Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Recent Advancements and Challenges in FinTech(Institute of Electrical and Electronics Engineers Inc., 2023) Girish, K.K.; Bhowmik, B.The rapid advancement of technology in recent years has brought about numerous changes in various industries, and the financial sector is no exception. The rise of financial technology (FinTech) has disrupted traditional banking and financial services by offering more convenient, accessible, and personalized services to customers. Contrarily, financial services have become more efficient, cost-effective, and secure with FinTech, enabling people to manage their finances with just a few clicks, even on their smartphones. FinTech has also created new opportunities for financial inclusion, making it possible for people who were previously unbanked or underbanked to access financial services. Despite its many benefits, the rise of FinTech has also brought about several challenges. This paper gives an overview of FinTech, its progress, and its importance. Following this, significant challenges of FinTech are highlighted to ensure its long-term success and continued growth. The recent literature shows the way how it is transforming our perceptions. © 2023 IEEE.Item Dynamic Checkpointing: Fault Tolerance in High-Performance Computing(Institute of Electrical and Electronics Engineers Inc., 2024) Bhowmik, B.; Verma, T.; Dineshbhai, N.D.; Reddy, M.R.V.; Girish, K.K.Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.Item Money Laundering Detection in Imbalanced E-wallet Transactions with Threshold Optimization(Institute of Electrical and Electronics Engineers Inc., 2024) Doddamani, S.S.; Girish, K.K.; Bhowmik, B.The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Consequently, mobile payment systems centered on electronic wallets (e-wallets) have signifi-cantly transformed contemporary finance, introducing improved security measures and transaction functionalities. However, the prevalence of unlawful activities, including money laundering and associated fraud via e-wallets, presents a substantial threat to the integrity of the financial sector. This research paper delves into the pivotal role of machine learning models in identifying money laundering activities within e-wallet transactions. The study focuses on addressing the imbalance inherent in the PaySim dataset through the oversampling technique. Employing three distinct models - Logistic Regression (LR), Gradient Boosting, and XGBoost - the research systematically evaluates their effectiveness. Notably, XGBoost emerged as the standout performer, showcasing exceptional accuracy at 99.88%, precision at 0.9984, and sensitivity at 0.999. Furthermore, a threshold moving technique is employed to enhance the model's efficiency, optimizing its performance in detecting potential instances of money laundering within e-wallet transactions. © 2024 IEEE.Item Historical Analysis of Financial Fraud and Its Future(Springer Science and Business Media Deutschland GmbH, 2024) Girish, K.K.; Bhowmik, B.As the world is sailing toward a highly advanced digital financial culture with the advent of financial technologies (FinTech), more and more people are now under the shore of financial inclusion. Subsequently, new opportunities are created in the financial sector, making it possible for people who were previously unbanked or underbanked to access financial services. However, the rise in financial fraud and its potential implications are creating a rift in the financial sector, resulting in substantial economic losses across the globe. This paper provides an in-depth comprehension of financial fraud, encompassing its historical perspectives and ramifications. After that, factors contributing to fraudulent behavior are highlighted. In addition, the paper presents a comprehensive framework for fraud classification and accentuates the impacts of financial fraud. Furthermore, the paper underscores the aspects influencing future occurrences of financial fraud, enabling the formulation of proactive strategies to prevent and mitigate it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Enhancing Financial Accessibility: A Tailored UPI Payment Application for Divyangjan(Institute of Electrical and Electronics Engineers Inc., 2024) Bhowmik, B.; Sudhama, K.K.; Dongala, J.R.; Antony, R.T.; Girish, K.K.The emergence of financial technology (FinTech) has transformed the financial sector, introducing a new era characterized by state-of-the-art technologies that enhance speed, affordability, and accessibility. The proliferation of the internet and smartphones has further accelerated this transformation, fostering greater connectivity and global interaction. Subsequently, these advancements have significantly expanded financial inclusion, ensuring access to financial services for previously under-served populations. While the rise of FinTech has propelled financial inclusion for many, individuals with disabilities have not experienced commensurate improvements in their financial accessibility. As the banking sector increasingly migrates to online platforms, people with disabilities encounter barriers stemming from inaccessible websites, mobile applications, and online banking services. This paper introduces a specialized UPI payment application designed explicitly for individuals with disabilities. The objective is to integrate this underserved demographic into the digital financial landscape, fostering financial inclusion and enhancing access to essential financial services. © 2024 IEEE.Item Money Laundering Detection in Banking Transactions using RNNs and Hybrid Ensemble(Institute of Electrical and Electronics Engineers Inc., 2024) Girish, K.K.; Bhowmik, B.The financial sector has witnessed significant transformations due to the emergence of financial technology (FinTech), transitioning from traditional paperbased processes to a dynamic digital ecosystem. Despite the industry's advancements driven by FinTech innovations, concerns persist, particularly regarding financial fraud, notably money laundering. Perpetrators exploit modern technologies to launder illicitly obtained funds, posing a global threat to economies. Effective detection mechanisms for money laundering are crucial. This paper introduces a novel approach utilizing a recurrent neural network (RNN) for detecting money laundering in banking transactions. The proposed framework exercises standalone RNN models such as LSTM, GRU, BiLSTM, and stacked RNN models for the detection. Additionally, the effectiveness of hybrid ensemble models combining RNNs with XGBoosts is investigated. The evaluation achieves standard performance metrics, with the stacked RNN model achieving 92% accuracy. Surpassing it, the ensemble model achieves an impressive 95%. These results underscore the superiority of hybrid ensemble models over standalone RNNs, particularly in accurately detecting money laundering activities. © 2024 IEEE.Item An Integrated MPI and OpenMP Approach for Plasma Dynamics Simulations(Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, Y.M.; Girish, K.K.; Verma, L.; Kumar, S.; Bhowmik, B.Plasma dynamics is the behavior exhibited by two or more charged species with respect to electric or magnetic fields. In high-performance computing (HPC) applications, it requires all these factors: the accuracy of parallel implementations, effective inter-process communication, and scalability with respect to workload. This paper points out the limitations in the current approaches to the plasma dynamics problems, and discusses the use of MPI continuation tasks and of its performance enhancement with OpenMP methods. Within the framework of the Vlasov-Poisson system, we develop theory of MPI continuation and describe techniques optimal for its use, which allows to efficiently combine communication with computation, which is quite a difficult task in most of the cases, especially in the case of multidimensional simulations. The results allow better insights on how to increase the level of parallelism and reduce the time to compute, which in turn fosters the formulation of more effective high-performance strategies and also the understanding of the parallelism in plasma simulations using the MPI standard. © 2024 IEEE.Item Enhancing Movie Recommendation Systems with MapReduce Genetic Algorithms: Addressing Scalability and Accuracy Challenges(Institute of Electrical and Electronics Engineers Inc., 2024) Patidar, P.; Posa, S.V.; Girish, K.K.; Rao, S.; Bhowmik, B.In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, often encounter limitations such as data sparsity, cold start problems, and scalability issues. This paper introduces a novel approach that integrates MapReduce technology with Genetic Algorithms (GAs) to address these chal-lenges. Utilizing the Hadoop framework, our MapReduce Genetic Algorithm (MRGA) efficiently processes extensive datasets by distributing tasks across a cluster of machines. The genetic algorithm component optimizes recommendation accuracy through advanced techniques like selection, crossover, and mutation. Our experimental results, based on the MovieLens 100K dataset, demonstrate that the MRGA approach outperforms traditional collaborative filtering methods in terms of recommendation accuracy and scalability. By leveraging MapReduce's distributed computing power and the GA's optimization capabilities, this research offers a robust solution to improve movie recommendations and handle large-scale data efficiently. © 2024 IEEE.Item Enhancing MPI Communication Efficiency for Grid-Based Stencil Computations(Institute of Electrical and Electronics Engineers Inc., 2024) Goudar, S.I.; Nayaka, P.S.J.; Girish, K.K.; Bhowmik, B.In parallel computing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computational tasks, particularly in grid-based computations that solve intricate numerical problems by discretizing spatial domains into structured grids. However, MPI Cartesian communicators exhibit limitations in handling these computations effectively, especially when managing large-scale data exchanges and complex stencil patterns. This paper addresses these challenges by presenting an integrated approach that combines MPI collective and Cartesian communication methods. The proposed solution simplifies data distribution, eliminates redundant interfaces, and enhances communication efficiency. Experimental results show a 43% reduction in execution time and a 40% decrease in communication overhead, with scalability improvements achieving 12.5x speedup using 64 processes. These quantitative outcomes demonstrate the advan-tages of the proposed method over conventional MPI Cartesian approaches, establishing it as a reliable framework for advancing High-Performance Computing (HPC) capabilities in grid-based applications. © 2024 IEEE.Item Outlier Detection in Streaming Data Using Deep Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024) Dudipala, S.; Gangavarapu, S.; Girish, K.K.; Bhowmik, B.In the realm of the Internet of Things (IoT), devices continuously generate a vast and relentless stream of data, providing a real-time representation of digital landscape. The continuous and high-velocity nature of this streaming data poses significant challenges for real-time analysis. Accurate outlier detection within this data is essential, as such anomalies may indicate critical issues, attacks, or errors. Nevertheless, the dynamic and rapidly evolving characteristics of streaming data render traditional outlier detection methods inadequate. This paper investigates the application of Artificial Neural Networks (ANNs), specifically a Multi-Layer Perceptron (MLP), for outlier detection in streaming IoT data. The selection of the MLP from a range of Deep Neural Networks (DNNs) is based on its optimal balance between computational efficiency and model complexity. The model's efficacy is confirmed through rigorous experimentation, demonstrating strong performance across diverse scenarios and data classes. The MLP achieved an accuracy of 99.4%, underscoring its ability to detect even minor deviations from expected patterns. This high level of accuracy establishes the MLP as a robust tool for outlier detection in dynamic IoT environments. © 2024 IEEE.