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1
Johnson, Mark, Min Jung Kang, and Tolani Lawson. "Stock Price Reaction to Data Breaches." Journal of Finance Issues 16, no. 2 (2017): 1–13. http://dx.doi.org/10.58886/jfi.v16i2.2263.
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Data Breaches occur in many forms that include bad security practices, hacking, insider attacks, stolen or lost equipment and computer or data theft. Data breaches happen to organizations of all types. In this paper, we present an analysis of the stock market’s assessment of the cost of data breaches through the examination of 467 heterogeneous data breach events that occurred at 261 publicly traded companies between year 2005 and 2014. Our event study findings indicate that publicly traded firms in the U.S. lost, on average, .37% of their equity value when a data breach occurs. Particularly, we find that breaches resulting from payment card fraud contributed more to negative announcement returns than the other breach types. Such negative announcement effects are most heavily felt when firms with card breaches are larger than the average, resulting in a 3% decline in firm equity value. Contrary to previous studies, we find that repeated breaches do not impact firm stock value differently than first-time-breaches. However, we find that there is a high correlation between firm sizeand the existence of multiple, repeat, data breaches. This implies that large firms hit by a data breach are more likely to experience subsequent breaches than small firms.
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Bush, Don. "How data breaches lead to fraud." Network Security 2016, no. 7 (2016): 11–13. http://dx.doi.org/10.1016/s1353-4858(16)30069-1.
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Baladari, Venkata. "SMART PAYMENT SECURITY: A SOFTWARE DEVELOPER'S ROLE IN PREVENTING FRAUD AND DATA BREACHES." International Journal of Core Engineering and Management 6, no. 9 (2020): 165–75. https://doi.org/10.5281/zenodo.15020546.
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Smart payment systems are increasingly vulnerable to security threats such as fraud and data breaches, necessitating effective protection measures. This study proposes a framework for software developers to boost transaction security via encryption, multi-factor authentication (MFA), secure API practices, and fraud prevention methods. The system also ensures adherence to requirements such as PCI DSS, GDPR, and PSD2, incorporating secure coding practices, DevSecOps, and real-time monitoring to mitigate potential risks. Through real-world examples and applied solutions, the research assesses the security efficiency of payment systems and recommends potential improvements, including blockchain technology, quantum-proof encryption methods, and anti-fraud measures. This framework allows developers and payment providers to construct secure, expandable, and dependable financial platforms.
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Sanskrati Agarwal and Usha J. "Detection of fraud card and data breaches in credit card transactions." International Journal of Science and Research Archive 9, no. 2 (2023): 576–82. http://dx.doi.org/10.30574/ijsra.2023.9.2.0603.
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Data breaches and credit card fraud are now among the biggest problems affecting financial organizations and customers globally. The purpose of this study is to develop an effective fraud detection system that can detect fraudulent credit card transactions and prevent data breaches. The strategy proposed in this paper makes use of machine learning techniques like decision trees and logistic regression, to analyze large datasets of credit card transactions and identify suspicious patterns. The proposed system also includes a real-time monitoring mechanism that alerts the relevant authorities in case of any suspicious activity. The results of the experiments show that the suggested system achieves great accuracy and efficiency in detecting fraudulent transactions and data breaches. It can provide a powerful tool for financial institutions to prevent financial losses and maintain their customers' trust.
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Chaturvedi, Mudit, Shilpa Sharma, and Gulrej Ahmed. "Optimized early financial fraud detection and prevention method to enhance the security." Journal of Information and Optimization Sciences 46, no. 2 (2025): 359–69. https://doi.org/10.47974/jios-1920.
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Financial fraud poses a major threat to banking and financial organizations, leading to financial losses and erosion of public trust. This paper proposes an early financial fraud detection and prevention method to secure financial data in transit, at rest, and in process to mitigate risks of frauds such as unauthorized transactions, identity thefts, data breaches, and cyberattacks. The method involves assessing the current cybersecurity posture through surveys, defining optimal security controls baseline per regulatory guidelines, performing gap analysis to identify vulnerabilities, and suggesting remedial measures. Effective implementation of such a system is envisaged to significantly reduce incidents of financial frauds and resulting revenue losses, enhancing cyber resilience of financial institutions.
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Eze, Charles Uzodinma, Emmanuel Chukwuma Ebe, Ifeoma Mary Okwo, et al. "Effect of the Capability Component of Fraud Theory on Fraud Risk Management in Nigerian Banks." International Journal of Financial Research 13, no. 1 (2022): 90. http://dx.doi.org/10.5430/ijfr.v13n1p90.
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The incidence of bank fraud is a fundamental problem with diverse consequences to banks and their stakeholders. Therefore, this study examined the effect of the capability component of fraud theory on fraud risk management in Nigerian banks. The specific objectives of the study are to: examine the effect of malicious insider abuses on fraud risk management efficiency of Nigerian banking sector; evaluate the effect of internal control bypasses on fraud risk management efficiency of Nigerian banking sector; investigate the effect of information security breaches on fraud risk management efficiency of Nigerian banking sector, and ascertain the effect of fraud risk governance on fraud risk management efficiency of Nigerian banking sector. The study adopted ex-post factoresearch design. Secondary data were gathered from the quarterly report on fraud and forgeries of the Financial Institutions Training Centre (FITC) from the first quarter of 2011 to the second quarter of 2020 given a total of thirty-eight (38) observations. The dependent variable of the study was fraud risk management efficiency (FRMη) while the independent variables were malicious insider abuses (MIA), Internal Control Bypasses (ICB), Information Security Breaches (ISB), and fraud risk governance (FRG). Four hypotheses were formulated and tested using robust linear regression analysis. The study employed Stata 14.2 and SPSS 22 in data analyses. We also conducted Skewness/Kurtosis and Shapiro-Francia W’ normality tests, Variance Inflation Factor (VIF) of multicollinearity, Breusch-Pagan/Cook Weisberg test of heteroskedasticity, and Durbin-Watson test for autocorrelation. The results revealed statistically significant negative effects of internal control bypasses and information security breaches on fraud risk management efficiency. The study also found an insignificant positive effect of malicious insider threats and fraud risk governance on fraud risk management efficiency. The implication of these findings is that the Nigerian banking sector is confronted with both internal and external fraud capability challenges which require management attention and stakeholders’ education and awareness. Based on these findings, the study offers comprehensive fraud vulnerability suggestions integrating all banking stakeholders (internal and external) to improve fraud risk management efficiency in Nigerian banking sector.
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Chaudhary, Newal. "Enacting Data Protection Law in Nepal." Prashasan: The Nepalese Journal of Public Administration 57, no. 1 (2025): 206–16. https://doi.org/10.3126/prashasan.v57i1.80668.
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In today's interconnected world, digital transactions and online activities have become indispensable facets of daily life, revolutionizing the way we communicate, conduct business, and access information. However, this rapid digitalization has also raised a critical concern, the protection of personal and sensitive data. As individuals and organizations increasingly rely on digital platforms and services, the risk of unauthorized access, disclosure, or theft of confidential information has escalated significantly. Data breaches, which involve the unauthorized acquisition of sensitive data, pose grave risks to both individuals and organizations. For individuals, a data breach can lead to identity theft, financial fraud, and misuse of personal information, potentially causing long-lasting harm and financial losses. Organizations, on the other hand, may face severe reputational damage, loss of customer trust, legal liabilities, and substantial financial consequences resulting from data breaches. Nepal, like many other nations, has witnessed a surge in data breach incidents in recent years, exposing vulnerabilities in its digital landscape and highlighting the pressing need for a comprehensive legal framework to address this critical issue. High-profile cases, such as the breach of the Ramailo app database in 2023 and the Vianet data breach in 2020, have underscored the urgency of implementing robust measures to safeguard the privacy and security of individuals' personal information. This article explores the current legislative landscape in Nepal by examining existing laws and policies related to cybersecurity and data protection. It critically evaluates the gaps and shortcomings in the current legal framework, highlighting the lack of specific provisions and enforcement mechanisms needed to effectively address the complexities of data breaches. Through comprehensive analysis, the article advocates for the enactment of a dedicated data protection law in Nepal. Such a law would encompass key aspects like mandatory breach notification requirements, stringent data protection standards, and effective enforcement mechanisms. By addressing these crucial elements, a robust data protection law can safeguard the rights and interests of Nepali citizens, fostering a secure and trusted digital environment that promotes economic growth, innovation, and public confidence in the digital ecosystem. This article emphasizes the importance of a comprehensive legal framework that aligns with international best practices and facilitates cross-border cooperation in combating the global threat of data breaches. By establishing clear guidelines, accountability measures, and consumer protections, a dedicated data protection law can empower individuals, organizations, and regulatory bodies to proactively address data breaches, mitigate potential risks, and uphold the principles of privacy and data security in the digital age.
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Hasan, Kumrul, Md Nazmul Hosen, and Kinjol Saha. "Federated Learning for Telecom Fraud Detection: A Privacy-Preserving Approach to Overcoming Data Fragmentation and Enhancing Security." European Journal of Theoretical and Applied Sciences 2, no. 6 (2024): 99–109. http://dx.doi.org/10.59324/ejtas.2024.2(6).08.
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Over the last couple of years, the world has seen numerous frauds about telecom: fraud calls, phishing, and misutilization of personal numbers, among others. However, traditional methods of fraud detection cannot fit the shifting intricacy of these fraud schemes since they depend on data collection in a centralized way. While the arrival of deep learning improves the detection capability, it engenders considerable privacy risks and issues of data fragmentation. As one might guess, this work investigates federated learning as a decentralized solution to these limitations. The FL allows various organizations to train fraud detection models collaboratively while preserving data privacy via sharing only model updates, not raw data. This paper proposes a federated learning-based system for phone number fraud detection and defends personal data against various industries. We will discuss in more detail the advantages of federated learning in solving the "data island" problem and reducing the risk of privacy breaches in a distributed environment. The paper also looks at the use of horizontal and vertical federated learning in co-governance both within and across industries. Finally, we discuss the limitation of FL from a practical perspective by including problems arising from non-IID data and heterogeneity in systems and scalability. We also identify further works to be pursued in optimizing the performance of FL fraud detection with privacy preservation.
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Kumrul, Hasan, Nazmul Hosen Md, and Saha Kinjol. "Federated Learning for Telecom Fraud Detection: A Privacy-Preserving Approach to Overcoming Data Fragmentation and Enhancing Security." European Journal of Theoretical and Applied Sciences 2, no. 6 (2024): 99–109. https://doi.org/10.59324/ejtas.2024.2(6).08.
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Over the last couple of years, the world has seen numerous frauds about telecom: fraud calls, phishing, and misutilization of personal numbers, among others. However, traditional methods of fraud detection cannot fit the shifting intricacy of these fraud schemes since they depend on data collection in a centralized way. While the arrival of deep learning improves the detection capability, it engenders considerable privacy risks and issues of data fragmentation. As one might guess, this work investigates federated learning as a decentralized solution to these limitations. The FL allows various organizations to train fraud detection models collaboratively while preserving data privacy via sharing only model updates, not raw data. This paper proposes a federated learning-based system for phone number fraud detection and defends personal data against various industries. We will discuss in more detail the advantages of federated learning in solving the "data island" problem and reducing the risk of privacy breaches in a distributed environment. The paper also looks at the use of horizontal and vertical federated learning in co-governance both within and across industries. Finally, we discuss the limitation of FL from a practical perspective by including problems arising from non-IID data and heterogeneity in systems and scalability. We also identify further works to be pursued in optimizing the performance of FL fraud detection with privacy preservation.
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SWAMY, Mr K. K., B. MEGHANA REDDY, K. SAI CHAITANYA, M. HARINI, and T. MEHER PRANEETH. "DETECTING UNAUTHORIZED OR FRAUD PROFILES USING ARTIFICIAL NEURAL NETWORKS." YMER Digital 21, no. 05 (2022): 404–11. http://dx.doi.org/10.37896/ymer21.05/43.
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In moment's digital age, the ever- adding reliance on computer technology has left the average citizen vulnerable to crimes similar as data breaches and possible identity theft. These attacks can do without notice and frequently without announcement to the victims of a data breach. At this time, there's little provocation for social networks to ameliorate their data security. These breaches frequently target social media networks similar as Face book, Twitter, Instagram and many other platforms. They can also target banks and other fiscal institutions. Vicious users’ produce fake accounts to phish login information from unknowing users. A fake profile will shoot friend requests to numerous users’ with public profiles. These fraud account users bait unknowing genuine users with film land of people that are considered seductive. Once the person accepts the request, the fake user of the phony profile will spam friend requests to anyone this person is a friend. Then, by using Artificial Neural Networks (ANN) we're relating whether given account details are from genuine or fake users’. ANN algorithm will be trained with all former users’ fake and genuine account data and also whenever we gave a new test data then that ANN train model will be applied on new test data to identify whether given new account details are from genuine or fake accounts. Online social networks similar as Face book or Twitter contain users’ details and some vicious users’ will hack social network database to steal or transgress genuine user’s information, to cover users’ data we're using ANN Algorithm. To train ANN algorithm we're using below details from social networks. Age of Account, User gender, Age of user, Profile link description, Usage count, Friends or followers count, Position, Location IP address, Status.
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Wanasiri, Yunita. "Analysis of NIK Data Breaches and Their Implications for Achieving the SDGs in Indonesia: A Case Study and Policy Recommendations." Records Management System Journal 3, no. 02 (2025): 11–19. https://doi.org/10.62201/rmsj.v3i02.204.
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The case of NIK (Nomor Induk Kependudukan or Indonesian Identification Number) data breaches in Indonesia highlights the urgent need for robust data protection. These breaches pose a high risk of identity theft, financial fraud, and other cybercrimes, having widespread impacts on society and economic stability. This study compares Indonesia's data protection framework with that of developed countries such as Singapore and South Korea, identifying significant shortcomings in data governance. Through a systematic literature review, this research examines the factors causing NIK data breaches, their social impacts, and the necessary policy recommendations. Key findings include weaknesses in cybersecurity infrastructure, human error, and minimal regulatory oversight, which exacerbate the risk of breaches. The consequences include a decline in public trust, an increase in cybercrime, and economic losses. Proposed policy recommendations include the establishment of an independent data protection authority, strengthening cybersecurity infrastructure, and enhancing education and awareness about data privacy. The NIK data breaches underscore the urgency for Indonesia to strengthen data protection regulations to create a safer and more trustworthy digital environment.
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Abhishek Jain. "AI-Driven Models for Financial Fraud Mitigation: A Data-Centric Approach to Detecting and Preventing Fraudulent Transactions." Darpan International Research Analysis 13, no. 2 (2025): 1–14. https://doi.org/10.36676/dira.v13.i2.164.
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The operation of new digital financial systems is greatly simplified by the incorporation of AI and blockchain networks. However, the techniques criminals employ have evolved, creating distinct challenges for conventional fraud detection systems. The contribution of this research is a framework for financial ecosystems that incorporates edge AI technology with blockchain for heightened security, alongside Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for decentralised fraud detection and response. The model is trained on heterogeneous financial datasets through GNNs with a multi-dimensional performance index assessment which showed exemplary gains in accuracy of detection, latency, and adaptability to changing fraud countermeasures. Moreover, credibility blockchains enhance system integrity by fortifying security measures against data breaches, while Explanatory Artificial Intelligence fulfils the regulatory necessity. The model’s design provides flexibility and adaptability to increasingly advanced requirements, reinforcing resilience against modern threats to financial infrastructures.
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Oluwabiyi Oluwawapelumi Ajakaye, Ayobami Gabriel Olanrewaju, David Fawehinmi, Rasheed Afolabi, and Gold Mebari Pius-Kiate. "Integrating Artificial Intelligence in organizational cybersecurity: Enhancing consumer data protection in the U.S. Fintech Sector." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 2802–21. https://doi.org/10.30574/wjarr.2025.26.1.1421.
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Financial technology (fintech) companies face escalating cyber threats that jeopardize consumer data. This research investigates how integrating artificial intelligence (AI) into organizational cybersecurity can enhance consumer data protection in the U.S. fintech industry. We pose key questions on AI’s role in threat detection, its current use cases and challenges in fintech cybersecurity, and the effectiveness of deep learning models in preventing data breaches. A comprehensive literature review reveals that AI techniques – particularly deep learning models like Long Short-Term Memory (LSTM) networks and Transformers – are increasingly applied for intrusion detection, fraud mitigation, and threat intelligence in fintech cybersecurity. However, challenges such as adversarial attacks, data bias, regulatory constraints, and implementation costs persist. To address our research questions, we develop an AI-driven cybersecurity methodology applying LSTM and Transformer models to recent U.S. fintech breach datasets and a benchmark intrusion dataset. Real-world breach data from 2018–2023 (e.g., the Verizon VERIS breach database and public disclosures) and a modern intrusion detection dataset are used to train and evaluate the models. The LSTM-based model and Transformer-based model are assessed on their accuracy, detection speed, and impact on breach prevention. Results show that both models achieve high detection rates (over 98–99% accuracy) in identifying malicious activities, with the Transformer slightly outperforming the LSTM in precision and recall. These AI models dramatically reduce incident response times and flag threats that may otherwise go undetected, aligning with industry reports that organizations using security AI contain breaches significantly faster. Discussion of the findings connects these performance gains to improved consumer data protection: earlier and more accurate detection of intrusions allows fintech firms to prevent or mitigate data breaches before sensitive customer information is compromised. We also explore how AI integration must be paired with governance, risk, and compliance (GRC) frameworks to address ethical and regulatory considerations. Conclusion: The study concludes that AI-driven cybersecurity holds great promise for strengthening data protection in fintech by augmenting threat detection capabilities and reducing breach impacts. We provide actionable insights for fintech organizations and researchers, highlighting that while AI can substantially enhance cybersecurity resilience and consumer data safety, a socio-technical approach addressing challenges of trust, transparency, and compliance is essential for successful implementation
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Ganji, Shubham. "Mobile Forensics in Financial Fraud Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30381.
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Mobile forensics, a critical component of digital forensics, is essential for investigating and analyzing digital evidence pertinent to financial fraud security. This guide delineates key steps and methodologies, with a focus on device identification, secure data extraction, password decryption, cloud and deleted data analysis, timeline reconstruction, network and geolocation analysis, app and social media investigations, and comprehensive reporting. The operational process involves acquiring mobile devices, analyzing file systems for financial data, extracting and decoding relevant information, verifying findings, collaborating with network forensics for broader insights, and presenting conclusive results. Mobile forensics plays a pivotal role in uncovering evidence for financial crimes, ranging from fraudulent transactions to data breaches, where mobile devices serve as critical tools. Specialized techniques in mobile forensics contribute significantly to meticulous and successful investigations aimed at mitigating financial fraud risks. Keywords- Mobile Forensics, Data, Analysis, Extraction, Evidence, Financial fraud
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Md, Abdul Quadir, Dibyanshu Jaiswal, Jay Daftari, Sabireen Haneef, Celestine Iwendi, and Sanjiv Kumar Jain. "Efficient Dynamic Phishing Safeguard System Using Neural Boost Phishing Protection." Electronics 11, no. 19 (2022): 3133. http://dx.doi.org/10.3390/electronics11193133.
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The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.
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Bacellar, Leandro Wünsche, and Elizeu Luiz Toporoski. "Clonagem de cartão de crédito sob a perspectiva da Lei Geral de Proteção de Dados." Academia de Direito 6 (December 18, 2024): 4374–88. https://doi.org/10.24302/acaddir.v6.5673.
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Technological advancement has transformed payment methods, replacing cash with credit cards and facilitating online transactions. However, this evolution has also brought challenges, such as vulnerability to fraud, hacking, and cloning, raising concerns about the security of personal and banking data. In this scenario, financial institutions have become targets of lawsuits from consumers harmed by cybercrimes. The implementation of the General Data Protection Law (GDPL) is essential to ensure the privacy of data subjects, requiring operators to adopt stringent security measures. This work aims to demonstrate that the application of the GDPL can prevent data breaches and fraud, promoting a safer digital environment for consumers.
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Jimmy, Fnu. "Enhancing Data Security in Financial Institutions with Blockchain Technology." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (2024): 424–37. http://dx.doi.org/10.60087/jaigs.v5i1.217.
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The rapid digitization of financial institutions has significantly increased the need for robust data security measures. Blockchain technology, with its decentralized and immutable nature, offers a promising solution to enhance data security in this sector. This paper explores how blockchain can address key security challenges faced by financial institutions, such as data breaches, fraud, and unauthorized access. By leveraging cryptographic principles and a distributed ledger system, blockchain ensures data integrity, transparency, and secure authentication. The potential benefits, limitations, and real-world applications of blockchain in financial security are also discussed to provide a comprehensive understanding of its impact on safeguarding sensitive financial data.
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S Prabhakar, I Nalinaksha, and V Anjaneyulu. "Role of AI in enhancing cybersecurity measures to protect sensitive financial data." International Journal of Science and Research Archive 10, no. 1 (2023): 1091–97. http://dx.doi.org/10.30574/ijsra.2023.10.1.0700.
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In the digital age, the protection of sensitive financial data has become paramount due to the increasing sophistication of cyber threats. Artificial Intelligence (AI) has emerged as a critical tool in enhancing cybersecurity measures within the financial sector. This paper explores the multifaceted role of AI in safeguarding financial information from breaches and unauthorized access. AI-driven systems offer real-time fraud detection, advanced encryption techniques, and proactive threat identification, significantly reducing the risk of data compromise. By leveraging machine learning algorithms, AI can identify anomalous patterns and predict potential security breaches, enabling financial institutions to respond swiftly and effectively. Furthermore, AI enhances regulatory compliance by automating audits and ensuring adherence to security protocols. The integration of AI in cybersecurity not only fortifies the defense mechanisms of financial institutions but also fosters trust and reliability in digital financial transactions. This study underscores the transformative impact of AI on financial data security and highlights the ongoing advancements in AI technologies that continue to shape the future of cybersecurity.
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Sunil Kumar. "Blockchain and Machine Learning: A Multidisciplinary Synergistic Approach for Fraud Detection in Finance, Healthcare, and Cybersecurity." Communications on Applied Nonlinear Analysis 32, no. 4s (2024): 218–36. https://doi.org/10.52783/cana.v32.2768.
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The integration of Blockchain and Machine Learning (ML) technologies offers a transformative approach to combating fraud across various sectors, including finance, healthcare, and cybersecurity. Blockchain's decentralized and immutable nature ensures data integrity and transparency, while Machine Learning algorithms enable the detection of intricate fraud patterns through predictive analytics and anomaly detection. This synergistic combination provides a robust mechanism for identifying fraudulent activities in real time, minimizing human error, and optimizing decision-making processes. In the financial sector, Blockchain enhances the security and transparency of transactions, while ML models analyze transaction data to identify unusual patterns that may indicate fraud. In healthcare, Blockchain ensures the secure sharing of medical records, and ML assists in detecting fraudulent claims and potential identity theft. Cybersecurity applications leverage Blockchain for secure communication and data storage, with ML identifying potential threats or vulnerabilities. By combining these two cutting-edge technologies, organizations can strengthen their fraud detection systems, improve trust, and mitigate the risks associated with financial losses, data breaches, and privacy violations. This paper explores the multidisciplinary synergy of Blockchain and ML, illustrating their potential to revolutionize fraud detection mechanisms across multiple domains, providing a comprehensive overview of current advancements, challenges, and future directions for their integration in the fight against fraud.
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Kalyanasundharam, Ramachandran. "Shielding the Digital Vault: Harnessing Tokenization to Safeguard Financial Transactions." Journal of Scientific and Engineering Research 6, no. 8 (2019): 338–45. https://doi.org/10.5281/zenodo.11234558.
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This white paper explores tokenization technology in the digital payment ecosystem, focusing on its role in enhancing data security by replacing sensitive payment details with non-sensitive tokens. These tokens help secure transactions and reduce the risk of data breaches and fraud by making the data less attractive to cyberthreats. This Whitepaper helps the stakeholder delve deep into the architecture and need of tokenization systems and exploring the traditional methods used in digital payment ecosystem and how these vulnerabilities are overcome with tokenization. We examine the key challenges and benefits including system integration, maintaining data integrity, regulatory compliance and scalability to accommodate growing transaction volumes.
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Liu, Yanling, and Yun Li. "Financial Data Security Management in the Era of Big Data." Proceedings of Business and Economic Studies 8, no. 2 (2025): 37–42. https://doi.org/10.26689/pbes.v8i2.10259.
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In the era of big data, the financial industry is undergoing profound changes. By integrating multiple data sources such as transaction records, customer interactions, market trends, and regulatory requirements, big data technology has significantly improved the decision-making efficiency, customer insight, and risk management capabilities of financial institutions. The financial industry has become a pioneer in the application of big data technology, which is widely used in scenarios such as fraud detection, risk management, customer service optimization, and smart transactions. However, financial data security management also faces many challenges, including data breaches, privacy protection, compliance requirements, the complexity of emerging technologies, and the balance between data access and security. This article explores the major challenges of financial data security management, coping strategies, and the evolution of the regulatory environment, and it looks ahead to future trends, highlighting the important role of artificial intelligence and machine learning in financial data security.
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Rajkumar Sekar. "Designing secure data applications and products in the AI-driven finance sector." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 556–63. https://doi.org/10.30574/wjaets.2025.15.1.0238.
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The financial sector is experiencing a profound transformation through artificial intelligence and big data technologies, creating both opportunities and security challenges. Financial institutions now implement sophisticated AI systems for fraud detection, trading, and personalized services, necessitating robust security frameworks to protect sensitive data. These organizations face threats, including data breaches, adversarial attacks, and regulatory compliance issues, requiring multilayered protection strategies. This article explores key security challenges in AI-driven finance and presents best practices, including advanced encryption, sophisticated access control, and privacy-preserving AI techniques. It also examines future directions, such as blockchain integration for immutable audit trails and quantum-safe security measures to address emerging threats.
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JANISAR, MOHD. "Navigating the Cybersecurity Landscape: Trends, Threats, and Strategies." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28610.
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Cybersecurity is the practice of protecting computer systems, networks, and data from digital threats, such as unauthorized access, attacks, and data breaches. It encompasses various technologies, processes, and measures to ensure the confidentiality, integrity, and availability of information in the digital realm. As the digital landscape evolves, cybersecurity plays a critical role in safeguarding individuals, organizations, and nations against cyber threats. Key Words: cybersecurity, cyber threats, online fraud, Indian Cyber security measures, articles
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Doorn, Peter, Ingrid Dillo, and René Van Horik. "Lies, Damned Lies and Research Data: Can Data Sharing Prevent Data Fraud?" International Journal of Digital Curation 8, no. 1 (2013): 229–43. http://dx.doi.org/10.2218/ijdc.v8i1.256.
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After a spectacular case of data fraud in the field of social psychology surfaced in The Netherlands in September 2011, the Dutch research community was confronted with a number of questions. Is this an isolated case or is scientific fraud with data more common? Is the scientific method robust enough to uncover the results of misconduct and to withstand the breach of trust that fraud causes? How responsible and reliable are researchers when they collect, process, analyse and report on data? How can we prevent data fraud? Do we need to adapt the codes of conduct for researchers or do we need stricter rules for data management and data sharing?This paper discusses the conclusions and recommendations of two reports that were published recently in consequence of this data fraud. The reports are relevant for scientific integrity and trustworthy treatment of research data. Next, this paper reports on the outcomes of enquiries in data cultures in a number of scientific disciplines. The concluding section of this paper contains a number of examples that show that the approach towards data sharing is improving gradually. The data fraud case can be regarded as a wake-up call.
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Доценко, Тетяна, and Михайло Кузьменко. "CYBER FRAUD AS A THREAT TO THE SUSTAINABLE DEVELOPMENT OF THE HEALTH CARE SYSTEM: A SYSTEMATIC BIBLIOMETRIC ANALYSIS." Сталий розвиток економіки, no. 2(47) (December 29, 2023): 50–57. http://dx.doi.org/10.32782/2308-1988/2023-47-7.
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There is a growing wave of cyber fraud in the healthcare industry. Cases of breaches of confidentiality and security, leakage, theft and breach of medical confidential data, and fraudsters gaining access to machines and networks of medical institutions have become more frequent. The main purpose of the study is a retrospective and current systematic bibliometric analysis of scientific research in the field of cyber fraud as a threat to the sustainable development of the health care system based on Scopus, VOS Viewer, Statista. The relevance of solving this scientific problem lies in conducting not a traditional, but a comprehensive innovative systematic study of the industry, identifying priority dynamic, geographical and inter-sectoral links and directions, and problematic aspects. The research was carried out in the following logical sequence: determining the criteria for selecting publications on cyber fraud in the healthcare system; determining the dynamics of scientific articles in this area; analyzing the geographical distribution of research; studying the distribution of subject areas of the problem under study; forming and analyzing clusters of scientific articles on cyber fraud in the healthcare system by key terms; building an evolutionary and temporal map of the relationships of the studied categories with other scientific concepts in dynamics. Scopus platform, VOS Viewer software, Statista statistical database were used as analytical tools for the study. The study theoretically proves the existence of a close relationship between the health care system and cyber fraud. The results of the proposed model of a comprehensive systematic study of the healthcare industry will allow timely identification of priority areas and problematic aspects of the industry in terms of cybersecurity, will improve the protection of patients, patient data, hospital security management, strengthen the protection of medical devices, minimize the risks of cyber losses in the healthcare system, to organize sustainable development of the healthcare industry, to ensure good health of people.
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Bandari, Madhu, and P. Pavan Kumar. "Securing Iot Networks Against Fraud Using Deep Radial Basis Function Neural Networks." Journal of Neonatal Surgery 14, no. 5 (2025): 219–26. https://doi.org/10.52783/jns.v14.2926.
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The rapid proliferation of Internet of Things (IoT) devices has led to an increased risk of security frauds within IoT networks. Traditional security measures often fall short in addressing the dynamic and diverse nature of these frauds. The heterogeneity of IoT devices and their intricate communication patterns pose significant challenges in identifying potential security breaches. Conventional security approaches struggle to adapt to the evolving fraud landscape, necessitating the exploration of advanced techniques. Deep Radial Basis Function (RBF) networks offer promise in capturing the complex relationships inherent in IoT data, enabling more effective fraud detection. While existing literature has explored various machine learning approaches for IoT security, the integration of Deep RBF networks specifically in this context remains underexplored. This research aims to bridge this gap by investigating the efficacy of Deep RBF networks in identifying anomalies within IoT networks, addressing the unique challenges posed by the interconnected and diverse nature of IoT devices. The study involves the collection of a comprehensive dataset encompassing normal and anomalous IoT network activities. Feature selection focuses on key parameters such as device communication patterns, data traffic, and system behavior. Deep RBF networks are then trained on this dataset to learn and distinguish normal behavior from potential security frauds. The methodology combines the strengths of Deep Learning with the adaptability of RBF networks to capture nuanced patterns indicative of security vulnerabilities. The results demonstrate the effectiveness of Deep RBF networks in accurately detecting security frauds in IoT networks. The model exhibits a high level of sensitivity to anomalous activities, showcasing its potential as a robust tool for enhancing the security posture of IoT environments.
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Nidhi Sonkar. "An Empirical Study on the Economic Impact of Cybersecurity Breaches and Computer Fraud on SMEs." Journal of Information Systems Engineering and Management 10, no. 7s (2025): 730–35. https://doi.org/10.52783/jisem.v10i7s.986.
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An empirical study of the economic impact of cybersecurity breaches and computer fraud on Small and Medium Enterprises (SMEs) is presented in this research paper. As the need for digital infrastructure continues to rise, SMEs are ever increasingly finding themselves being hit by cyber attacks, resulting in huge financial losses and operational disruptions. Using SME data from various sectors, the study examines the extent and nature of these impacts. We examine key areas of focus associated with breaches, including direct financial costs, indirect costs (such as reputational damage) and broader impacts on business continuity. The research is done by employing quantitative analysis methods to identify trends and correlations of frequency of cyber attacks and financial resilience of SMEs. These findings highlight the need for robust cybersecurity measures as well as provide lessons for policymakers and business owners on how to reduce risks. As such, it further contributes to the increasingly voluminous SME cybersecurity literature by providing a foundation for further research and strategic policy development.
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Ajay Tanikonda. "Deep Learning for Anomaly Detection in E-commerce and Financial Transactions: Enhancing Fraud Prevention and Cybersecurity." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 70–77. https://doi.org/10.52783/jisem.v10i30s.4776.
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E-commerce and financial transaction platforms are increasingly vulnerable to cyber threats and fraudulent activities due to the rapid digitization of global markets. Anomaly detection plays a vital role in identifying unusual behavior indicative of fraud, security breaches, or financial manipulation. Traditional methods such as rule-based systems and statistical models often fall short in adapting to evolving patterns of fraud. Deep learning, with its ability to extract complex features and learn non-linear relationships from massive datasets, offers a transformative approach to anomaly detection. This paper explores the use of deep learning techniques—such as Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs)—to enhance fraud prevention and cybersecurity in the e-commerce and financial sectors. The paper highlights the comparative effectiveness of different models, challenges such as data imbalance and explainability, and future prospects for integrated intelligent systems.
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Xu, Yue. "Research on Social Network Secunity Issues and Countermeasures Based on Big Data." International Journal of Computer Science and Information Technology 4, no. 3 (2024): 373–79. https://doi.org/10.62051/ijcsit.v4n3.42.
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In the current era of rapid advances in information technology, personal secrets and data protection in the social field are facing unprecedented challenges. The phenomena of privacy breaches, online fraud, data tampering, and social automation robots on social platforms pose a serious threat to users and platforms. This article delves into the core security risks faced by social networks in the era of big data, and proposes corresponding solutions from the perspectives of technology, privacy protection, and social collaboration, such as data encryption technology, multiple authentication mechanisms, abnormal behavior monitoring, and the construction of a network security education system. Through the dual means of technology and management, social platforms are expected to significantly enhance data security protection and reduce potential security risks.
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Nero, Alex, and Gifty Sarin. "A study on analysing the role of blockchain in supply chain data security and developing a conceptual diagram." Journal of Management Research and Analysis 11, no. 4 (2025): 202–8. https://doi.org/10.18231/j.jmra.2024.035.
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This research explores the role of blockchain technologyin enhancingdata securitywithin modernsupply chains. As global supply chains become increasingly complex and vulnerable to risks such as fraud, counterfeiting, and data breaches, blockchain offers a promising solution to address these challenges. By leveraging its core features blockchain can improve transparency, trust, and traceability in supply chain operations. This study examines how these blockchain features can secure critical data elements, such as product provenance, inventory management, and financial transactions, ensuring their integrity and authenticity. The research also discusses the technical benefits of blockchain, including its ability to prevent unauthorized access, reduce fraud, and enhance collaboration among supply chain participants. Despite its advantages, the study identifies several limitations, including adoption barriers, scalability issues, and regulatory concerns. The findings suggest that while blockchain has the potential to revolutionize supply chain security, further research is needed to overcome these challenges and explore its broader applicability across various industries. Future studies should focus on improving scalability, exploring sector-specific implementations, and addressing the legal frameworks necessary for blockchain adoption.
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SONI, ADITYA. "The Impact of Artificial Intelligence on Financial Services Industry: JP Morgan." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50802.
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This industrial research report explores how Artificial Intelligence (AI) is revolutionizing the financial services sector, with a specific focus on JP Morgan Chase. AI’s integration has redefined banking functions including compliance, trading, credit analysis, fraud detection, and customer engagement. The report outlines how AI-powered systems like COiN for contract analysis and LOXM for algorithmic trading are driving operational efficiency and cost savings. AI has improved fraud detection accuracy by over 14%, reduced contract review time by 99.7%, and enabled $1.5 billion in annual savings through real- time fraud prevention. However, the growing reliance on AI also introduces new risks—data breaches, algorithmic bias, and complex regulatory landscapes. The study recommends a hybrid approach, integrating ethical AI governance, robust workforce upskilling, and strategic compliance frameworks. The research concludes that the financial institutions best prepared for the future will be those that balance innovation with responsibility, using AI to both enhance performance and earn public trust.
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N, Mrs Sridevi. "A Review on UPI Fraud Detection using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 18–22. https://doi.org/10.22214/ijraset.2024.65514.
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In today's digital era, nearly all business transactions are conducted online. While online transactions offer numerous advantages such as convenience, faster payments, and ease of access, they also come with significant risks, including fraud, phishing attacks, and data breaches. The growing volume of internet-based transactions has heightened concerns about unethical practices and threats to personal privacy.For instance, cybercriminals may gain unauthorized access to an individual's account and transfer funds illicitly. To mitigate potential financial losses, it is crucial to enhance existing machine learning methodologies. A robust, featureengineered machine learning model, leveraging algorithms like Random Forest and Gradient Boosting, can improve performance, enhance stability, and become more effective by analysing extensive datasets. Moreover, understanding the costs and risks associated with various payment systems is critical for systematically and efficiently combating fraud. We propose three models: a risk assessment model to predict and counter fraud risks, a machine learning-basedfraud detection system, and an economic optimization framework for refining machine learning outcomes. These models are validated using real-world data to ensure their reliability and applicability.
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Kato, Jimmy, Eria Othieno Pinyi, Iga Daniel Ssetimba, Harriet Norah Nakayenga, Brian Akashaba, and Evans Twineamatsiko. "Securing Taxpayer Data: Advancing Cybersecurity in Tax Accounting Practices." International Journal of Research in Interdisciplinary Studies 2, no. 7 (2024): 42–46. https://doi.org/10.5281/zenodo.12793618.
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This paper explores the integration of advanced cybersecurity protocols into tax accounting practices to combat the growing issues of tax fraud and data breaches. Integrating advanced technologies, particularly Artificial Intelligence (AI), is revolutionizing various sectors. These innovations facilitate enhanced data security and operational efficiency, aiming to secure tax information, protect taxpayers from identity theft and financial loss, and ensure compliance with stringent data protection regulations. This initiative seeks to safeguard sensitive financial data Penh, enhance trust in the tax system, and support a secure and resilient economic environment. The proposed cybersecurity measures are designed to create a robust defense against cyber threats, ensuring the integrity and confidentiality of taxpayer data. The paper outlines the architecture of the cybersecurity framework, key components, and implementation steps, demonstrating the practical application and benefits of integrating these technologies into tax accounting practices.
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Haland, Christoffer, and Anders Granmo. "Machine Learning for Anomaly Detection: Insights into Data-Driven Applications." International journal of data science and machine learning 05, no. 01 (2025): 36–41. https://doi.org/10.55640/ijdsml-05-01-07.
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Anomaly detection plays a pivotal role in data-driven machine learning applications, enabling the identification of rare or unexpected patterns that deviate from the norm. These anomalies, which can indicate critical events such as fraud, security breaches, equipment failures, or medical conditions, are invaluable in a variety of fields. This paper provides an in-depth review of anomaly analytics, focusing on the various techniques used in machine learning to detect anomalies in complex, high-dimensional data. We explore statistical methods, machine learning-based approaches, and hybrid models, analyzing their strengths and weaknesses across multiple domains including cybersecurity, finance, healthcare, and manufacturing. The paper also discusses key evaluation metrics for anomaly detection and highlights the challenges of scalability, noise handling, and model interpretability. Finally, we examine emerging trends in anomaly detection, including real-time processing and explainability, and suggest future research directions to improve the robustness and efficiency of anomaly detection systems in large-scale, dynamic environments. This work serves as a comprehensive guide for understanding the role of anomaly analytics in modern machine learning applications, offering insights into current methodologies and future advancements.
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Unuigbokhai, Nkem Belinda, Godfrey Perfectson Oise, Babalola Eyitemi Akilo, et al. "ADVANCEMENTS IN FEDERATED LEARNING FOR SECURE DATA SHARING IN FINANCIAL SERVICES." FUDMA JOURNAL OF SCIENCES 9, no. 5 (2025): 80–86. https://doi.org/10.33003/fjs-2025-0905-3207.
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This paper explores the application of Federated Learning (FL) in the financial sector, focusing on enhancing security and privacy in key areas such as fraud detection, Anti-Money Laundering (AML) compliance, and biometric authentication systems. FL enables collaborative model training across multiple financial institutions without sharing sensitive transaction data, thereby preserving privacy while improving the accuracy of fraud detection models. In AML compliance, FL facilitates the development of robust models by leveraging diverse datasets, enhancing the ability to detect suspicious activities. Moreover, FL strengthens biometric authentication systems by decentralizing model training, reducing the risks of data breaches, and ensuring compliance with privacy regulations. The paper also evaluates the performance of a loan default prediction model trained using FL, highlighting challenges with class imbalance and model bias toward the majority class. The classification report indicates high recall (98%) but also shows a potential for misclassifying non-default cases, leading to a moderate precision (81%) and an F1-score of 89%. The model's AUC of 0.69 suggests moderate discriminatory power, with room for improvement in its ability to differentiate between default and non-default cases. The model achieves an overall accuracy of 80%. Despite these challenges, it demonstrates good generalization capabilities while maintaining the privacy of client data, presenting a promising approach to secure financial transaction analysis.
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Soundararajan, Balaji. "Challenges and Checkpoints of Payment Systems Security." Journal of Software Engineering and Simulation 7, no. 9 (2021): 46–53. https://doi.org/10.35629/3795-07094653.
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The transition towards a cashless economy has amplified the importance of securing digital payment systems against evolving cyber threats. This study examines the vulnerabilities inherent in traditional and electronic payment systems, including credit cards, mobile payments, and RFID technologies, while addressing emerging risks such as data breaches, fraudulent activities, and network attacks. Through an analysis of case studies and current literature, the paper evaluates the efficacy of security measures like encryption, tokenization, multifactor authentication (MFA), and compliance frameworks. Findings reveal that while technological advancements offer convenience, they also introduce complex vulnerabilities requiring layered security strategies. The study underscores the necessity of aligning technical safeguards with international standards, fostering stakeholder collaboration, and adopting proactive fraud detection mechanisms. By highlighting successful implementations and lessons from security breaches, this work advocates for adaptive, user-centric approaches to mitigate risks and sustain trust in digital payment ecosystems.
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Motak, Maciej. "Enhance Cloud Security for Financial Data with Blockchain Integration and ECC Encryption." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 4 (2020): 67–76. https://doi.org/10.54660/.ijmrge.2020.1.4.67-76.
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Cloud computing quickly transformed financial data management into scalable and efficient solutions. Benefits are coupled with substantial security threats requiring robust protection. Innovative security architecture that integrates Blockchain technology with Elliptic Curve Cryptography to enhance financial data security in cloud platforms has been suggested. Blockchain provides data with transparency and immutability by reducing possibility of illegal interference and ECC provides strong encryption at low processing expenses. BERT and GPT are better at identifying anomalies and making predictions. AI algorithms identify fraudulent transactions through monitoring cultural patterns in transactions and behavioral irregularities. This encompasses secure cloud storage technologies employing distributed ledgers and encrypted announcement channels to prevent data breaches and illegal access. It is tested with financial datasets proving improvements in business security, encryption speed and fraud detection accuracy. Experimental results reveal 99.2 percent security strength with low latency and computational overhead suitable for secure cloud-based economic services. Smart contracts ease verification operations ensuring adherence to financial security regulations and highpoints essential role of Blockchain and ECC in enhancing cloud security and lays foundation for future research into financial cybersecurity. Emphasize importance of combining AI-driven fraud detection with top-level encryption methods to create adaptive, expandable and secure financial system.
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Kshitij Varshney. "Digital Identity Management Using Biometric Systems: BioTrace." Journal of Information Systems Engineering and Management 10, no. 51s (2025): 374–85. https://doi.org/10.52783/jisem.v10i51s.10396.
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In an increasingly digital world, establishing secure and reliable methods for verifying identity has become a critical priority across sectors such as finance, healthcare, education, and e-governance. Traditional authentication mechanisms—relying on passwords, personal identification numbers, and physical documents—are increasingly susceptible to fraud, data breaches, and user inconvenience. This paper presents a multi-modal biometric framework for digital identity management, integrating facial recognition and fingerprint verification to enhance accuracy, reduce fraud, and ensure user-centric security. The proposed system includes modules for data acquisition, preprocessing, feature extraction using Convolutional Neural Networks (CNNs) and minutiae detection, score-level fusion, and final authentication decisions. Security and privacy are ensured through AES-256 encryption, differential privacy techniques, and decentralized blockchain-based data storage. This research contributes a scalable, privacy-aware, and highly accurate digital identity model capable of addressing challenges such as interoperability, user trust, and regulatory compliance. Future enhancements include the integration of additional biometric modalities and deployment in mobile and IoT environments.
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Vaishnavi, R., and Robert M. Sandeepthi. "Cybersecurity Challenges and Regulatory Compliance in Neobanking in India." TECHNO REVIEW Journal of Technology and Management 4, no. 4 (2024): 24–33. https://doi.org/10.31305/trjtm2024.v04.n04.004.
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Neo-banking, a fully digital approach to banking, has redefined the financial landscape in India by offering seamless, user-friendly services without the need for physical branches. This transformation has enabled cost efficiency, 24/7 accessibility, and innovative financial solutions tailored to tech-savvy customers. However, the digital-first nature of neo-banking also introduces significant cybersecurity challenges, such as phishing attacks, data breaches, and identity theft, which threaten customer trust and operational stability. Neobanks must strictly adhere to RBI guidelines, the Data Protection Bill, and global compliance standards like PCI DSS and GDPR to safeguard customer data, prevent cyber crimes, and ensure secure digital transactions. Compliance with these regulations helps mitigate data breaches, fraud, and unauthorized access, reinforcing trust in digital banking services. This article explores the dual cybersecurity and regulatory compliance challenges that neobanks face in India. It highlights the importance of robust security measures, advanced authentication systems, and adherence to legal frameworks in fostering a secure and trustworthy neo-banking ecosystem. Addressing these challenges is critical for the sustainable growth and success of neobanks in India's competitive financial market.
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P., R. Gowdham Sankar, and Mahalakshmi K. "MICRO PAYMENTS IN OFF-LINE SECURE CREDITS USING RODO RESILIENT DEVICE." International Journal of Advanced Trends in Engineering and Technology 2, no. 1 (2017): 34–38. https://doi.org/10.5281/zenodo.495579.
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Credit and debit card data theft is one of the earliest forms of cybercrime. Still, it is one of the most common nowadays. Attackers often aim at stealing such customer data by targeting the Point of Sale (for short, PoS) system, i.e. the point at which a retailer first acquires customer data. Modern PoS systems are powerful computers equipped with a card reader and running specialized software. Increasingly often, user devices are leveraged as input to the PoS. In these scenarios, malware that can steal card data as soon as they are read by the device has flourished. As such, in cases where customer and vendor are persistently or intermittently disconnected from the network, no secure on-line payment is possible. This paper describes FRoDO, a secure off-line micro-payment solution that is resilient to PoS data breaches. Our solution improves over up to date approaches in terms of flexibility and security. To the best of our knowledge, FRoDO is the first solution that can provide secure fully off-line payments while being resilient to all currently known PoS breaches. In particular, we detail FRoDO architecture, components, and protocols. Further, a thorough analysis of FRoDO functional and security properties is provided, showing its effectiveness and viability.
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Adonis, Ridoh, and Bethuel Sibongiseni Ngcamu. "An empirical investigation into the information management systems at a South African financial institution." Banks and Bank Systems 11, no. 3 (2016): 58–65. http://dx.doi.org/10.21511/bbs.11(3).2016.06.
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The study has been triggered by the increase in information breaches in financial organizations worldwide. Such organizations may have policies and procedures, strategies and systems in place in order to mitigate the risk of information breaches, but data breaches are still on the rise. The objectives of this study are to explore the shortfalls of information security on a South African financial institution and further investigate whether business processes are responsive to organization’s needs. This study employed both quantitative and qualitative research methods. Questionnaires were sent to staff level employees, and semi-structured in-depth interviews were conducted with senior management at the organization. The study revealed that employees require training on information management and that there are major training deficiencies for training officers to conduct beneficial information management training at the organization. Information security program that include business risk analysis were not implemented, which results in inadequate information management planning and decisions. A standardized or uniform house rule policy was not consistently implemented across the organization, which resulted in certain areas not protecting information. The qualitative findings revealed that the external cleaning company could obtain access to customer information, if customer data are left lying around. Furthermore, there is major misalignment between policy setters and employees in this organization. The findings allow senior managers to construct projects and program with their teams to improve the state of information management in the organization which spans across the people aspect, technology systems and general information management processes. Furthermore, external companies should start signing Non-Disclosure Agreements - which is not being done currently as this opens the door for data fraud. The organization has information management and security policies in place, but the study concluded that employees do not understand these policies and should receive specialized training to ensure understanding and, ultimately, have employees following these information security policies. Keywords: data breach, information management, business processes, information legislation. JEL Classification: G2
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Ms., Manindra Singh Hanspal, and Ashutosh Acharya Mr. "Cybercrimes in the Digital Age: Typologies, Legal Challenges, And Countermeasures." Annual International Journal of Vaikunta Baliga College of Law (AIJVBCL) 2 (May 2, 2025): 201–15. https://doi.org/10.5281/zenodo.15327248.
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<em>The rapid expansion of digital technologies has ushered in a new era of connectivity, efficiency, and innovation. However, this digital transformation has also led to a parallel rise in cybercrimes, posing significant challenges to individuals, organizations, and governments. Cybercrimes encompass various illicit activities, including identity theft, financial fraud, cyberbullying, hacking, ransomware attacks, and cyberterrorism. These crimes have severe economic, social, and security implications, often transcending national borders and complicating legal enforcement. This paper critically examines the types and evolution of cybercrimes, highlighting their impact on economic stability, societal well-being, and critical infrastructure. Furthermore, it explores existing legal frameworks, including international treaties like the Budapest Convention and national cybersecurity laws, while discussing the challenges in prosecution and jurisdictional limitations. The study underscores the importance of robust cybersecurity measures, public awareness, legal reforms, and international cooperation in combating cyber threats. By fostering a comprehensive approach to cybersecurity, society can mitigate risks, enhance digital resilience, and ensure a safer online environment.</em>
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Manish keshavrao Hadap, Arvinder Kour Mehta, Gitti Narsimlu, Parag Jawarkar, Vijay kumar Kaluvala, and Ajay Kumar Chaturvedi. "An Empirical Study on the Economic Impact of Cybersecurity Breaches and Computer Fraud on SMEs." Metallurgical and Materials Engineering 31, no. 2 (2025): 93–97. https://doi.org/10.63278/1340.
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Small and Medium Enterprises (SMEs) are indispensable for economic development; however, they are highly susceptible to cyber attacks and computer fraud. This research empirically investigates the financial implications of cyber risks to SMEs in terms of monetary losses, reputational backlash, business continuity break and remediation costs. This research adopts a mixed-methods approach, with primary data collected via structured surveys and interviews with SME owners, IT managers, and cybersecurity experts, and secondary data obtained from industry reports and case studies. Regression analysis was utilized to determine the relationship between cybersecurity incidences and business performance through descriptive and inferential statistical techniques. The results show that cyber-risk incidents bring about considerable financial losses, reduced customer confidence, regulatory penalty payments (fines) that have long-term implications for SMEs. The study concludes that there is a strong need for robust cybersecurity frameworks, better awareness programs, and regulatory interventions to mitigate risks associated with the Internet of Things (IoT). These insights can help policymakers better support the small business sector to improve protections against cyberattackers, and as this research shows, small businesses can take certain actions to minimize their own economic vulnerability.
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Mithun, Kumar A., and V. Kavitha Dr. "Navigating Cyber Crime and Fraud in the Metaverse: Emerging Threats and Mitigation Techniques." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 2064–70. https://doi.org/10.5281/zenodo.14836484.
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The metaverse, an immersive digital ecosystem, is revolutionizing the way people interact, work, and socialize. However, its rapid expansion has also given rise to significant challenges in cybersecurity, particularly in the realm of cybercrime and fraud. This paper explores the emerging threats within the metaverse, such as identity theft, financial scams, data breaches, and exploitation of virtual assets. The decentralized and anonymized nature of metaverse platforms, combined with their reliance on blockchain, cryptocurrency, and smart contracts, presents unique vulnerabilities. This study highlights key threat vectors, including phishing in virtual environments, malicious virtual items, and social engineering attacks targeting user trust. It also delves into the legal and ethical complexities of jurisdiction, regulation, and enforcement in a borderless digital realm.
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Pavan, Kumar Joshi, and Kotha Rajesh. "An In-depth Knowledge on EMV Tags and their Adoption in FinTech and Traditional Banking." Journal of Scientific and Engineering Research 10, no. 9 (2023): 77–86. https://doi.org/10.5281/zenodo.14049938.
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EMV stands as the leading global standard for smart card payments. “EMV transaction specifications are based on a variety of standards, each serving a distinct purpose” [6, p. 17]. Figure 1 depicts such specifications: The EMV specification offers a versatile toolkit for payment protocols, allowing for various combinations of authorization. Its complexity and flexibility make it difficult to thoroughly analyze the security of the EMV standard. This study explored the implementation of “Europay, Mastercard, and Visa” (EMV), prompted by notable cybercrimes [1]. The aim is to shed light on the impact, solutions, and applications of EMV technology. The findings suggest that the increase in data breaches has driven this technological transition. High-profile companies like Target have experienced cyberattacks, resulting in substantial financial losses for numerous U.S. organizations due to data breaches. The technology landscape is particularly susceptible when it comes to processing financial exchanges. EMV bolsters the security level of financial transactions.
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Nagesh Mapari, Samiksha Sandip Borkar, Karan Pradip Morey, Harshwardhan Tejrao Pawar, and Om Vishwanath Vasu. "A Systematic Credit Card Analysis for Detection of Compromised Data Using Machine Learning." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1273–77. https://doi.org/10.47392/irjaem.2025.0208.
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Credit card security is paramount for banks, especially during the pre-issuance phase. This paper examines the multifaceted security measures implemented by banks to protect credit cards and cardholder data before a card is even issued. We explore the vulnerabilities inherent in the card production and personalization processes, and analyze the various countermeasures employed to mitigate these risks. These include secure printing facilities, data encryption, EMV chip technology integration, and rigorous access controls. Furthermore, we discuss the importance of robust data security protocols for safeguarding sensitive information during application processing and account setup. This paper highlights the proactive approach taken by banks to minimize the potential for fraud and data breaches in the critical pre-issuance stage, ensuring the integrity and security of the credit card ecosystem. The findings emphasize the continuous need for vigilance and innovation in security practices to stay ahead of evolving threats and maintain customer trust.
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Altynov, Artem I., and Yulia V. Turovets. "Regulatory technologies in traditional industries as an element of the digital transformation: Perspectives for development." Vestnik of Saint Petersburg University. Management 21, no. 2 (2022): 237–62. http://dx.doi.org/10.21638/11701/spbu08.2022.204.
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The article considers regulatory technologies as one of the key topics in finance digitalization due to the consistent growth of fraud, compliance rules, and procedures. It also gains attention in other traditional industries to answer similar scope of challenges. Regulatory technologies have become a common element of digitalization which allows for effective management of a growing administrative burden and reduces associated risks. At the same time, regulatory technologies adoption can be considered as an indirect indicator of the digitalization effects, including the growth of regulatory burden. This paper identifies the main objectives of regulatory technologies adoption relevant to traditional industries (telecommunication, trade, manufacturing, transport, construction, energy and utilities, healthcare, public administration). Based on the results, the study offers the prospects for regulatory technologies development in these industries according to the proposed composite index. It comprises nine indicators in four groups measuring financial losses from fraud and data leakages, administrative compliance workload, the digital maturity of sectors. The results indicate that the highest potential for regulatory technologies adoption, apart from service (finance, telecommunication, trade), is significant for manufacturing and healthcare due to a high frequency of fraud, data breaches, high compliance costs, and new regulatory requirements associated with the increasing pace of digitalization.
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R, Anuvidhya. "RANFO: An Intelligent Anomaly Detection in IoT Edge Devices." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2900–2907. http://dx.doi.org/10.22214/ijraset.2021.35622.
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As devices, applications, and communication networks become more connected and integrated, computer attacks on the Internet of Things (IoT) become more sophisticated. When attacks on IoT networks cause long-term outages, it affects the availability of critical end-user programmers, increases the number of data breaches and fraud, raises prices, and reduces revenue. In this paper we present the RANFO (IDS), prepared to protect inherently linked Iot systems. The proposed entry-level system can successfully enter real-world entrance, according to our experimental results. We'll illustrate how RANFO can identify a variety of harmful assaults, including DOS, R2L, Probe, and U2L.
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Jagetia, Anoop. "The Factors that are Influencing Mobile Banking App Adoption: Focusing on Security Perceptions, Behavioral Intentions and Technological Trust in Emerging Markets." Journal of Extension Systems 38, no. 2 (2023): 16–21. https://doi.org/10.48165/jes2022.38.2.3.
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The rapid adoption of mobile banking apps in emerging markets is driven by digital transformation and financial inclusion but hindered by security concerns, trust in technology, and behavioral factors. This study explores how security perceptions, behavioral intentions, and technological trust influence adoption. It examines user concerns about data breaches, fraud, and privacy, along with factors like ease of use and social influence. Trust in technology and financial institutions plays a crucial role in adoption decisions. Using a quantitative approach, the study provides insights for banks, fintech firms, and policymakers to enhance security frameworks and build trust, fostering financial inclusion.
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Mahaboobsubani, Shaik. "Edge Computing for Financial Data Processing." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 3, no. 3 (2017): 1–9. https://doi.org/10.5281/zenodo.14352602.
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The Edge computing is staking its claim in financial data processing by mitigating the inherent latency, bandwidth, and security issues of centralized computing systems. The article presents a comprehensive analysis of different edge computing frameworks applied in real-time analytics over financial data. This article discusses a state-of-the-art architecture for decentralizing data processing, wherein computations would be closer to sources like stock exchanges, banking systems, and payment networks. By reducing data over-distance travel, edge solutions significantly reduce latency and improve response times, hence driving operational efficiency. Benchmark tests of edge computing against traditional centralized systems have demonstrated as much as a 40% improvement in real-time transaction processing and fraud detection capabilities. Additionally, the paper talks about the scalability and fault tolerance of edge systems for energy efficiency, which would position them well for high-frequency trading, risk assessment, and personalized financial services. Attention is also given to the security discussion, where localized processing ensures less chance of data breaches and helps organizations maintain compliance with regulations. Use cases from banking, trading, and insurance illustrate how edge computing will transform financial ecosystems.