Papers by Jai Kiran Reddy Burugulla
cuestionesdefisioterapia, 2025
This study presents a carefully conducted quantitative analysis to demonstrate how generative AI ... more This study presents a carefully conducted quantitative analysis to demonstrate how generative AI and big data analytics can significantly enhance risk assessment of credit and charge card usage, a key challenge for the banking sector. The underlying data analytics methods can be applied to multiple related data mining challenges, including high-performing fraud detection and pattern analyses to support strategic operational management decision-making through a deep understanding of consumer behavior. The research complements these data and machine learning studies by developing the use of generative AI to model hidden attributes, including consumer spending behavior. In this new paper, we use big data analytics to assess consumer behavior and understand how spending patterns can be used to predict over-limit card usage fraudulently made by active card users to ensure intense model experimentation leading to increased fraud detection success. The recently built algorithms can further be exploited to digitally recreate the corresponding spending behavior and offer a novel solution to credit and charge card over-limit fraud detection based on information revealed in the form of generated data and hidden driver variable output rather than model parameters.The methodological approach adopted for this study, the results presented and analyzed, and the established conclusions wherecredit card over-limit is detected with a high level of precision and recall during the legacy, validation, and prediction phases appear to be both novel and theoretically sound. As a result, one could argue that our published document makes a significant scientific contributionand should have a wide audience. The paper's content should be of interest to both academics and early career researchers who work in data mining and big data, including those working in the specific subjects of credit and charge cards; commercial experts will benefit from the economic understanding of this best practice. The development of artificial intelligence and machine learning, often complemented by predictive analytics, represents some of the most resourceful data mining and big data technologies. Today's advanced infrastructure has allowed experts to utilize substantial datasets to establish solutionsthat respond to challenges found in many domains of interest, such as credit and charge card consumer protection and banking, fraud detection, management, and strategic asset policy-making.
Kurdish Studies, 2022
The advent of cloud computing has brought a significant change in traditional business banking se... more The advent of cloud computing has brought a significant change in traditional business banking services. This essay intends to explore how the cloud revolutionizes banking operations while enriching the customer delight. Smart banks are already lengthening their business service capacity to include the richness of 24/7 online banking services for their commercial account customers. It creates a business service model being steadily spread out towards digital resonance. Transference and history of business banking services in a different dimension for qualitative inquiry have been revealed to be speeding up by the introduction to an integral part of the cloud computing paradigm. Business clients need business services in such a way as not being easily comparable to individual retail banking customers. Better visibility and control over the cash flow are needed, setting up different authorization tiers based on roles on the business side, expedient payroll capabilities lighting quickly while travelling, place approving, and the like. It was the early 2000s when large money center banks began to gratify these high demand business requirements such as a compressive check manipulation solution. Such an exclusive facility service always required a long chain of face-to-face engagement between the banker and the business client and was accessible exclusively for those business clients domiciled in a location that only a large money center bank might afford. All other business clients, mostly the Small and Medium Businesses were soonly to have been gratifying the way they had been.
Nanotechnology Perceptions, 2023
Over the recent year's various models of forecasting methods have been developed which are based ... more Over the recent year's various models of forecasting methods have been developed which are based on artificial intelligence technologies that have shown to be more effective than the traditional ones, especially in the industries where many structured and unstructured types of data feed are exchanged. Key trends of what has been done in the field and how the existing travel demand forecast models are working exactly have been outlined. In addition, three use case examples are presented which indicate the concrete applicability of these new technologies and how they could be utilized. Furthermore, the biggest potential benefits for the broader society, the travel industry, and the individual citizen are pointed out. Throughout the recent years, there has been an arising interest in artificial intelligence technologies which can be seen in several sectors and industries. In the travel & tourism sector forecast models for booking data streams of a model representing booking request responses and thus anticipatory predictions have become of importance. Meanwhile, stagnancy occurs in the field with no advancements since then. The rich set of research that was carried out within the scope of travel demand forecasting, going back about a decade, therefore led to a reconsideration of the scope of the research question. At that time, findings of the conducted research were published and the remaining aspects moved to the background. The interest has now returned to this topic. Given the recent advancements in publishing related to neural networks and other approaches to machine learning, the proper technology might also be advanced enough in order to drive insights or concrete recommendations. Hence, following research will map the key trends in this field and provide an overview on how the existing models are working and how they might be used in an exemplary manner in the future.
MSW Management Journal, 2024
In the era of 5G and the Internet of Everything (IoE), the roles of artificial intelligence (AI),... more In the era of 5G and the Internet of Everything (IoE), the roles of artificial intelligence (AI), cloud computing, and big data technologies in the realm of digital finance and security will become ever more crucial as the number of online payments steadily increases. The transformation of AI, cloud computing, and big data technologies decrees the paradigm of integration, a technological push towards building a robust framework for digital finance security. These technologies play a pivotal role in smart and efficient automation by enhancing decision-making and the scalability of security measures. The use of AI, cloud computing, and big data technologies is geared to serve as a surveillance and auditing mechanism for the monitoring of real-time transactions in payment systems. The blend of AI, cloud computing, and big data technologies cultivates the integration paradigm, shaping the framework of digital finance security. It is imperative for policy makers and stakeholders in the pertinent areas of software engineering to be attuned to the threats of evolving digital cyber-financial systems. This work highlights the use of AI, cloud computing, and big data technologies as a surveillance and auditing mechanism for the purpose of monitoring real-time transactions in payment systems. Finally, a discussion and future work are outlined.
Journal of Electrical Systems, 2024
In various marketing fields, rewarding customer behavior is an effective and successful practice.... more In various marketing fields, rewarding customer behavior is an effective and successful practice. Loyalty programs serve as one of the very well-known marketing tools for rewarding customers for repeat purchases or repeat patronage. Different behavioral sequences are rewarded with some type of points or benefits, and these rewards are usually redeemable after reaching some predetermined levels. Inherently, loyalty programs are designed to motivate customer transactional behavior, leading to several highly researched topics such as optimal reward and level configurations. However, consumer behavior and loyalty program characteristics have been investigated in terms of their macroscopic interactions. Hence, there will be numerous opportunities to create or enhance consumer insights and proper mechanisms generating or interacting with them better insights being at the sequence, item level, and at the consumer level. This paper attempts to analyze consumer loyalty program interactions within banking transactional datasets and develops generative artificial intelligence based methods to enhance insights or generate mechanisms. The proposed methods and generated models for enhanced insights are then evaluated within a loyalty bank setting. When designing new forms creating customer rewards and be nefit programs operating such generative AI algorithms, that choice dictates the entire personalized algorithmic framework. Eliciting customer information in the form of simple, conventional data structures may not be expressive or complex enough to fit such approache s. At the least, care should be taken so that consumer information can be molded for direct consumption by leveraging generative AI frameworks, the study and exploration of which is currently barely starting, a vast and rich field. Therefore, financial services looking to operate such customer personalization algorithms are advised to thoroughly research and consult hyper-personalization scheme experts and plan for significant investments and deployments to benefit from major AI leaps in analytics and personalization.
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Papers by Jai Kiran Reddy Burugulla