Mugdha
@scit.edu
Associates Professor
Symbiosis Centre for Information Technology
RESEARCH INTERESTS
Finance and Accounting
Scopus Publications
Scopus Publications
Mugdha S. Kulkarni and Kanchan Patil
Springer Nature Switzerland
Mugdha Shailendra Kulkarni, Dhanya Pramod, and Kanchan Pranay Patil
Springer Nature Switzerland
Nisha TN and Mugdha Shailendra Kulkarni
Emerald
Purpose The purpose of the study is to confirm the fact that in informations security, the human factor was considered as a key carrier of the majority of attacks that an information system faces. Banking and other financial services are always top among the most attractive targets for cyber attackers. Blind phishing or spear phishing is still one of the major contributors to all malicious activities in the e-banking sector. All the counter mechanisms, therefore, revolve around the concept of how security-aware the customers are. To fool these mechanisms, attacks are becoming smarter and are searching for methods where the human involvement is diminishing to zero. Zero click attacks are one big leap that attackers are taking that removes the requirement of human involvement in initiating attacks and are moving toward an era of unassisted attacks. Even though the standard procedure and protocols are built into the banking system, they fail to detect this attack resulting in significant losses. Design/methodology/approach This paper follows a conceptual review of the upcoming concept in security and its implication in e-banking sector. The methodology adopted in this paper uses review papers, articles and white papers to conclude a theoretical model. A detailed analysis of unassisted attacks is considered from 2010 onwards till 2022. Findings This research deliberates on the methodologies of zero click attacks and gives a detailed analysis of attack vectors and their exploits. This research also identifies the likely attacks on e-banking that these vulnerabilities can trigger. Originality/value The key contribution is toward the early detection of zero click attacks, suggesting countermeasure, reducing the likelihood of these attacks and the financial impact.
Lydia Fernandes, Mugdha Kulkarni, and Mandaar B. Pande
IEEE
Quantum Computing is a dynamic field which has evolved since its inception around 3 decades back, to the development of various powerful algorithms including Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) to name a few. Today's quantum computers are not as powerful as the current breed of supercomputers. However, the existing breed of NISQ (Noisy Intermediate Scale Quantum) quantum computers have a significant potential to provide faster solutions to problems in various domains which are not just relevant for the present but also for the future such as Quantum Machine Learning in the Fintech domain. Portfolio Optimization (PO) is one such problem in research and the financial services industry at large. In principle, Quantum Computing, makes it possible to arrive at an optimal portfolio composition for a high return and low risk investment much faster than any existing supercomputer today. This review paper examines literature on classical and quantum machine learning approaches for Mutual Fund PO, analyzing 44 papers from 2003 to 2023. PO being a vast topic, we limit our scope to Mutual Funds and its driver that is the equity markets. We provide an overview to the types of problems, preferred approaches, their benchmarks, deduced conclusions, and research gaps as a comprehensive survey for diverse readers.
Mugdha Shailendra Kulkarni, Hrishikesh Laxman Naik, and S. Vijayakumar Bharathi
IEEE
The General Data Protection Regulation (GDPR) has far-reaching implications that extend beyond the European Union (EU) borders. Its visionary concepts have worked as a catalyst, inspiring states and regions worldwide to rethink their data privacy policies. The GDPR is the foundation of an international privacy framework that crosses political and cultural boundaries, encouraging a collaborative effort to protect personal data. This study aims to explore the GDPR's multifaceted effects on data privacy, including organisational, legal, and technological aspects. To provide comprehensive insights, data was collected from the privacy policies of the top 50 websites for both the pre-GDPR and postGDPR eras. Textual analysis of these policies allowed for a comparison using keyword frequency, personal information disclosure, GDPR-related terms, and the enumeration of user rights being evaluated. To examine the structural changes in privacy rules, we use the average frequency with which a specific group of words. The finding reveals that the average word count of privacy rules has increased significantly, rising from 2355.74 to 5882.82. This shift indicates a positive trend towards increasing transparency and clarity in communicating privacy policies. Privacy policies are now written in more precise, accessible language, emphasising the essence of GDPR's objective and improving user comprehension. In the post-GDPR, key terms such as “Consent,” “Data Breach,” “Data Subject,” “Data Controller,” and “Data Protection” feature prominently, indicating more awareness and more substantial compliance obligations. Implementing GDPR user rights under amended rules demonstrates a noteworthy dedication to user data protection.
Manoj Hudnurkar and Mugdha Kulkarni
IEEE
The study aims to create a method to determine the credit rating and evaluate the risks related to the Indian banking business. To create predictive models for credit ratings, Artificial Neural Network (ANN) algorithms are used, which include Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. For this purpose, secondary data from over 254 Indian banks covering 2014–15 to 2021–22 were considered. The study evaluated crucial factors to predict credit scores while considering business, financial, and industrial hazards. The data includes current liability and Revenues, COGS, SG&A, R&D, EBIT, EBITDA, Interest expense, Abnormal Gains/Losses, Income Taxes, Net Income from Discontinued Op., Net Profit, and Dividends. Current and quick ratios. GDP per capita, The research's main contribution is its capacity to forecast how credit ratings for different Indian banks may change over time based on their financial, commercial, and industrial risk profiles. This cutting-edge method of credit rating prediction improves forecasting accuracy. It boosts the effectiveness of rating systems, providing invaluable insights for the banking sector to navigate these uncharted times while analysing credit risks successfully.
Mugdha S Kulkarni, Dudhia Hard Ashit, and Chauhan Nency Chetan
IEEE
Organizations strive to secure their valuable data and minimise potential damages, recognising that critical operations are susceptible to attacks. This research paper seeks to elucidate the concept of proactive cyber threat hunting. The proposed framework is to help organisations check their preparedness against upcoming threats and their probable mitigation plan. While traditional threat detection methods have been implemented, they often need to address the evolving landscape of advanced cyber threats. Organisations must adopt proactive threat-hunting strategies to safeguard business operations and identify and mitigate unknown or undetected network threats. This research proposes a conceptual model based on a review of the literature. The proposed framework will help the organisation recover from the attack. As the recovery time is less, the financial loss for the company will also be reduced. Also, the attacker might need more time to gather data, so there will be less stealing of confidential information. Cybersecurity companies use proactive cyber defence strategies to reduce an attacker's time on the network. The different frameworks used are SANS, MITRE, Hunting ELK, Logstash, Digital Kill Chain, Model in Diamonds, and NIST Framework for Cybersecurity, which proposes a proactive approach. It is beneficial for the defensive security team to assess their capabilities to defend against Advanced Threats Persistent (ATP) and a wide range of attack vectors.
Mugdha Kulkarni, Juhi Tikyani, and Krishna Kumar Singh
Springer Nature Singapore
Mugdha Kulkarni, Arnab Mondal, and Krishna Kumar Singh
Springer Nature Singapore
Kanchan Patil and Mugdha Kulkarni
IEEE
This research empirically studies the perceptions of users/patients and medical practitioners towards adopting AI-based mobile healthcare chatbot services. The study presents a unified consumer behavioural model of Chatbot adoption having user-related psychosocial, technological, health factors and Trust by taking the theoretical base of Extended TAM, the propensity to trust technology and health, belief model. The causal conceptual model relationship hypothesized in the proposed model was validated using “PLS-SEM” with 265 responses. The findings confirmed that system-related “perceived usefulness (PU)” and “perceived ease of use (PEOU)”, “Social influence”, “Privacy”, and “Facilitating Condition” are salient antecedents of trust beliefs. Other Trust beliefs and Health Beliefs measured through Perceived benefits and perceived barriers are direct predictors of the adoption intention toward the health chatbots. Propensity to trust, Safety risk, and health beliefs like perceived severity and perceived susceptibility have an insignificant impact on health Chatbot adoption. The proposed integrative psychosocial-technological-health based on Trust is a theoretical model. The empirical data from stakeholders of the health department, including government officials, is thus tested.
Rahul Dhiman and Vimal Srivastava
Routledge India
Mugdha Kulkarni and Vijayakumar Bharathi. S.
IGI Global
The purpose of this research is to study how intellectual capital impacts firm performance in information technology companies in India. Based on an extensive review of the existing literature, critical factors of intellectual capital (human capital, structural capital, and relational capital), and firm performance (financial performance and brand reputation) were defined to build a theoretical (causal) model. Senior executives of Indian IT companies (N=112) formed the sample for the empirical study. Using structural equation modeling, the study found human capital and structural capital impact brand reputation and financial performance. Interestingly, relational capital impacted financial performance and not brand reputation. The outcome of this research is significant in two ways: one, it explored the impact of intellectual capital on brand reputation and financial performance, which underpins research directions from the literature, and two, it contributed to the comparatively less-researched contextual relevance from an Indian information technology company perspective.
Vijayakumar Bharathi S. and Mugdha Shailendra Kulkarni
IGI Global
This research article investigates the impact of using a Monopoly Board Game (MBG) in the teaching-learning process of financial statement analysis (FSA) to information technology management students, who earlier had little or no finance or accounting prior educational background. The subjects were students (N=159) in an Indian University. The study; first, narrated the process of administering MBG; second, quantitatively analyzed the learning experience through a structured questionnaire to validate the research objectives. The study resulted in the creation of three factor-clusters namely cognizance, collaboration, and enthusiasm which impacted students' MBG learning experience over the traditional teaching-learning methods. Results showed that factors relating to cognizance are more impacting than collaboration and enthusiasm. In the future, this research can be extended to advanced finance courses and can be integrated with relevant educational theories that underpin teaching-learning processes in higher education to other disciplines.
Dr.Kanchan Patil*, , Dr.Mugdha S Kulkarni, and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
The growing sophistication technology has helped us exchange Information at our fingertips, eliminating the need for human support.” A platform designed to understand, learn and converse like a human and answer ad-hoc queries in real time is commonly referred to as a Chabot”. Chabot advisor is Artificial intelligence (AI) computer program that impersonates human communication in its natural format including text or spoken language using a technique such as NLP, image processing or video processing along with the end task completion as instructed by the user [1]. The purpose of the paper was to examine what are the drivers for Chabot advisor services adoption (CBA), focusing on financial services. This study presents the explanatory Chabot advisor services factors by extending the Technology Acceptance Model (TAM). The construct in the research are like perceived privacy, perceived security, enjoyment and social influence. This empirical study was conducted in Pune city in India by collecting primary data from 310 online financial services customers. Data collected was analyzed using structural equation modeling using PLS-SEM.The outcome of this study is vital to financial companies like banks, policymakers, technology services adoption literature and provide customer-centric financial services.