Dr.R.V.Naveenan
@sibmbengaluru.edu.in
Assistant Professor
Symbiosis Institute of Business Management, Bengaluru, 95/1, 95/2, Hosur Rd, Electronics City Phase 1, Electronic City
, is working with Symbiosis International University, Bengaluru, India as Assistant Professor. He has obtained his Ph.D. in Management (Finance) from Manonmaniam Sundaranar University, Tamil Nadu, India. He has thirteen years of experience in academics and research. He is a passionate teacher and enthusiastic researcher. He has presented his research ideas in various national and international conferences. He has published around 15 research papers in Journals. His area of research is Banking, Sustainable Finance, Climate Finance and Energy Finance.
EDUCATION
MBA, MA(Economics),PhD
RESEARCH, TEACHING, or OTHER INTERESTS
Business, Management and Accounting, Economics, Econometrics and Finance, Social Sciences
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jayasri Kotti, C. Naga Ganesh, R. V. Naveenan, Swapnil Gulabrao Gorde, Mahabub Basha S., Sabyasachi Pramanik, and Ankur Gupta
IGI Global
The rise of cloud computing, internet of things, and information technology has made big data technology a common concern for many professionals and researchers. A financial risk control model, known as the MSHDS-RS model, was creatively suggested in response to the present state of inappropriate feature data design in big data risk control technology. The concept is built on multi source heterogeneous data structure (MSHDS) and random subspace (RS). This model is novel in that it uses a normalized sparse model for feature fusion optimization to create integrated features after extracting the hard and soft features from loan customer information sources. Subsequently, a base classifier is trained on the feature subset acquired via probability sampling, and its output is combined and refined by the application of evidence reasoning principles. The accuracy improvement rate of the MSHDS-RS method is approximately 3.0% and 3.6% higher than that of the current PMB-RS methods under the conditions of soft feature indicators and integrated feature indicators, respectively, according to an observation of the operation results of MSHDS-RS models under various feature sets. As a result, the suggested optimization fusion approach is trustworthy and workable. This study has helped to reduce financial risks associated with the internet and may be useful in helping lenders make wise judgments.
R. V. Naveenan, Chee Yoong Liew, and Ploypailin Kijkasiwat
Springer Science and Business Media LLC
Naveenan R. V, Ooi Kok Loang, Najaf Iqbal, Suresh G, and Mohd Asif Shah
Informa UK Limited
Asif Pervez, R. V. Naveenan, Ali Hazim Alyamoor, Rohit Bansal, Ankur Gupta, and T. Joby Titus
AIP Publishing
Naveenan Ramaian Vasantha, Chee Yoong Liew, and Ploypailin Kijkasiwat
Emerald
Purpose Research on financial inclusion (FI) in Islamic countries has evolved and gained prominence. This study aims to construct an extensive multidimensional FI index to ascertain the level of inclusion and trends in the Middle East/North Africa (MENA) countries. Additionally, this study examines the potential role of Islamic finance in improving access to financial services. Design/methodology/approach Data for the study were collected from databases covering MENA countries for the period 2010–2020. An inclusion index has been constructed using the entropy method. Findings Key findings indicate that the overall FI has improved in Islamic countries. However, it should be noted that all MENA countries fall within the low or medium levels of the inclusion index. It was observed that insurance access and penetration savings were poor in the Islamic MENA countries. Social implications The authors recommend that policymakers focus on insurance access and saving behaviour in their respective countries. Based upon these observations, policymakers should promote the economic benefits of Islamic finance, which will help improve FI and economic development in Islamic countries. This study emphasises the necessity of policy framework reform to provide Islamic financial services to the poorest in society at low or no cost for better economic benefits. Originality/value Most studies tend to overlook important indicators such as insurance, savings and credit penetration while calculating the index. These indicators add value to the existing literature. The majority of prior studies used United Nation Development Programme methodology or principal component analysis for Inclusion Index measurements. The adoption of the entropy weighting method is the novelty of this study.
OOI KOK LOANG, ZAMRI AHMAD, and R. V. NAVEENAN
World Scientific Pub Co Pte Ltd
This study examines the relationship between bank-specific variables, macroeconomic variables and non-performing loans (NPLs) in the seven countries of Southeast Asia (Cambodia, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam) during the pre-COVID-19[Formula: see text]and COVID-19 pandemic. This study adopts panel data regression and distributed lagged regression to examine the impact of bank-specific variables and macroeconomic variables as NPL determinants. The results show that bank-specific variables significantly correlate to NPL, but limited evidence indicates the influence of macroeconomic variables during pre-COVID. Nonetheless, macroeconomic variables are significant to NPL with the emergence of the pandemic, while the bank-specific variables are found to be insignificant. It shows that macroeconomic variables have a greater impact during the turbulent period as they affect most businesses, especially during the pandemic. Furthermore, macroeconomic variables are observed to have a stronger influence on developed countries, but the impact of bank-specific variables is stronger in emerging countries. The results of this study assist policymakers, regulators, banks and governments in identifying the determinants of high NPL as the indicator of a financial crisis. Greater emphasis shall be given to the changes in macroeconomic variables.
R.V. Naveenan and G. Suresh
Routledge
R.V. Naveenan, T. Jarin, and S.R. Boselin Prabhu
Inderscience Publishers