknesses of password-based systems. This systematic literature review, which covers studies from 2020 to 2025, provides a critical review of biometric authentication methods used in mobile banking. It analyses existing approaches, security risks and implementation practices adopted by major banks across the world. While biometric systems are more secure and user friendly than traditional systems, they also introduce new challenges in terms of privacy, spoofing and regulatory compliance. The review gives a detailed overview of the current advances, key issues, and emerging research directions, which will give valuable insight to the development of secure and easy-to-use authentication systems in mobile banking.

The rapid digitization of financial services has accelerated the transition from conventional banking models to intelligent, data-driven ecosystems powered by advanced computational technologies. Smart Banking Intelligence emerges through the strategic integration of Machine Learning, Internet of Things (IoT), and Cloud Analytics to enable real-time decision-making, predictive risk assessment, and personalized financial services. Massive volumes of heterogeneous data generated from transactional platforms, mobile applications, smart devices, and connected banking infrastructure demand scalable analytical architectures capable of handling high velocity, variety, and veracity. Machine learning algorithms enhance fraud detection, credit scoring, customer segmentation, and behavioral analytics, while IoT networks contribute contextual awareness through biometric authentication, geospatial monitoring, and device-level intelligence. Cloud-based infrastructures provide elastic computing resources, distributed storage, and high-performance analytics frameworks essential for deploying intelligent financial applications at scale. This chapter presents a comprehensive examination of integrated ML–IoT–cloud architectures for smart banking, highlighting system design principles, data acquisition pipelines, edge-cloud collaboration models, intelligent dashboards, and decision support systems. Critical challenges including cybersecurity threats, adversarial machine learning risks, regulatory compliance, data privacy preservation, and multi-cloud governance are analyzed to establish a secure and resilient operational framework. Emerging paradigms such as federated learning, explainable artificial intelligence, and edge computing are discussed as transformative enablers of next-generation financial ecosystems. The proposed conceptual framework advances scalable, secure, and regulation-aligned smart banking infrastructures capable of delivering adaptive, transparent, and high-performance financial intelligence.

The banking and financial sectors are transforming through the integration of blockchain and automated machine learning (ML). This chapter explores their combined potential to revolutionize traditional financial processes, enhancing efficiencies, transparency, and risk management. Blockchain's decentralized, secure nature ensures tamper-proof record-keeping and reduces fraud, while automated ML enables real-time data analysis, predictive modeling, personalized customer experiences, and efficient decision-making. This synergy streamlines operations, optimizes risk assessment, and offers tailored financial solutions. The chapter also addresses the impact on regulatory compliance, cross-border transactions, identity verification, and lending, while discussing challenges related to scalability, data privacy, and integration, providing a comprehensive understanding of the evolving financial landscape.

In the healthcare industry, maintaining high-quality standards is crucial to ensure the safety and efficacy of medical products. Good Manufacturing Practices (GMP) provide a set of guidelines and regulations that help ensure the consistent production, control, and quality assurance of pharmaceuticals, medical devices, and other healthcare products. This paper explores the significance of GMP in the healthcare industry, highlighting its background, objectives, role in safeguarding patient health and maintaining public trust. It examines the key principles of GMP, its regulatory framework, and its impact on various aspects of the healthcare sector, including drug manufacturing, quality control, product safety, and regulatory compliance. Furthermore, the paper discusses the challenges and future perspectives of GMP implementation, emphasizing the need for ongoing advancements to meet the evolving demands of the industry. To summarize, adherence to GMP standards is crucial for the healthcare industry to produce high-quality, safe, and effective healthcare products. GMP compliance promotes patient safety, regulatory compliance, and innovation in the pharmaceutical industry.

Accurate identification and management of medical devices is of particular importance to ensure patient safety and regulatory compliance within healthcare systems. This paper presents a comprehensive exploration of medical device data retrieval, focusing on the integration of web scraping and API technologies. The utilization of Unique Device Identifiers (UDIs) and the Global Medical Device Nomenclature (GMDN) system is emphasized to enhance device authentication, attribute verification, and accurate categorization. This paper introduces a state-of-the-art code implementation that combines web scraping techniques and API integration to address the challenges of retrieving and verifying device information. The code facilitates both access to data and for healthcare professionals and stakeholders to make informed decisions based on reliable and up-to-date information. This is a significant and defining advance in the field, offering a powerful solution that is innovative as well as vital. The paper concludes by discussing the potential impact of these developments on patient safety, regulatory compliance, and the overall advancement of healthcare technology. In addition, the importance of accurate device identification, the role of UDIs and GMDN, and the significance of the provided cutting-edge code are highlighted, providing valuable insights into the field of medical device data retrieval.

The growing complexity of global food supply chains, coupled with stringent demands for safety, authenticity, and regulatory compliance, necessitates innovative approaches leveraging advanced technologies. METROFOOD-IT addresses these challenges by integrating cutting edge analytical techniques such as NIR/IR spectroscopy, GC-MS, and microbiome sequencing with a dedicated High-Performance Computing (HPC) infrastructure (Urania), optimized for machine learning (ML) and big data analytics. Practical applications demonstrate the system’s effectiveness, from detecting olive oil adulteration using deep learning to verifying PDO products through interpretable AI techniques. The implementation of Explainable AI methods ensures transparency in predictive models, addressing both scientific rigor and consumer trust requirements. The METROFOODIT approach not only enhances current quality control measures but also provides a scalable model for future applications in the agri-food sector, setting new standards for data-driven food analysis and regulatory compliance. The findings underscore the project’s potential to reshape industry practices while maintaining scientific accountability and operational efficiency.