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Digital Identity Verification: Transforming KYC Processes in Banking Through Advanced Technology And Enhanced Security Measures
Abstract
The digital transformation of the banking sector has ushered in a new era of opportunities and challenges, particularly in the realm of identity verification. This article delves into the evolution and significance of digital identity verification mechanisms, especially in the context of KYC (Know Your Customer) processes, which are pivotal for ensuring the security and integrity of financial transactions in the digital age. Through a comprehensive literature review, the article underscores the convergence of technologies such as machine learning, 5G communication, and blockchain in shaping the future of digital identity verification in banking. Case studies provide practical insights into the implementation of these technologies, highlighting both their transformative potential and the challenges they present. The analysis of findings in the context of the literature review offers a deeper understanding of the real-world implications of digital identity verification technologies. The article emphasizes the benefits of these technologies, including enhanced security, efficiency, and improved customer experience, while also addressing challenges such as security concerns, interoperability issues, and evolving regulatory landscapes. Furthermore, the research underscores the importance of trust, transparency, and user-friendliness in digital identity verification systems. The conclusion reiterates the promising future of digital identity verification in banking while emphasizing the need for continuous innovation and adaptation to address emerging challenges. In essence, this article provides a holistic overview of the digital identity verification landscape in the banking sector, offering valuable insights for stakeholders, researchers, and policymakers interested in the digital transformation of banking and its implications for identity verification.
... Automated compliance management systems have demonstrated significant advancements in regulatory adherence within the banking sector. The research on digital identity verification indicates that financial institutions implementing automated compliance checking systems reduce manual verification workload by 82.5% while improving accuracy rates to 96.3% [9]. These systems successfully map and monitor compliance across an average of 12 different jurisdictions simultaneously, with audit trail generation reducing regulatory reporting time from an average of 48 hours to 4.2 hours per reporting cycle. ...
... Advanced monitoring and detection systems have shown remarkable capabilities in identifying and responding to security threats in digital identity systems. The banking sector research reveals that contemporary threat detection systems can identify potential security breaches with 98.2% accuracy and an average response time of 73 milliseconds [9]. Anomaly detection systems utilizing deep learning algorithms demonstrate the capability to process up to 725,000 events per second, with false positive rates reduced to 0.4%. ...
This comprehensive article explores the evolving landscape of identity security in the age of artificial intelligence, examining the challenges and opportunities presented by advancing technology. The article investigates the impact of AI-driven threats on digital identity systems, focusing on deep fake technologies, synthetic identity fraud, and current verification system limitations. Through detailed examination of cross-border compliance challenges, regulatory frameworks, and implementation considerations, the research presents strategic solutions for enhancing detection systems and integrating regulatory technology. The article encompasses advanced security controls, monitoring systems, and future considerations including quantum-resistant cryptography and decentralized identity systems. By synthesizing findings from multiple research studies, this article provides a framework for understanding and addressing the complex interplay between AI advancement and identity security, offering insights into effective risk mitigation strategies and technological solutions.
... Automated compliance management systems have demonstrated significant advancements in regulatory adherence within the banking sector. The research on digital identity verification indicates that financial institutions implementing automated compliance checking systems reduce manual verification workload by 82.5% while improving accuracy rates to 96.3% [9]. These systems successfully map and monitor compliance across an average of 12 different jurisdictions simultaneously, with audit trail generation reducing regulatory reporting time from an average of 48 hours to 4.2 hours per reporting cycle. ...
... Advanced monitoring and detection systems have shown remarkable capabilities in identifying and responding to security threats in digital identity systems. The banking sector research reveals that contemporary threat detection systems can identify potential security breaches with 98.2% accuracy and an average response time of 73 milliseconds [9]. Anomaly detection systems utilizing deep learning algorithms demonstrate the capability to process up to 725,000 events per second, with false positive rates reduced to 0.4%. ...
This comprehensive article explores the evolving landscape of identity security in the age of artificial intelligence, examining the challenges and opportunities presented by advancing technology. The article investigates the impact of AI-driven threats on digital identity systems, focusing on deep fake technologies, synthetic identity fraud, and current verification system limitations. Through detailed examination of cross-border compliance challenges, regulatory frameworks, and implementation considerations, the research presents strategic solutions for enhancing detection systems and integrating regulatory technology. The article encompasses advanced security controls, monitoring systems, and future considerations including quantum-resistant cryptography and decentralized identity systems. By synthesizing findings from multiple research studies, this article provides a framework for understanding and addressing the complex interplay between AI advancement and identity security, offering insights into effective risk mitigation strategies and technological solutions.
... Automated compliance management systems have demonstrated significant advancements in regulatory adherence within the banking sector. The research on digital identity verification indicates that financial institutions implementing automated compliance checking systems reduce manual verification workload by 82.5% while improving accuracy rates to 96.3% [9]. These systems successfully map and monitor compliance across an average of 12 different jurisdictions simultaneously, with audit trail generation reducing regulatory reporting time from an average of 48 hours to 4.2 hours per reporting cycle. ...
... Advanced monitoring and detection systems have shown remarkable capabilities in identifying and responding to security threats in digital identity systems. The banking sector research reveals that contemporary threat detection systems can identify potential security breaches with 98.2% accuracy and an average response time of 73 milliseconds [9]. Anomaly detection systems utilizing deep learning algorithms demonstrate the capability to process up to 725,000 events per second, with false positive rates reduced to 0.4%. ...
This comprehensive article explores the evolving landscape of identity security in the age of artificial intelligence, examining the challenges and opportunities presented by advancing technology. The article investigates the impact of AI-driven threats on digital identity systems, focusing on deep fake technologies, synthetic identity fraud, and current verification system limitations. Through detailed examination of cross-border compliance challenges, regulatory frameworks, and implementation considerations, the research presents strategic solutions for enhancing detection systems and integrating regulatory technology. The article encompasses advanced security controls, monitoring systems, and future considerations including quantum-resistant cryptography and decentralized identity systems. By synthesizing findings from multiple research studies, this article provides a framework for understanding and addressing the complex interplay between AI advancement and identity security, offering insights into effective risk mitigation strategies and technological solutions.
... Studies indicate that digital identity verification advances financial inclusion, that is significant for long-term economic development, additionally, to enhance security. Secured identity verification mechanism minimal frauds and cyber-attacks, protecting financial agencies from extensive losses and fostering over long-period sustainability Parate et al., (2023). Digital identity verification plays a leading role in minimizing the operating cost linked with Know Your Customer (KYC) and Anti-Money Laundering (AML) organizations, which steers to more influential and cost-effective operations, according to Yawalkar et al., (2023). ...
... Digital identity verification plays a leading role in minimizing the operating cost linked with Know Your Customer (KYC) and Anti-Money Laundering (AML) organizations, which steers to more influential and cost-effective operations, according to Yawalkar et al., (2023). Likewise, recent study by Parate et al., (2023) underscore exactly how digital identity verification could deliver safe and reliable access to financial platforms, subsequently extending financial services to relegated groups, particularly in developing economies. Since it fosters institutions to extend their clientele and encourage economic sustainability, this excessive accessibility is firmly related to the more generic goal of financial sustainability. ...
Digital transformation is profoundly reshaping the financial sector and enhancing financial sustainability through innovations such as blockchain, AI, ML, and IoT. This chapter explores how these technologies converge to foster a more resilient financial ecosystem. Financial sustainability traditionally involves maintaining financial health through effective governance and resource allocation. In the context of today's rapidly evolving technological landscape, the urgency of financial sustainability has increased, with strategic foresight becoming crucial for stability amid digital transformation. The chapter examines the impact of digital technologies, highlighting blockchain's role in ensuring transaction security, AI and ML's advancements in decision-making, and IoT's contributions to real-time data analysis. These technologies enhance the inclusiveness of the financial landscape but also introduce challenges such as data privacy concerns. Case studies of Goldman Sachs' Marcus and Kenya's M-Pesa illustrate how digital innovations promote sustainability.
... В начале необходимо кратко поговорить о верификации и ее современном виде в цифровых сервисах. Верификация -необходимый процесс для многих современных цифровых сервисов, поскольку она способствует обеспечению безопасности пользователей и защищает компании от мошеннических действий [8]. Процесс верификации может включать в себя разные уровни проверки, такие как подтверждение личности, аутентификация по биометрическим данным или многослойная авторизация через SMS-коды или электронную почту. ...
... Despite such measures traditional KYC/AML methods are still in-efficient. Excessive identity verification steps drive-up compliance costs, slow onboarding processes and annoy customers and financial institutions [12]. Moreover, centralized identity databases are such risky of security problems, exposure for financial data sensitive to breaches [13]. ...
This study explored the potential of blockchain technology in improving Know Your Customer (KYC) and Anti-Money Laundering (AML) processes within the financial sector. The study aims to conduct an investigation of the current state of existing literature surrounding the use of blockchain in KYC/AML, as well as the emerging trends in the space, it further aims to clarify the barriers to adoption, and that includes barriers such as regulatory concerns and technical challenges. An organized method that contains thorough literature reviews, well-defined inclusion and exclusion criteria, thematic analysis is used to examine the effectiveness and strategic implications of blockchain technology. Specifically analyzed topics include the improvements as concerns identity verification, advanced fraud detection functionalities and the integration of the blockchain technology within regulatory aspects. This research helps significantly not only in the academic domain but is also very useful for the financial institutions and regulatory bodies striving to utilize blockchain for a more efficient and secure compliance environment..
... Regulators are increasingly supporting fintech innovation by creating sandboxes, which are controlled environments where new digital banking solutions can be tested without immediate adherence to all regulatory requirements. The digitization of banking services has emphasized the importance of secure and reliable digital identification and authentication mechanisms (Parate et al., 2023). Regulatory frameworks have evolved to establish standards for secure user verification processes. ...
Digital banking has emerged as a transformative force in Africa, catalyzing financial inclusion, enhancing accessibility, and reshaping the financial landscape across the continent. This review delves into recent developments and challenges within the realm of digital banking in Africa. The analysis encompasses the adoption of advanced technologies, regulatory frameworks, and the impact of digital banking on financial services, with a keen focus on the unique challenges faced by African nations. Recent developments reveal a dynamic shift toward digitalization, driven by increasing internet penetration, mobile phone usage, and a growing tech-savvy population. Mobile money services, digital wallets, and online banking platforms have gained prominence, enabling individuals, including those in remote areas, to access financial services conveniently. The integration of innovative technologies, such as blockchain and artificial intelligence, has further expanded the scope of digital banking offerings, fostering financial empowerment. However, amidst the progress, challenges persist. Regulatory frameworks are evolving to accommodate the rapid pace of digital banking, ensuring consumer protection, data security, and the prevention of financial crimes. Infrastructure limitations, particularly in rural areas, pose obstacles to universal access. Additionally, concerns related to cybersecurity, privacy, and the digital divide demand strategic solutions to bridge the gaps and mitigate risks. This review critically examines the nuanced landscape of digital banking in Africa, acknowledging both its transformative potential and the hurdles that must be addressed. By understanding recent developments and challenges, stakeholders, including policymakers, financial institutions, and technology providers, can collaboratively work towards harnessing the full potential of digital banking to foster inclusive economic growth and financial stability across the diverse nations of Africa.
... The rapid embrace of remote work has underscored the need for robust and secure identity verification systems. Conventional authentication methods such as passwords and two-factor authentication are increasingly inadequate in safeguarding sensitive information in unmonitored environments, leaving them vulnerable to breaches, phishing events, and social engineering scams ( Parate et al., 2023). ...
With the increased adaptability of remote work patterns, secure and efficient identity verification has also become a paramount concern. The traditional model of authentication involving the use of passwords or security tokens fails to provide any continuous verification of the user and is also prone to hacking attempts. Behavioral biometrics, especially keystroke dynamics and mouse movement patterns, offer an effective alternative as they allow for unobtrusive user authentication that is based on the individual user's behavior and is therefore continuous. This paper explores the application of Long Short-Term Memory (LSTM) networks, a sequence-based AI model, for studying and differentiating behavioral biometrics. We are using freely available data sets of keystroke and mouse dynamics to design and test an LSTM based system which is capable of making a distinction between users and imposters. We have shown that LSTM networks are significantly better in handling time series data such as state transition sequences than other statistical machinery traditional machine learning methods, such as Random Forests and Support Vector Machines, where they scored 89% accuracy between normal user operations and abuse activity. This aspect of the research addresses the reason why LSTM networks are appropriate for live remote identity verification systems, which is their ability to learn long strips of sequences and the behavioral flow. The originality of the study is to expand secure authentication solutions based on artificial intelligence systems that can be integrated to facilitate remote working in any sector for enhanced security.
... Blockchain technology has significantly transformed the financial sector by revolutionizing transaction processes, bolstering security, and combating fraud [42]. ...
... In conclusion, the integration of advanced technologies in retail banking has ushered in a new era of financial services innovation (Parate et al., 2023). Machine learning and artificial intelligence have become indispensable tools for analyzing data, enhancing customer experiences, and optimizing operational processes. ...
The advent of data analytics has revolutionized the landscape of retail banking, propelling financial institutions to explore innovative ways to enhance customer insights and revolutionize financial services. This paper presents a comprehensive review of the intersection between data analytics and retail banking, focusing on the transformative impact on customer insights and financial services innovation. In the realm of customer insights, data analytics plays a pivotal role in deciphering patterns, behaviors, and preferences. By harnessing advanced analytical techniques, retail banks can glean actionable insights from vast datasets, enabling them to understand customer needs, predict trends, and personalize services. This not only facilitates targeted marketing strategies but also fosters a deeper understanding of customer journeys, thereby optimizing the overall customer experience. Financial services innovation within the context of retail banking is another facet deeply influenced by data analytics. The integration of analytics-driven technologies, such as machine learning and artificial intelligence, empowers banks to develop predictive models for risk management, fraud detection, and credit scoring. These applications not only streamline operational processes but also fortify security measures, safeguarding both the institution and its customers. Furthermore, the review explores the role of data analytics in driving product and service innovation. Retail banks leverage analytics to identify gaps in the market, create customized financial products, and enhance existing offerings. The ability to analyze customer behavior aids in tailoring products that resonate with specific segments, fostering customer loyalty and increasing market share. The review also delves into challenges associated with data analytics in retail banking, including privacy concerns, data security, and regulatory compliance. Striking a balance between extracting valuable insights and respecting customer privacy remains a crucial consideration for banks navigating the data analytics landscape. The synthesis of data analytics and retail banking heralds a new era of customer-centricity and financial services innovation. As financial institutions continue to harness the power of data, the review underscores the need for ethical considerations and robust data governance frameworks to ensure sustainable and responsible implementation of data analytics in the retail banking sector.
... This helps financial institutions comply with AML regulations and other regulatory requirements. For instance, blockchain-based digital identity platforms can store and verify customer identities, reducing the risk of identity theft and ensuring compliance with KYC regulations (Parate, Josyula, & Reddi, 2023). ...
In the digital age, safeguarding financial data has become paramount amid increasing cyber threats and data breaches. This chapter explores the transformative role of blockchain and distributed ledger technology (DLT) in enhancing financial data security. Through practical applications and detailed case studies, the chapter illustrates the real-world implementation and efficacy of blockchain and DLT in the financial sector. Security features intrinsic to these technologies, such as decentralization, cryptographic hashing, and consensus mechanisms, are explored in depth, highlighting their contributions to a more secure financial ecosystem. Furthermore, the chapter addresses the regulatory landscape surrounding blockchain and DLT, discussing both the challenges and opportunities posed by these innovations. By presenting a comprehensive analysis of the current state and future potential of blockchain and DLT, this chapter aims to provide valuable insights into how these technologies are reshaping the approach to financial data security and resilience.
... New studies of the modern period explicitly connect cryptocurrency with the areas of remittance and trade (Parate et al., 2023). As cryptocurrency become even more of an economic eminence, nations would hardly avoid it so it is becoming a universally accepted digital currency. ...
Cryptocurrencies are the digital currencies that gained popularity with blockchain technology because they were encrypted (Bitcoin, Ethereum) and changed global economic landscape profoundly. These one-of-a kind business conducts have become a major trend in the financial communities within an exceptionally short while, changing global economic activity. As useful as they are however, there continues to be high reluctance towards their use and practice due to vague ideas about the exactness of 'how it goes' So the present situation is that these knowledge gaps have become opportunities for development. This paper uses TAM to model the parsimonious adoption of awareness acceptance cryptocurrency. PLS-SEM was used to validate the model's results using a sample consisting of 332 people between eighteen and forty-two. The findings indicate a positive and statistically significant direct influence of cryptocurrency consciousness on the adoption of cryptocurrencies. With acceptance indicators toward cryptocurrencies, such as convenience and value are mediating elements for this positive relationship. The results, furthermore, strengthen the level of trust which is considered to be one crucial variable forming direct and indirect connections. These lessons reinforce the need for stimulating informed knowledge about cryptocurrencies that will help increase their prevalence in financial markets. Thereby, the development of awareness and myths about cryptocurrencies' adoption as well ease factors like usefulness or trust are to strengthen policymakers and financial sectors.
... As a result, the model might give these extreme values disproportionate weight, impairing its ability to make useful generalizations. In order to overcome this limitation, bid-price prediction models must be carefully preprocessed using methods like outlier detection and robust modeling [21]. This will increase the models' accuracy and resilience in various dynamic auction situations. ...
Fast-paced internet auctions require smart choices and real-time bidding. Both buyers and sellers in online auctions need price prediction. Accurate estimates let suppliers list products at the right time and type, and bidders choose. Data-driven decision-making requires advanced analytics. Selling prices are calculated first because auctions are dynamic markets with uncertain bid-by-bid evolution. Traditional papers focus on final sale prices, although interim bidding can aid. Strategic knowledge, value perception, and market enthusiasm drive bids. Bidder values and market interest determine bidding. The entire auction price prediction system that shows sophisticated bid history in this research fills this gap. Use a certified online auction dataset. To predict the auction form's eventual price and buyer bids, imported data must be cleansed and preprocessed to remove noise. Strong Z-scores eliminate outliers, ensuring bid progression analysis accuracy. Auction data comprises item, length, and open price. K-Fold cross-validation improves models across datasets. Assessors use bid forecast precision measure RMSE. The statistical basis predicts accurately, and sophisticated visualizations help stakeholders understand auction behavior. Finally, the bids are compared to historical data from auctions with the same number of bids to validate the system. To assure auction behavior, the paper evaluates predictions and bids. The bid value standard deviation is used to evaluate bids. These phases compare bids to auction history. Bids are visualized against auction data. Box and line graphs are popular. Compare produced bids to past auction data to understand bid distribution and dynamics. Auction players gain from its real-time predictions over other systems. The paper's holistic approach beats auction analytics. This paradigm provides a complete toolkit for forecasting bid-by-bid dynamics, interpretation, and visualization, not selling prices.
... In an era characterized by rapid technological advancements and an ever-evolving threat landscape, the banking industry finds itself at the forefront of digital transformation (Khattak, et al., 2023, Xie and Wang, 2023, Johri and Kumar, 2023. As financial institutions increasingly leverage technology to enhance services and operations, the critical need to safeguard sensitive information against sophisticated cyber threats becomes paramount (Adebukola et al., 2023, Parate, Josyula, and Reddi, 2023, Zhou and Li, 2023). In response to this imperative, the adoption of robust information security frameworks has gained prominence, with ISO 27001 emerging as a globally recognized standard for Information Security Management Systems (ISMS) (Buha, and Bjegović, 2023). ...
As the banking industry becomes increasingly reliant on digital technologies and faces evolving cyber threats, the adoption of robust information security frameworks is imperative. ISO 27001, a globally recognized standard for information security management systems, has gained prominence as a comprehensive framework for safeguarding sensitive data. This study explores the implementation and effectiveness of ISO 27001 within the banking sector, evaluating its impact on enhancing information security. It delves into the specific challenges and considerations unique to the banking industry, where the confidentiality, integrity, and availability of financial information are paramount. It examines the motivations behind adopting ISO 27001, the process of implementation, and the associated organizational changes required to align with the standard's principles. A critical aspect of the evaluation involves assessing the tangible benefits and outcomes resulting from ISO 27001 implementation. This includes improvements in risk management, incident response capabilities, and the overall resilience of information security controls within banking environments. The study also investigates the role of ISO 27001 in fostering a culture of security awareness among banking employees and stakeholders. Ethical considerations, compliance challenges, and the balance between security and operational efficiency are examined to provide a holistic perspective on the standard's impact. It concludes with insights into the future of ISO 27001 adoption in the banking sector, considering emerging technologies, regulatory developments, and the evolving nature of cyber threats. This research contributes valuable insights into the effectiveness of ISO 27001 as a strategic framework for information security in banking, offering practical implications for industry practitioners, policymakers, and stakeholders invested in fortifying the digital resilience of financial institutions Keywords: ISO 27001, Banking, Information Security, Ethical, Financial Institution.
... As a testament to its transformative potential, recent research highlights its increasing influence in various sectors of life, from remittances to cross-border trade (Parate et al., 2023). Given its rising economic significance, numerous countries are gradually recognizing and shifting towards cryptocurrency. ...
Cryptocurrencies—i.e., digital or virtual currencies secured by cryptography based on blockchain technology, such as Bitcoin and Ethereum—have brought transformative changes to the global economic landscape. These innovative transaction methods have rapidly made their mark in the financial sector, reshaping the dynamics of the global economy. However, there remains a notable hesitation in its widespread acceptance and adoption, largely due to misconceptions and lack of proper guidance about its use. Such gaps in understanding create an opportunity to address these concerns. Using the technology acceptance model (TAM), this study develops a parsimonious model to explain the awareness, acceptance, and adoption of cryptocurrency. The model was assessed using partial least squares structural equation modeling (PLS-SEM) with a sample of 332 participants aged 18 to 40 years. The findings suggest that cryptocurrency awareness plays a direct, positive, and significant role in shaping cryptocurrency adoption and that this positive relationship is mediated by factors that exemplify cryptocurrency acceptance, namely the ease of use and usefulness of cryptocurrency. The results also reveal that trust is a significant factor that strengthens these direct and mediating relationships. These insights emphasize the necessity of fostering an informed understanding of cryptocurrencies to accelerate their broader adoption in the financial ecosystem. By addressing the misconceptions and reinforcing factors like ease of use, usefulness, and trust, policymakers and financial institutions can better position themselves to integrate and promote cryptocurrency in mainstream financial systems.
... Additionally, this objective delves into the educational and therapeutic applications of Simon's Game, shedding light on how it can be leveraged to bolster cognitive development in students and extend therapeutic support to individuals with cognitive disorders. The research aims to uncover this iconic game's societal and cultural impact [37]. ...
... Spatial filtering and masking are interchangeable terms. Masking and filtering are interchangeable terms [133][134][135][136][137]. We merely address the image-level filtering process in this idea. ...
... Advanced data exploration with machine learning algorithms have been used in criminology studies, although prediction is still in its infancy. For reliable forecasting, more research is required [18]. Hidden Markov Model (HMM) parameters are chosen by mining the double-layered hidden states of past trajectories [19]. ...
Crime is a dangerous and global social issue. Crimes influence a nation’s economy, reputation, and quality of life. Innovative technology and novel approaches to crime analytics are needed to safeguard society from Crime. Predicting crime in a chosen place and visualizing crime data can assist law enforcement in preventing Crime. This paper uses Vancouver crime statistics from crime study resources. As vital as the final prediction, data pre-processing comes first. This paper cleans and feeds data with feature selection, null removal, and label encoding. Pre-processed data is used to build a training model utilizing KNN, Decision trees, classification, linear regression, and random forest algorithms. Sklearn’s mat plotlib library predicts and analyses after model creation. The crime dataset is graphed. This work offers a reliable machine-learning criminal case-prediction model. Systematic crime analysis and prediction identify crimes. Classification and resolution are difficult due to escalating criminal cases. Knowing local crime patterns helps crime-solving agencies. Machine learning and random forest can reveal local crime tendencies. This article predicts local crimes using crime statistics, speeding up criminal case classification and proceedings. This approach can anticipate high-crime locations and show crime-prone areas. Data mining finds useful information in unstructured data. Existing data anticipates new extraction. Using this method, we can analyze, detect, and estimate regional crime probability. It also discusses crime prediction and analysis using machine learning and data mining. Data mining and machine learning are becoming crucial in almost all fields, including crime prediction. Crime prediction and analysis are essential for detecting and reducing future crimes.
... The following steps need to be taken and completed on or before the employee's first day of work. It is not prescriptive and can be modified to fit your requirements [72][73][74][75][76][77]. While the new employee waits to sign on, you or a delegate should brief them on the other necessities of the first day on the job. ...
The goal of every company's orientation programme should be to help new hires feel at home and ready to contribute as quickly as feasible. Fast and efficient results can be achieved by including this procedure into the organization's larger training plans and making sure it receives adequate attention and management. Human resource management (HRM) begins with the orientation of new hires. It includes introducing the new hires to the company's culture and their coworkers for the first time. This study, titled "A Study on Effectiveness of Orientation Process", attempts to understand the importance and the effectiveness of the orientation process in Sign Ware Technologies. The purpose of this study is to examine how employees at Sign Ware Technologies felt about the company's orientation programme. To further explore the connection between orientation input and perception of employee output, this research is also helpful. For this study, we use a questionnaire to gather information from participants. For the purpose of this research, 100 workers will serve as the sample. This project is statistically analysed using simple percentages, one-way ANOVA, and correlation. We suggest an orientation period of two to five days for new hires. During employee orientation, the first stop is always Human Resources, where
Purpose: Unlicensed money lending has become a pervasive issue in India, deeply affecting the financial stability of vulnerable populations. Unlike regulated financial institutions, these lenders operate outside the purview of legal frameworks, exploiting borrowers through exorbitant interest rates that often range from 120% to 360% annually. 1 Known colloquially as “loan sharks” or “hudkhoors” they prey on individuals in desperate need of immediate cash, creating a vicious cycle of debt and financial ruin. The Karnataka Prohibition of Charging Exorbitant Interest Act caps interest rates at 18% annually, yet unlicensed lenders continue to charge far beyond permissible limits. 2 Furthermore, coercive recovery tactics such as harassment and threats exacerbate the plight of borrowers, leading to extreme consequences like suicides and mass migrations. 3 Despite existing laws, enforcement remains weak, allowing these predatory practices to thrive unchecked. Materials and Methods: This study adopts a qualitative legal research methodology combined with doctrinal analysis and technological assessment to explore how legal and technological frameworks can combat unlicensed money lending and improve tax compliance in India. Findings: The results of this research underscore the imperative need for wide-ranging policy reforms that deal with the causative factors of unregulated lending while promoting tax compliance through increased transparency. Unique Contribution to Theory, Practice and Policy: Tax Compliance, Increased Revenue Collection for Government, Reigning Unlicensed Money Lending.
This chapter delves into the historical development and transformative journey of cyber-intelligent markets since 1834. Employing a qualitative methodology and comprehensive literature review, the chapter traces the evolution from basic cybersecurity measures to advanced intelligence-driven ecosystems. Highlighted is the pivotal role of technological advancements in threat assessment and response, emphasizing the proactive, predictive capabilities enabled by sophisticated algorithms, AI, and machine learning. This research offers insights for cybersecurity professionals, business leaders, policymakers, and academics. Documented is the transition from reactive cybersecurity to dynamic, proactive strategies, underscoring the significance of continuous adaptation to evolving cyber threats. The chapter offers significant contribution to understanding the evolution and future trajectory of digital defense mechanisms in intelligent cybersecurity markets.
Artificial Intelligence (AI) has emerged as a transformative technology with significant potential to reshape various aspects of our lives, from healthcare and transportation to finance and entertainment. As AI becomes increasingly integrated into our society, it is essential to address the ethical considerations surrounding its deployment and regulation. While AI offers numerous benefits, such as improved efficiency, productivity, and decision-making capabilities, it also presents unique challenges and risks. Ethical considerations in AI encompass a range of principles and values that guide the responsible development, deployment, and use of AI systems. These considerations aim to ensure that AI technologies align with societal values, respect human rights, and promote fairness, transparency, and accountability. As AI systems become more autonomous and influence critical aspects of our lives, it becomes crucial to establish a framework that addresses the ethical implications and safeguards against potential harm. This outline will explore the key ethical principles in AI, the roles of various stakeholders, challenges and considerations in AI ethics, and different regulatory approaches to ensure ethical AI deployment and regulation. By examining these aspects, we can gain a deeper understanding of the complex ethical landscape surrounding AI and work towards developing responsible and beneficial AI systems for the benefit of society as a whole.
Fraud has become a pervasive and costly problem in the fintech industry, posing significant risks to financial institutions, businesses, and consumers alike. As fraudulent activities continue to evolve and become more sophisticated, traditional rule-based fraud detection systems struggle to keep up. However, advancements in technology present a promising solution to this challenge. This paper aims to explore the potential of harnessing the power of machine learning and behavioral analytics in enhancing fraud detection in the fintech sector. By leveraging these powerful tools, financial institutions can strengthen their defenses against fraudulent activities, mitigate risks, and protect their customers' assets and sensitive information. Machine learning, a subset of artificial intelligence, offers the ability to automatically learn from vast amounts of data, detect patterns, and make predictions or decisions without explicit programming. This capability makes it well-suited for fraud detection, where the detection of subtle anomalies and patterns is crucial. Behavioral analytics, on the other hand, focuses on understanding and analyzing user behavior to identify deviations from normal patterns. By establishing baseline behavior profiles and monitoring real-time activities, behavioral analytics can detect suspicious or fraudulent actions that may go unnoticed using traditional methods.
This study examines the integration of AI, Machine Learning (ML), Enterprise Resource Planning (ERP), and Oracle technologies in finance digital transformation. The inquiry focuses on the collaborative impact of these technologies, revealing crucial results and insights that highlight their transformational potential. The report demonstrates the interconnectedness of AI, ML, ERP, and Oracle technologies, highlighting their cumulative impact on predictive analytics, risk management, fraud detection, business process optimisation, and data synchronisation inside financial institutions. The findings of the investigation highlight the importance of these technologies in supporting data-driven decision-making, increasing operational efficiency, and boosting overall financial performance. The report also digs into new financial technology trends, offering a view into the future of AI, machine learning, ERP, and Oracle technology. It emphasises their expanding significance in transforming company models, consumer experiences, and market dynamics. The findings highlight the revolutionary influence of personalised client interactions, new business models, and collaborative industry ecosystems, which place financial institutions at the forefront of technological progress. The analysis's recommendations include technological, organisational, and cultural issues, with an emphasis on continual technological review, strategic alignment, and the development of a flexible and inventive organisational culture. The report emphasises the significance of comprehensive integration plans that capitalise on the synergies between various technologies, providing financial institutions with a road map for effective digital transformation.
In the digital age, power transmission systems play a crucial role in ensuring the reliable supply of electricity to homes, businesses, and industries. These systems are the backbone of critical infrastructure, supporting essential services and economic activities. However, as power transmission systems become increasingly interconnected and reliant on digital technologies, they also face heightened security risks and vulnerabilities.
Descriptive textual data analysis involves extracting meaningful insights and patterns from text data. With the exponential growth of textual data available in various domains such as social media, customer reviews, news articles, and scientific literature, it has become crucial to develop effective techniques for analyzing and understanding this unstructured data. Machine learning techniques play a vital role in exploring and extracting valuable information from textual data. Machine learning algorithms enable the automation of the analysis process by learning patterns and relationships within the text. They can handle large volumes of data, identify trends, and make predictions or classifications based on the patterns they discover. By leveraging machine learning techniques, descriptive textual data analysis becomes more efficient, accurate, and scalable. The primary objective of exploring descriptive textual data analysis using machine learning techniques is to gain insights into the underlying structures, themes, sentiments, and entities present in text data. This analysis can provide valuable information for various applications, including market research, sentiment analysis, customer feedback analysis, content recommendation systems, and information retrieval. By preprocessing textual data to remove noise, tokenizing the text into meaningful units, and applying feature extraction techniques, such as bag
The modernization of the electrical grid is imperative in meeting the evolving demands of our energy landscape. With the increasing integration of renewable energy sources, growing electricity demand, and the pressing need for sustainability, traditional grid systems are facing unprecedented challenges. In this era of rapid technological advancement, Artificial Intelligence (AI) emerges as a pivotal force driving the transformation of our grid infrastructure. AI, with its ability to analyze vast amounts of data, predict outcomes, and optimize operations, holds tremendous promise in revolutionizing grid management and operation. From predictive maintenance to dynamic load balancing and renewable energy integration, AI technologies are reshaping the way we conceive, manage, and utilize electrical grids. This essay explores the profound impact of AI on grid modernization efforts, delving into its applications, benefits, challenges, and future prospects. By harnessing the power of AI, we not only enhance the reliability and efficiency of our grid systems but also pave the way for a sustainable energy future. Definition of grid modernization Grid modernization refers to the comprehensive overhaul and enhancement of the electrical grid infrastructure to meet the evolving demands of the modern energy landscape. It involves the integration of advanced technologies, policies, and practices aimed at improving the reliability, efficiency, flexibility, and sustainability of the grid system. Grid modernization initiatives often include upgrades to infrastructure components such as transmission lines, substations, and distribution networks, as well as the implementation of smart grid technologies, renewable energy integration, energy storage solutions, and advanced monitoring and control systems. The ultimate goal of grid modernization is to create a more resilient, adaptable, and environmentally sustainable electrical grid capable of meeting the needs of today's society while accommodating future energy challenges and opportunities.
In modern computing, Federated Machine Learning (FML) and Database-as-a-Service (DBaaS) are key paradigms that fulfill the changing requirements of scalable, effective, and collaborative machine learning and data management. The difficulties in maintaining and analyzing distributed data have increased as businesses use distributed databases in DBaaS systems more and more for data storage and retrieval requirements. Federated machine learning emerged as a result of the simultaneous explosion of machine learning applications across multiple domains, which increased the demand for collaborative models.The crucial problem of data decoupling, or the separation of data from computer operations, is at the center of these developments. In the context of DBaaS FML, this separation is particularly important because it affects the efficiency, security, and scalability of distributed systems. The goal of this study is to thoroughly examine and contrast several data decoupling techniques in the context of DBaaS FML. This study explores various approaches including data sharding, virtualization, federation, and replication in an effort to determine the advantages, disadvantages, and real-world applications of each. For systems to be reliable, scalable, and cooperative, it is crucial to comprehend the complex interplay between data decoupling techniques and DBaaS FML. The following parts will examine the FML principles, the architecture of DBaaS, and a comparative study of data decoupling techniques, providing information on their effectiveness, security implications, and suitability for practical use. The results of this study will support well-informed decision-making, the creation of best practices for managing distributed data, and the promotion of collaborative machine learning across many databases as more and more enterprises implement DBaaS FML solutions. DATABASE-AS-A-SERVICE (DBAAS) Database-as-a-Service (DBaaS) is a cloud computing model that provides database management and access as a fully managed service. In traditional database deployment models, organizations had to set up, configure, and maintain their own database infrastructure, which often required significant investments in hardware, software, and skilled personnel. DBaaS simplifies this process by offering a cloud-based, on-demand database service, allowing users to focus on application development and data utilization rather than the underlying infrastructure.
Database-as-a-Service (DBaaS) and Federated Machine Learning (FML) represent pivotal paradigms in contemporary computing, addressing the evolving needs of scalable, efficient, and collaborative data management and machine learning. As organizations increasingly leverage distributed databases in DBaaS environments for their data storage and retrieval needs, the challenges associated with managing and analyzing distributed data have become more pronounced. Simultaneously, the proliferation of machine learning applications across various domains has fueled the need for collaborative models, leading to the emergence of Federated Machine Learning. At the nexus of these advancements lies the critical issue of data decoupling-the separation of data from computational processes. This separation is especially crucial in the context of DBaaS FML, as it influences the scalability, security, and efficiency of distributed systems. This research aims to comprehensively analyze and compare various data decoupling methods in the realm of DBaaS FML. By delving into methodologies such as data sharding, virtualization, federation, and replication, this study seeks to uncover the strengths, weaknesses, and practical implications of each approach. Understanding the intricate relationship between data decoupling methods and their impact on DBaaS FML is essential for devising robust, scalable, and collaborative systems. The subsequent sections will explore the architecture of DBaaS, the principles of FML, and conduct a comparative analysis of data decoupling methods, offering insights into their performance, security considerations, and applicability in real-world scenarios. As organizations increasingly adopt DBaaS FML solutions, the findings of this research will contribute to informed decision-making and the development of best practices in managing distributed data and fostering collaborative machine learning across diverse databases. DATABASE-AS-A-SERVICE (DBAAS) Database-as-a-Service (DBaaS) is a cloud computing model that provides database management and access as a fully managed service. In traditional database deployment models, organizations had to set up, configure, and maintain their own database infrastructure, which often required significant investments in hardware, software, and skilled personnel. DBaaS simplifies this process by offering a cloud-based, on-demand database service, allowing users to focus on application development and data utilization rather than the underlying infrastructure.
In the evolving landscape of financial analysis, Generative Artificial Intelligence (AI) has emerged as a pivotal tool for deriving predictive insights from vast historical data. This paper explores the transformative role of Generative AI in financial forecasting, focusing on its capacity to unveil future market trends and investment opportunities. Employing methodologies like Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs), the study demonstrates the enhanced predictive accuracy of these models compared to traditional financial analysis techniques. Key findings reveal Generative AI's potential in stock market prediction, credit risk analysis, and fraud detection, offering a broader and more dynamic perspective for financial decision-making. The paper also addresses the ethical considerations and challenges inherent in deploying AI in sensitive financial contexts. These insights underscore the growing significance of Generative AI in shaping future financial strategies and its potential to revolutionize risk management, personalized financial services, and regulatory compliance. This study contributes to the burgeoning field of AI in finance, highlighting the need for ethical deployment and offering a roadmap for future research and application in this domain.
Fraud detection in the fintech sector is a critical area of concern as financial transactions increasingly shift to digital platforms. This paper presents a comprehensive analysis of enhancing fraud detection in fintech by combining machine learning techniques, leveraging behavioral analytics, and adopting RegTech solutions. The objective is to develop a holistic approach that strengthens fraud prevention strategies, ensures regulatory compliance, and safeguards the interests of customers and financial institutions. The paper begins with an introduction that sets the context by highlighting the growing importance of fraud detection in the digital financial landscape. It outlines the research objectives, scope, and structure of the paper. Subsequently, the methodology section details the data collection process, the selection and comparative analysis of machine learning models, the integration of behavioral analytics, and the implementation of RegTech solutions. The paper concludes with a summary of findings and contributions, emphasizing the significance of adopting a holistic approach to fraud detection in the fintech industry. It underscores the need for financial institutions to embrace advanced technologies, comply with data privacy regulations, and collaborate within the industry to combat financial crimes effectively.
Openhole well logging encompasses a diverse range of measurements, including as measurement-while-drilling (MWD) logs, standard wireline logs, and mud logs. These measurements are the major source of formation evaluation data, and they are used in applications ranging from individual drilling-well appraisals to extensive reservoir description studies. They are also known as "logs." The technology for openhole well-logging is continuously being developed in response to the demand for increased precision in determining the features of reservoirs. Recent years have seen a
In order to get rid of voltage imbalances and voltage harmonics, a single-phase unified power quality conditioner (PV-UPQC) is discussed in this paper as a means of integrating photovoltaic systems into the grid. The current UPQC's DC-link voltage is insufficient to maintain a constant voltage. The proposed work combines the PV panel and UPQC. The boost converter runs the PV array in maximum power point tracking mode, which maximises power output. Matlab-Simulink is used to model the behaviour of PV-UPQC with nonlinear loads in the presence of varying irradiance and grid voltage fluctuations. To regulate photovoltaic (PV)-connected UPQC series and shunt inverters, a PLL-based control method is proposed. In addition to correcting for voltage and current fluctuations, the proposed controller can also detect phases and perfectly synchronise grids. The effectiveness and efficiency of PV-UPQC are studied through computer modelling.
Artificial intelligence is becoming increasingly significant in online education, image processing, identity verification, and document integrity checking. We designed an approach for identifying the optimal method and tools for speeding and improving AI-assisted image processing. This paper compares appropriate, practical concepts to help you choose. Our article studies various AI modelsto recognise data by processing various photos, enhancing accuracy, minimising administrative time, and decreasing the likelihood of misreading and mistyping. In our paper, we presented character recognition and face identification based on deep learning through processing a personal identification document. We divided the process into parts, taught different artificial intelligence models, produced the data necessary for teaching, and then integrated it into our developed environment. The completed software, which canbe adapted to any system in the form of an application, uses the image on the ID card and the device's camera to determine whether the application is being used by the person authorised to administer it and can optionally match the signature on the ID card with a digital sample requested by the application from its user.
The European Blockchain Services Infrastructure (EBSI) is a major policy initiative of the EU and European Blockchain Partnership, to leverage blockchain to create cross-border services for public administrations, businesses, citizens, and their ecosystems, to verify information and increase trust in services. The EBSI aims to provide a secure and interoperable system infrastructure, utilizing new digital technologies such as digital wallets, verifiable credentials, and decentralized identifiers. These technologies are expected to change the way government services are organized between citizens and service providers. This article presents empirical findings from a cross-border pilot that was the first case to utilize this new blockchain infrastructure, and which tested the institutional, technical, and user-specific requirements for wider adoption. The pilot tested and assessed the verification of education credentials through the EBSI blockchain in a cross-border setting between a Belgian and an Italian university. The research was based on action research with the ICT units of the participating universities, wallet solution providers, the Belgian government, and EBSI officials. The findings highlight the following as key challenges for the wider adoption of the EBSI and verification credential use case: (1) onboarding of the EBSI ecosystem governance, (2) issuance of EBSI-compliant digital wallets and data schemes for transcript validation, (3) interoperability issues concerning digital identity systems.
Introduction
The article aims to shed light on the process of shaping the relational space of work in the service triangle through the progressive digitalization of work in retail banking industry. It addresses the following research question: how do technological shifts affect the relationships and interactions (a) between employees and supervisors, and (b) between employees and customers? Through a close examination of the redesign of the interpersonal relationships from the subjective viewpoint of front-line workers across these two levels, the paper contributes to advancing the understanding of the impact of technologies on surveillance practices, work identity and professional ethics in a key working sector with regard to digitalization and changes in professional requirements.
Methods
The question is addressed through a qualitative case study of retail banking in Italy. In the (retail) banking sector, the redesign of the relations between supply and demand for services is more sensitive to the changes afforded by digitalization and learning algorithms. The study was conducted with the involvement of workers and trade unionists, with whom we embarked on a constant work of re-articulation through data collection, analysis, and conceptualization. We collected a multiplicity of data for triangulation: interviews, focus groups, documents, and ethnographic notes.
Results
Data analysis shows how work processes and interpersonal relationships start to be redesigned across the two levels. At (a) level, two main aspects are found: the measurement of individual performance within the logic of quantification, which reduces employees to a set of measured dimensions, pushing workers into conditions of stress and competition; new surveillance practices and forms of organizational control enabled by technologies and learning algorithms. At (b) level, from being an expert with specific knowledge in the financial sector the bank employee turns into a kind of seller of any product that the algorithm decides to sell, thus ignoring the value of situated experience held by embedded, embodied social actors. Moreover, algorithms enter jurisdictional spaces traditionally controlled by knowledge workers and produce unknown outcomes concerning to whom to sell which products that cannot be clearly understood by workers.
Discussion
Technology contributes to engendering complex identity constructions to maintain, protect, and revise professional identity.
This study aims to investigate the determinants of NDIV platform acceptance among young investors in Malaysia within the context of protection motivation theory and uncertainty reduction theory. The study proposed a conceptual model comprising five hypotheses tested using structural equation modelling-partial least squares. Data was collected through an online questionnaire survey from 361 young investors in Malaysia. The results show that acceptance of the NDIV platform is directly influenced by perceived severity, response efficacy, self-efficacy, and transparency. This study extended the protection motivation theory by incorporating uncertainty reduction theory to strengthen the predictions of NDIV platform acceptance among young investors in Malaysia. The study brought meaning by filling the theoretical, empirical, and methodological gaps in the body of knowledge. This research contributes to the administrators, regulators, industry practitioners, and government to improve the platforms' strategies and increase citizen engagement conclusions.
The real-world use cases of blockchain technology, such as faster cross-border payments, identity management, smart contracts, cryptocurrencies, and supply chain–blockchain technology are here to stay and have become the next innovation, just like the Internet. There have been attempts to formulate digital money, but they have not been successful due to security and trust issues. However, blockchain needs no central authority, and its operations are controlled by the people who use it. Furthermore, it cannot be altered or forged, resulting in massive market hype and demand. Blockchain has moved past cryptocurrency and discovered implementations in other real-life applications; this is where we can expect blockchain technology to be simplified and not remain a complex concept. Blockchain technology’s desirable characteristics are decentralization, integrity, immutability, verification, fault tolerance, anonymity, audibility, and transparency. We first conduct a thorough analysis of blockchain technology in this paper, paying particular attention to its evolution, applications and benefits, the specifics of cryptography in terms of public key cryptography, and the challenges of blockchain in distributed transaction ledgers, as well as the extensive list of blockchain applications in the financial transaction system. This paper presents a detailed review of blockchain technology, the critical challenges faced, and its applications in different fields. Blockchain in the transaction system is explained in detail with a summary of different cryptocurrencies. Some of the suggested solutions are given in the overall study of the paper.
Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.
Based on 5G wireless communication technology, this paper proposes a secure online banking payment scheme using a password algorithm and identity authentication+SMS verification code. We use the lightweight block cipher algorithm to construct a numeric permutation table. Then, we use the digital permutation table to perform the permutation encryption operation to obtain the digital ciphertext data. Finally, we connect 5G radiofrequency identification, identity authentication, and other modules to the trusted computing platform. The research found that the online banking security encryption algorithm proposed in the paper has the characteristics of high security and high efficiency.
Security is crucial in the healthcare industry. Between 2012 and 2019, almost 179 million health records were exposed to data breaches. The intruders got into homes and companies to take credit card and banking information, as well as health and genetic research data. Blockchain is a fantastic Review Article Gulati et al.; JPRI, 33(64A): 252-261, 2021; Article no.JPRI.80420 253 technology for applications in the area of protection because it can keep an incorruptible, decentralized, and open record of all patient data. It is, nevertheless, secure and keeps any person's identity hidden. When dealing with decentralized technology, patients, doctors, and hospital personnel may all share the same information. On a blockchain, the cost of verifying a single transaction is negligible. You can audit a single piece of data in real time, and it's entirely visible to everyone. As a result, costless verification is economically possible. Recent audits of healthcare data, for example, have necessitated the time-consuming and costly examination of tiny units of transactions. This procedure had to be interrupted and redone on a frequent basis prior to blockchains in order to comply with regulations. Compared to traditional healthcare information management solutions, blockchain provides five potential benefits. Blockchains are ideal for decentralized administration applications, such as when hospitals, consumers, and payers want to work together without the need for a middleman. The second advantage of blockchains is that they may offer immutable archives of sensitive data, which makes them ideal for static databases (e.g., insurance claim records). Blockchains are very useful for tracing the origins and histories of digital artifacts (e.g., patient consent in clinical trials). According to cryptographic protocols, changing the ownership of an item can only be done by the person who owns it. Furthermore, the attributes' origins may be traced, making previously confirmed evidence considerably more reusable. The fourth advantage of blockchains is that they ensure the longevity, consistency, and accessibility of records. As a result, they're perfect for long-term records storage and availability (e.g., the electronic health records of patients). Patients' personal keys encrypt the data, which is only accessible with their consent, making it safer and more private. There is no method to access personal information even if the network is hacked by a hostile entity.
The falsification and embezzlement of personal data are still found in several cases in the past year. The reason is personal data in physical form are easily manipulated and difficult to be distinguished from the original. The most detrimental impact is if a person’s personal data is used for credit application fraud in the banking industry. Implementation of blockchain technologies, one of which is Ethereum, allows the use of contracts as a rule that must be fulfilled by the parties involved. All stored transactions are perpetual (cannot be deleted or changed), easy to be audited, transparent, and distributed at each participating node. This study aimed to develop a smart contract for personal data transactions with a case study of credit submission at the Bank. The authors developed a trial application using the prototype method in the process of assessment. Assessment was done by black box testing method in the scope of the lab with 10 credit submission data to be transacted. It is resulting in all credit submission data can be transacted and stored in the smart contract. The interview resulted in an opinion that blockchain technology can be used to store personal data and submit credit submissions at the Bank. The results of the analysis on assessments and interviews conclude that blockchain technology can be used as a medium to store personal data and secure credit applications. For future research, testing transactions can be done on Testnet networks with changing several blocks of data.
Blockchain technology promises to be hugely trending and empowering in financial domain computing applications. The digital economy is becoming an integral part of modern life. So as the use of the digital world increases there are more chances to decrease the security level. So more the use of digitization more the frauds and less the security. In some cases of personal data, leakage has brought back into the focus the security issues with the different identity sharing mechanisms. A customer is expected to provide his identity for authentication by different agencies. So theKYC process deals with the identification of the user. And in turn, provides the required security. The KYC procedures which are used by the banks are completely dependent on the encryption which is slow and it can lead to the loss of customer details to other theirs party financial institutions. This system can be efficient by using Blockchain technology, which has the potential to automate a lot of manual processes and it is also resistant to hacks of any sort. The immutable blockchain block and its distributed ledger is the perfect complement to the process of KYC. With the addition of smart contacts, fraud detection can be automated. For KYC identity details storage we can make use of any types of KYC. So, the banks can develop a shared private blockchain within the bank premise and the same can be used for verifying the documents. This allows the user to get control of their sensitive documents and also makes it easier for banks to obtain the documents they need for compliance.
One of the latest innovations that has caught the attention of generation Z is the introduction of the digital identity card. As a substitute for physical ID cards that have been around for a long time, digital ID cards offer various benefits and attractive potential for generation Z. In this approach, a digital ID cards are a digital version of a person's identity that is stored electronically, with personal information such as name, address, and number identity. However, a digital ID card is not just a substitute for a physical ID card but a tool that enables generation Z to make wider use of information technology. This study aimed to explore the use of digital national identity cards in generation Z. This research is qualitative research that explores and describes in detail a situation or phenomenon of the research object being researched by developing concepts and gathering existing facts. Raw data is analyzed and organized by data collection date, data source, data type, data description, and data nature. All data must be read in order to find out what data has been obtained, the source of the data, and its meaning. Overall, generation Z has a positive opinion of digital ID card services. They see it as convenience and convenience to access their identity electronically, avoiding the need to carry physical documents all the time and speeding up administrative processes. Generation Z also appreciates the administrative efficiency offered by digital Identity Card services, enabling them to process ID card extensions or personal data changes more easily and quickly. Apart from that, generation Z also appreciates the integration of digital ID card services with other digital platforms. They can integrate their identity with banking services, e-commerce, or online ordering platforms, enabling them to make transactions easily and quickly. Generation Z is also aware of the positive impact of using digital ID cards on the environment by reducing the use of physical documents and contributing to sustainability.
Hand verification and phony discovery is the process of vindicating autographs automatically and incontinently to determine whether the hand is real or not. There are two main kinds of hand verification static and dynamic. stationary, or offline verification is the process of vindicating a document hand after it has been made, while dynamic or online verification takes place as a person creates his/ her hand on a digital tablet or an analogous device. The hand in question is also compared to former samples of that person's hand, which set up the database. In the case of a handwritten hand on a document, the computer needs the samples to be scrutinized for disquisition, whereas a digital hand which is formerly stored in a data format can be used for hand verification. The handwritten hand is one of the most generally accepted particular attributes for verification of identity, whether it may for banking or business. The sub-sphere of machine literacy that's deep literacy allows us to train a model with the help of deep literacy algorithms. This is enforced in a complicated neural network model trained to classify and descry the forged hand from a collection of image samples which consists of two different sets of images, say real and forged image sets. We constructed a complicated neural network model from scrape to prize features from a given dataset. The image is given as input, and it tells whether a hand is forged or not. This will help descry the fraud. KEYWORDS: CNN, Forgery, Signature
Banks are now the almost sole source of confidence for internet commerce when it comes to processing electronic payments. With a peer-to-peer electronic currency, payments can be conducted online directly between parties without going via a banking organisation. While signatures do contribute in some ways, the main benefits are lost if a trustworthy third party is still required to prevent double spending. So in this project we can implement Bit Coin based banking system can be implemented leveraging the technologies of block chains to create hash functions. Bit coin is a crypto currency, which is not supported by the government or central bank of any nation. It can be traded for goods or services with vendors who the use of bit coins payment. These bit coins are the blocks of secure data. It takes a lot of CPU resources to securely verify each individual transaction as the data is passed from one person to another while also spending money on the transaction. The P2P network monitors and verifies the moving of bit coins between users. Bit coin is more secure than other currencies in terms of cryptographic implementation, and it is difficult to carry out fraudulent transactions. In a Bit coin transaction, the block chain will connect every user on the network, and each time a transaction is entered, the network will broadcast it to all other users after it has been validated. The network will also have a copy of every transaction. The network will group transaction data into blocks and broadcast them throughout the network rather than preserving any transactions in the block chain. Every block in this chain will link to the one before it, which is known as the genesis block. Peer-to-peer networks and a consensus mechanism are used in block chain systems, eliminating the potential of data alteration.
In this work, we propose a novel workflow to tackle the problem of verifying individuals’ identities online assuming banks as a central source of truth. The existing process for this involves auth mechanisms from privately held companies, where the data do not have any physical backing by an authorized entity, reducing its credibility. Alongside, the online verification methods used are unsophisticated, like passwords and OTPs, and anyone can easily impersonate with access to one’s devices and credentials. Considering alternatives like national identity cards to prove data, one needs to verify original documents again and again physically, decreasing customer’s inspiration for the activity. One more issue to tackle is excessive data getting shared during authentication can serve maliciously against the users. Companies utilize this data for digital profiling of users and targeting advertisements. Hence, only data attributes of utmost necessity should get shared. We have tried to create a web app named Canfirm to demonstrate the possibilities, workflow and use of open banking APIs.
Cyberspace is becoming the dominant global arena for the exchange of goods and services. In addition, cyberspace is playing an increasing role in meeting the social needs of the modern human being. Services provided by public administrations are moving to the Internet and modern information and communication technologies. In such an environment, the need for reliable identification of an individual in cyberspace becomes increasingly demanding. E-commerce, as well as e-business in most cases implies the possession of bancing cards as an instrument of non-cash payment transactions. Therefore, a banking card is recognized as an instrument that confirms the identity of an individual within electronic interactions, and the bank can also be seen as a provider of trust services in electronic identification procedures. In a large number of electronic transactions in cyberspace, there is often no need for identity verification via credit cards, because no financial transaction. At the same time, there is a need to reliably determine the identity of an individual in cyberspace. The intensive development of the Internet, the transfer of a large number of business and social activities in cyberspace has led to the need to adapt legal solutions that regulate some activities on the Internet, or the mentioned cyberspace. Thus, a system of reliable digital authentication of transactions and recognition of an individual’s identity when appearing in cyberspace has been developed. In the Republic of Srpska, but also in Bosnia and Herzegovina, legislation has been adopted that recognizes electronic signatures, as well as trust and electronic identification services. Back in 1999, the European Union adopted a regulation for digital signatures, which was replaced by the Regulation on electronic identification and trust services for electronic transactions in the internal market number 910/14, popularly called eIDAS. eIDAS regulations legally regulate the methods of digital identification, as well as the legal validity of electronic documents and electronic business with traditional documents and business. The paper studies the levels of electronic identifications, possible solutions in legislation and practice in the Republic of Srpska and Bosnia and Herzegovina and presents examples from neighboring countries.
The COVID‐19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID‐19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re‐establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain‐based privacy‐preserving platform for COVID‐19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self‐sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID‐19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self‐sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof‐of‐concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer‐indistinguishability, and revocation.
Distributed (i.e. mobile) enrollment to services such as banking is gaining popularity. In such processes, users are often asked to provide proof of identity by taking a picture of an ID. For this to work securely, it is critical to automatically check basic document features, perform text recognition, among others. Furthermore, challenging contexts might arise, such as various backgrounds, diverse light quality, angles, perspectives, etc. In this paper we present a machine-learning based pipeline to process pictures of documents in such scenarios, that relies on various analysis modules and visual features for verification of document type and legitimacy. We evaluate our approach using identity documents from the Republic of Colombia. As a result, our machine learning background detection method achieved an accuracy of 98.4%, and our authenticity classifier an accuracy of 97.7% and an F1-score of 0.974.
Blockchain-Based E-Certificate Verification and Validation Automation Architecture to Avoid Counterfeiting of Digital Assets in Order ( Peer-Reviewed, Open Access
- A Djajadi
- K S Lestari
- L E Englista
- A Destaryana
Djajadi, A., Lestari, K. S., Englista, L. E., & Destaryana, A. (2023). Blockchain-Based E-Certificate
Verification and Validation Automation Architecture to Avoid Counterfeiting of Digital Assets in Order
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:09/September-2023 Impact Factor-7.868
www.irjmets.com
www.irjmets.com
@International Research Journal of Modernization in Engineering, Technology and Science
[137]
Towards a Blockchain based digital identity verification, record attestation, and record sharing system
- M Aydar
- S Ayvaz
Aydar, M., & Ayvaz, S. (2019). Towards a Blockchain based digital identity verification, record
attestation, and record sharing system. Preprints and early-stage research.