Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal The Eastasouth Journal of Information System and Computer Science
Ravikumar Mani Naidu Gunasekaran
Independent researcher, California, United States
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Operational Challenges in Basel IV Credit Risk Compliance Ravikumar Mani Naidu Gunasekaran
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i01.1089

Abstract

The implementation of Basel IV regulations represents a significant advancement in global banking supervision, with a strong focus on enhancing the accuracy, consistency, and transparency of credit risk measurement. While these reforms strengthen the resilience of financial institutions, they introduce substantial operational complexities, particularly in the areas of data management, system integration, model governance, and regulatory reporting. This paper examines the key operational challenges faced by banks in complying with Basel IV credit risk requirements, including the adoption of revised standardized approaches, restrictions on internal ratings-based (IRB) models, and the introduction of the output floor. The study highlights critical issues such as fragmented data architectures, legacy system constraints, increased computational demands, and the need for robust data lineage and governance frameworks. Additionally, the paper discusses the implications of heightened regulatory scrutiny and the requirement for greater model transparency and validation under evolving compliance standards. To address these challenges, the paper outlines strategic approaches involving modernization of technology infrastructure, adoption of cloud-based platforms, automation of reporting processes, and integration of advanced analytics. By providing a comprehensive assessment of operational barriers and potential solutions, this study aims to support financial institutions in navigating the complexities of Basel IV implementation. The findings underscore the importance of aligning organizational processes, technology, and governance frameworks to achieve effective and sustainable compliance in an increasingly data-driven regulatory environment.
Liquidity Risk Modeling with Machine Learning: Big Data Approaches for Intraday Liquidity Prediction Ravikumar Mani Naidu Gunasekaran
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1096

Abstract

Liquidity risk has emerged as a critical concern for financial institutions due to increasing market volatility, regulatory scrutiny, and the growing complexity of global financial systems. Traditional liquidity risk management approaches, which rely on static assumptions and low-frequency data, are often inadequate for capturing rapid intraday fluctuations in cash flows and funding requirements. This paper explores the application of machine learning techniques combined with big data architectures to enhance intraday liquidity prediction and risk modeling. The study presents a data-driven framework that leverages high-frequency transactional data, market indicators, and behavioral patterns to forecast liquidity positions in near real time. Advanced machine learning models, including ensemble methods and deep learning architecture such as Long Short-Term Memory (LSTM) networks are evaluated for their ability to capture nonlinear dependencies and temporal dynamics inherent in liquidity flows. The proposed approach integrates scalable big data technologies to support real-time ingestion, processing, and predictive analytics. Results demonstrate that machine learning-based models significantly outperform traditional methods in forecasting accuracy and responsiveness to market stress conditions. The paper also discusses practical implementation considerations, including model interpretability, regulatory compliance, and integration with enterprise treasury systems. By enabling proactive liquidity management and early detection of stress scenarios, the proposed framework offers substantial improvements in financial resilience and operational efficiency for modern banking institutions.
Generative AI: Opportunities, risks and implications for Financial Services Ravikumar Mani Naidu Gunasekaran
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.1098

Abstract

Generative Artificial Intelligence (GenAI) is rapidly transforming the financial services industry by enabling advanced automation, intelligent decision-making, and enhanced customer experiences. Technologies such as large language models and generative models are reshaping processes across risk management, fraud detection, regulatory reporting, and customer engagement. However, the adoption of GenAI introduces significant challenges, including model risks, data privacy concerns, regulatory uncertainties, and ethical implications. This paper explores the opportunities and risks associated with generative AI in financial services and proposes a structured framework for responsible adoption. By integrating governance, risk management, and regulatory compliance mechanisms, the study provides practical insights for financial institutions seeking to leverage GenAI while ensuring security, transparency, and resilience.
From Data to Decisions: How Quality Drives Machine Learning Success Ravikumar Mani Naidu Gunasekaran
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1099

Abstract

In the era of data-driven decision-making, machine learning (ML) has emerged as a critical tool for extracting insights and enabling intelligent automation across industries. However, the success of ML models is fundamentally dependent on the quality of the data used throughout the analytics pipeline. This article explores the relationship between data quality and machine learning performance, emphasizing how data integrity directly impacts model accuracy, reliability, and fairness. Key dimensions of data quality—including accuracy, completeness, consistency, and timeliness—are examined in the context of real-world ML applications. The article further discusses common data challenges such as missing values, noise, bias, and data drift, highlighting their implications on predictive outcomes. Additionally, it presents practical approaches to improving data quality through data preprocessing, validation, governance frameworks, and automated monitoring systems. By bridging the gap between raw data and actionable insights, this study underscores that high-quality data is not merely a prerequisite but a strategic enabler of successful machine learning initiatives. Organizations that prioritize data integrity can achieve more robust models, better decision-making, and sustain competitive advantage in an increasingly data-centric world.
Co-Authors