Papers by Arunkumar Paramasivan
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2024
In particular, such ingredients as speed and convenience have become associated with the growth o... more In particular, such ingredients as speed and convenience have become associated with the growth of fraudulent activities in the field of financial services in recent years. The risks associated with BP have been best handled by another advanced solution, known as predictive analytics, combined with artificial intelligence (AI). This paper aims to analyze the application of AI-based predictive analytics to improve fraud detection and prevention in card transactions through machine learning techniques, big data and interactive decisions. In terms of techniques, we analyze how decision trees, neural networks and Support Vector machine models work to detect fraudulent patterns. In addition, this paper reviews the deployment of these models, the assessment criteria, and the weaknesses of the proposed models used for fraud detection. With this case, we focus on assessing the effectiveness of predictive analytics in the matter of transaction safety and customer trust to provide evidence that AI may transform existing approaches to financial fraud prevention.
International Journal of Leading Research Publication, 2024
In the evolving landscape of digital finance, ensuring secure and resilient payment networks has ... more In the evolving landscape of digital finance, ensuring secure and resilient payment networks has become crucial due to the increasing sophistication of fraud tactics targeting card transactions. This article investigates the role of artificial intelligence (AI) in fortifying security measures for card payments through advanced fraud detection and prevention techniques. AI-driven solutions leverage machine learning algorithms to monitor and analyze transaction patterns in real-time, identifying anomalous behaviors and potential threats with remarkable accuracy. By processing vast volumes of data at high speed, AI enables financial institutions to detect suspicious activity promptly, reducing the response time to potential fraud attempts and minimizing financial losses. The resilience of payment systems is enhanced by the proactive nature of AI, which can adapt to evolving fraud techniques and learn from new data patterns, thus improving the system's risk management capabilities over time. Additionally, AI models can account for factors such as user behavior, transaction history, location, and device information, creating a dynamic profile for each user. This approach not only strengthens defense mechanisms but also reduces the incidence of false positives, ensuring that legitimate transactions are not hindered by security protocols. Through these advancements, AI is instrumental in fostering secure card transactions, safeguarding both consumers and financial institutions, and instilling greater trust in digital payment networks. This article provides an in-depth analysis of various AI methodologies applied in fraud detection, including supervised and unsupervised learning models, neural networks, and anomaly detection algorithms. The research highlights case studies and real-world implementations to demonstrate the effectiveness of AI in reducing fraud instances and enhancing overall security in card payment systems. By exploring the future directions of AI-driven fraud prevention, this paper underscores the critical role of artificial intelligence in building robust, resilient, and trustworthy digital financial ecosystems.
International Journal of Leading Research Publication, 2024
Retracing the use of Artificial Intelligence (AI) in the financial services industry, particularl... more Retracing the use of Artificial Intelligence (AI) in the financial services industry, particularly in the credit card service, has eased the industry through a new level of personalization. Powered by large volumes of transactional and behavioral data, AI systems help issuers better understand customers and their lifestyles. This process employs sophisticated algorithms like micro and macro analyzing, whereby the behavior of customers in the future, based on previous ones, is predicted, and systems that offer specific products and services to specific customers are recommended. It also increases customer contact, which is a valuable way of creating and providing relevant services promptly in accordance with the customer's desires. This has been facilitated by machine learning and deep learning to provide a powerful capability to enhance customer satisfaction and customer loyalty, enhancing the competitive advantage of financial institutions in the digital environment. However, such personalization is based on the application of AI to cardholders' experience, and the perks come with a number of limitations and ethical questions. Because AI systems interrogate private customer data, issues concerning data protection, permission, and appropriate use of the customer's information emerge. Issues such as instances where some demographic type is given a raw deal also come into play while dealing with a model, thus forcing issuers to consider model fairness and transparency in their systems. Also, the problem of customer trust emerges as a critical priority since customers become more informed and worried about their data. In this paper, the author focuses on the possible ways financial institutions can be used to harness AI to the advantage of the customers without violating the customers' rights to privacy and trust.
International Journal of Leading Research Publication, 2024
The rapidly evolving digital transactions, enhancing customer trust in card payments has become p... more The rapidly evolving digital transactions, enhancing customer trust in card payments has become paramount.This article explores how artificial intelligence (AI) is reshaping risk management in card payments, fostering security and customer confidence through real-time fraud detection, predictive analytics, and regulatory compliance. AI-based models harness the power of machine learning algorithms to identify suspicious patterns, mitigate risks, and dynamically adapt to emerging security threats. This proactive approach to fraud prevention not only minimizes financial losses but also strengthens customer relationships by assuring them of a secure payment experience. By analyzing extensive datasets, AI systems detect anomalies and predict potentially fraudulent transactions with high accuracy. These models are designed to improve continuously, adapting to new trends in fraud and cyber security threats. Furthermore, AI-driven compliance tools keep pace with regulatory updates, reducing the risk of penalties and ensuring adherence to industry standards. The article also highlights how AI-based risk management models can personalize user experiences by tailoring risk assessments based on individual behavioral patterns, creating a balance between security and seamless customer interactions. Ultimately, the implementation of AI in card payment systems has proven effective in reducing charge backs, lowering fraud-related costs, and enhancing operational efficiency for financial institutions. Through case studies and real-time examples, this paper demonstrates the transformative impact of AI in the card payment industry and underscores its role in fostering consumer confidence in digital payments. By adopting AI-driven risk management frameworks, organizations can provide a reliable and transparent payment experience, which is critical to strengthening long-term customer trust in digital payment ecosystems.
International Journal of Leading Research Publication, 2024
Artificial Intelligence (AI) is revolutionizing international payment systems, paving the way for... more Artificial Intelligence (AI) is revolutionizing international payment systems, paving the way for faster, more secure, and cost-effective cross-border transactions. Traditional cross-border payment systems have been plagued by delays, high transaction fees, and complex regulatory challenges, often resulting in inefficient global card services. AI technologies, such as machine learning, natural language processing, and data analytics, are now enabling seamless and optimized payment processing by automating currency conversion, detecting fraud, and optimizing payment routing in real-time. These advancements ensure a smoother, quicker experience for consumers and businesses alike, reducing operational costs and increasing security. AI's ability to process vast amounts of data allows it to identify and mitigate fraud risks, providing enhanced protection in the international payment landscape. The limitations of traditional systems, AI is not only improving the efficiency of global card services but is also making crossborder payments more accessible and transparent, ultimately benefiting consumers worldwide. The future of cross-border transactions promises a more seamless and interconnected global economy, driven by AI's transformative potential.
International Journal on Science and Technology, 2024
Artificial Intelligence (AI) combined with Digital Twin Technology (DTT) has been considered an i... more Artificial Intelligence (AI) combined with Digital Twin Technology (DTT) has been considered an innovative solution for changing the health systems approach, especially in developing patientspecific simulation for better treatment plans. The virtual models called the digital twins are copies of physical systems, where the health care providers can predict patients', outcomes based on the real time data. This paper describes how AI and DTT are complementary technologies used in the healthcare environment together and with what difficulties. In this paper, we briefly discuss the current state of research on digital twins in general and their application in the field of precision medicine, together with the corresponding methodological frameworks; we also describe the results of the case study conducted for the first time. The findings indicate enhanced precision in matters relating to patients' interactions, disease control, and treatment schedules.
International Journal on Science and Technology, 2023
Medical supply chain networks are important for providingthe proper consumer with medicine and ot... more Medical supply chain networks are important for providingthe proper consumer with medicine and other related products. But frequent problems such as inadequate distribution channels and stock management lead to the following:always consumers experiencing stock out higher costs expended on the same stock since most will go to waste due to expiry. This paper explores how Artificial Intelligence (AI) has been used in healthcare supply chains especially drug distribution and inventory management. These include areas like Artificial intelligence in demand forecasting, Artificial intelligence in predictive analytics, and Artificial intelligence in inventory tracking, among others. A literature review captures AI development in the context of healthcare logistics, while a coherent approach assesses deployable interventions. The results have highlighted direct gains of AI implementation including increased productivity, reduced cost, and quality improvement of patient service. Future direction and prospects are also considered, as well as longer limitations and concerns such as the need for intelligent, scalable and ethically sound models.
International Journal on Science and Technology, 2023
PHM, in essence, therefore, forms one of the key aspects of contemporary health care systems with... more PHM, in essence, therefore, forms one of the key aspects of contemporary health care systems with goals of enhancing the health status of entire communities. Applying AI for PHM has been identified as a new generation thatausess insight and data fordecision-makingg to solve multifaceted problems in public health. In this paper, the author is trying to understand how the use of AI technologies and methods assists in the early detection of threats to health, efficient utilization of available resources and encouragement of prevention measures. Through applying big data, analytics, artificial intelligence and robotic processing, AI automates, optimizes and transforms care delivery while involving patients as daily managers. The literature review is provided in the paper, the methodologies are described, and the outcomes of real-life AI applications in PHM are discussed. Ethical issues, data limitations, patient privacy, and potential algorithms' self-learning are also discussed, along with ways of addressing these issues. Application of AI in PHM exhibits promising features in revolutionizing global public health through the provision of recommendations that inform practice change.
International Journal on Science and Technology, 2022
The current development and advancement in health information technology has become very importan... more The current development and advancement in health information technology has become very important, particularly regarding data security and patient privacy. This paper aims to discussthe integration of AI and blockchain as two solutions to the challenges discussed above. By utilizing AI, the firm has strong features for data analysis and predictions, whereas blockchain creates trustful and secure systems for data management. This helps create a sound and people-friendly healthcare environment in which patient data are shielded from hackers and other malicious intruders. The paper briefly describes the current state of the art in these technologies, outlines how these can be deployed together, and reports on outcomes from existing applications. Any limitations of this research are also discussed, and suggestions for future studies are given.
International Journal on Science and Technology, 2022
The digital economy is constantly expanding, and in the payment system, digital tendencies are be... more The digital economy is constantly expanding, and in the payment system, digital tendencies are becoming more intricate and important in financial operations. Nevertheless, this advancement exposes organizations to various risks, such as data leakage and fraudulent activities. Applying Artificial Intelligence (AI) in combination with the tokenization methods suggested can allow for minimizing such threats. Thus, in this article, we scrutinize how the usage of AI and tokenization contributes to the proactive shield for credit card information. From the literature, we review certain trends and issues, describe methodologies employed in data security, and strike the results and future perspectives of financial systems. Hence, we present guidelines for deploying AI and tokenization in the secure payment processing environment.
Journal of Scientific and Engineering Research, 2022
Data reside in the healthcare sector and are derived from many sources, such as Electronic Health... more Data reside in the healthcare sector and are derived from many sources, such as Electronic Health Records (EHRs), wearable devices, and patient management systems. Nevertheless, handling and integrating such data streams is problematic as data is fragmented, is not standardized, and raises privacy issues. The focus of this paper is on exploring how Artificial Intelligence (AI) can bring new life into healthcare data management through data processing being automated, data quality being better than ever and enabling interoperability between healthcare systems. Next, we examine the use of AI-driven algorithms to clean, classify and apply Natural Language Processing to data workflows to streamline data flow while improving patient care. Additionally, the study explores AI interoperability frameworks that provide a safe exchange of data across systems whilst ensuring patient privacy. Machine learning and deep learning are key advancements in predictive analytics and personalized medicine. The results suggest that AI has the potential not only to enhance data management efficiencies but also to produce more accurate diagnoses, reduce operational costs, and improve data-driven decision-making in healthcare. The ethical and regulatory implications of AI in healthcare are discussed in this paper, and the paper concludes with a discussion of the need for transparent, explainable AI models that prioritize patient outcomes.
Journal of Scientific and Engineering Research, 2021
Electronic card payment has been widely accepted and deployed in the global marketplace, but it i... more Electronic card payment has been widely accepted and deployed in the global marketplace, but it is still considered the biggest vulnerability to fraud. Machine learning technologies that are a subset of artificial intelligence are quickly becoming standard tools for card security. This paper aims to analyze how real-time solutions facilitated by artificial intelligence combat fraud. We start with a description of simple methods such as the credentials super vices unsupervised learning methods, then explore applications like anomaly detection together with an assessment of their effects. Real-world findings also show how combining self-learning models reduces the false positive rate while improving the fraud detection proportion. Consequently, this paper presents an extensive literature analysis of AI's innovative contribution to card security.
Journal of Scientific and Engineering Research, 2021
The integration of Machine Learning (ML) into healthcare systems is transforming the operational ... more The integration of Machine Learning (ML) into healthcare systems is transforming the operational and clinical capabilities of hospitals, significantly impacting patient outcomes and the efficiency of hospital workflows. This article explores into the multifaceted role ML plays in analyzing large volumes of patient data, including clinical records, diagnostic imaging, and real-time monitoring data, to create predictive models that enhance healthcare delivery. By identifying patterns and correlations in patient data, ML algorithms can forecast patient needs, streamline resource allocation, and enable proactive responses to potential complications, ultimately facilitating timely interventions. Moreover, ML-driven decision support systems enhance diagnostic accuracy and enable personalized treatment plans tailored to each patient's unique medical profile, fostering more effective care pathways. The impact of ML extends beyond individual patient care to system-wide operational improvements. Predictive analytics assist in optimizing hospital workflows by minimizing bottlenecks, improving bed management, and ensuring the efficient utilization of medical staff and resources. The reduction in administrative overhead and operational delays contributes to a more responsive healthcare environment that prioritizes patient-centric care while maintaining high standards of efficiency and quality. Additionally, this paper explores case studies demonstrating how ML applications in hospitals contribute to accelerated diagnoses, better resource distribution, and overall improvements in hospital throughput. These advancements position ML as a cornerstone of a data-driven, intelligent healthcare system capable of adapting to the dynamic needs of patient care. Ultimately, smart health systems leveraging ML technology are creating a framework for a more proactive, precise, and sustainable approach to modern healthcare, fostering improved patient satisfaction and clinical outcomes.
Journal of Scientific and Engineering Research, 2021
The integration of artificial intelligence (AI) in personalized medicine is revolutionizing the f... more The integration of artificial intelligence (AI) in personalized medicine is revolutionizing the fields of drug discovery and patient care. By leveraging AI's data processing capabilities, researchers can now analyze vast datasets to identify promising drug compounds, predict patient responses, and streamline clinical trials, making drug development more efficient and precise. This approach not only accelerates the drug discovery process but also enables a deeper understanding of patient-specific variables, such as genetic profiles, lifestyle factors, and comprehensive medical histories. Through these insights, AI facilitates the creation of targeted treatment plans that address individual needs, increasing the efficacy and safety of therapies. Additionally, AIdriven personalized medicine holds promise in minimizing adverse drug reactions, reducing trial and error in treatments, and optimizing healthcare resource allocation. By advancing precision in patient care and enhancing the predictability of clinical outcomes, AI is positioning itself as a catalyst for transformative changes in healthcare. The potential of AI to refine therapeutic approaches and provide personalized solutions will continue to shape a future where medical interventions are tailored uniquely to each patient, thereby improving overall health outcomes. This paper examines the current applications, benefits, and challenges of AI-driven personalized medicine and highlights its role in creating a more patient-centered healthcare paradigm.
Journal of Scientific and Engineering Research, 2020
The integration of AI-driven virtual assistants in healthcare is transforming administrative oper... more The integration of AI-driven virtual assistants in healthcare is transforming administrative operations and enhancing patient engagement, presenting a significant shift in the sector's approach to efficiency and patient-centered care. By automating routine administrative tasks, such as appointment scheduling, billing inquiries, and patient data management, AI virtual assistant free healthcare professionals from time-consuming duties, allowing them to concentrate on direct patient care. This shift not only improves operational efficiency but also minimizes the administrative burden on healthcare staff, contributing to a more streamlined workflow. Moreover, AI virtual assistants play a crucial role in fostering patient engagement, providing personalized health reminders, assisting with medical queries, and offering continual support outside clinical settings. Such intelligent systems enhance accessibility, enabling patients to access vital health information and support seamlessly, thus bridging the communication gap between patients and providers. As the healthcare industry faces increased demand for accessible, high-quality care, AI virtual assistants emerge as a vital tool, promoting an environment that prioritizes patient needs while enhancing healthcare delivery. This article explores the dual impact of AI virtual assistants on operational productivity and patient engagement, underscoring their potential to reshape healthcare systems and optimize patient outcomes.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2020
The integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in digital ... more The integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in digital health record systems is transforming healthcare by making Electronic Health Records (EHRs) more accurate, accessible, and usable. EHRs, traditionally structured around rigid data formats, often fail to capture unstructured information critical to patient care, such as physician notes, patient histories, and complex medical research. Through AI and NLP, healthcare systems can now process and interpret these unstructured data sources, turning them into actionable insights. This article explores how these advancements streamline healthcare workflows, alleviate administrative burdens, and enable healthcare providers to make data-driven decisions with greater precision and speed. AIdriven predictive analytics and NLP algorithms facilitate the extraction and organization of vital patient information, fostering improved patient care by anticipating risks and optimizing treatment strategies. By ensuring that healthcare providers have real-time access to comprehensive patient data, AI and NLP empower a more patient-centered approach to care that emphasizes personalization and responsiveness to individual patient needs. As a result, healthcare professionals are better equipped to make informed decisions, enhancing outcomes and reducing errors. This article underscores the significant role of AI and NLP in reshaping digital health records to support a future where healthcare delivery is not only more efficient but also more aligned with the nuanced needs of each patient.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2020
Artificial intelligence (AI) is transforming healthcare management by enabling predictive modelli... more Artificial intelligence (AI) is transforming healthcare management by enabling predictive modelling that leverages vast datasets for proactive and informed decision-making. This article explores the role of AI-driven predictive analytics in enhancing healthcare outcomes, operational efficiency, and patient care personalization. By analyzing comprehensive data sources, from electronic health records to real-time patient monitoring, AI models can accurately forecast health trends, identify individuals at risk of disease, and recommend optimized treatment plans. The ability to anticipate health events enables healthcare providers to shift from reactive to proactive care, which helps to reduce hospital admissions, manage chronic conditions more effectively, and improve overall population health. Furthermore, predictive modelling assists healthcare systems in anticipating resource demands, thereby streamlining allocation and reducing operational costs. The study underscores the value of AI in managing healthcare resources efficiently, especially under constraints, and provides insights into how predictive modelling supports policy-making, reduces clinical workload, and enhances decisionmaking capabilities. The article presents case studies illustrating the real-world impact of predictive AI applications across various healthcare sectors. Ethical considerations surrounding data privacy and model transparency are also addressed to ensure AI-driven solutions uphold patient trust and regulatory standards.AI continues to evolve, it will unlock new potentials for data-driven healthcare strategies, ultimately paving the way for a more adaptive and resilient healthcare infrastructure capable of delivering high-quality, cost-effective, and personalized care.
INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 2020
This Article examines the transformative impact of artificial intelligence (AI) on credit risk as... more This Article examines the transformative impact of artificial intelligence (AI) on credit risk assessment within card transaction analytics. By harnessing AI's advanced capabilities in data analysis and pattern recognition, financial institutions can now conduct more nuanced and accurate evaluations of an individual's creditworthiness, identify potential defaults, and detect early signs of financial distress in real time. Traditional credit scoring models, previously limited by static data and periodic assessments, are significantly enhanced by AI's ability to analyze vast transactional datasets dynamically. This approach facilitates more precise and personalized credit offers, improves decisionmaking processes, and mitigates risk, ultimately enabling more inclusive credit opportunities for a diverse consumer base. Through AI-powered analytics, lenders can foster responsible credit practices that support financial stability and expand access to credit.
Cognitive AI systems are transforming the landscape of financial transactions by enhancing accura... more Cognitive AI systems are transforming the landscape of financial transactions by enhancing accuracy, efficiency, and compliance. This article delves into the applications and advantages of cognitive AI in financial services, focusing on its impact on operational processes and decision-making. Through machine learning, natural language processing, and advanced data analytics, cognitive AI enables faster and more reliable insights, automates routine tasks, and identifies anomalies that could signal fraud. These systems play a crucial role in improving compliance with regulatory requirements, assisting financial institutions in adhering to evolving standards and minimizing legal risks. However, the adoption of cognitive AI is not without challenges. Issues such as data privacy, transparency in AI decision-making, and the potential for algorithmic bias raise ethical and operational concerns. Additionally, the complex nature of AI requires robust governance frameworks to ensure accountable, fair, and secure use in financial contexts. This paper also discusses potential frameworks and best practices to mitigate these risks, underscoring the importance of transparency and interpretability in AI-driven financial solutions. Through an in-depth analysis of cognitive AI's role in financial transactions, this study aims to provide insights into the technologies' transformative potential and the considerations necessary for responsible adoption in the industry.
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Papers by Arunkumar Paramasivan