This study examines the relationship between gender board diversity and financial performance using a comprehensive dataset of 2,341 Indian listed companies from 2009 to 2023. Exploiting India’s 2013 mandatory gender quota requiring at least one woman director on corporate boards, we employ a difference-in-differences approach to identify causal effects. Our findings reveal a positive and significant relationship between gender diversity and financial performance, with firms experiencing a 4.2
FinTech's expansion has brought about increased exposure to fraud threats. Traditional rule-based systems have been found to be ineffective in handling the changing fraud patterns. The current study outlines the implementation of AI-driven fraud detection tools. Such technologies will lead to a secure FinTech environment by raising the accuracy, versatility, compliance, and user trust levels. To address the issue of a lack of scalable and regulation-compliant frameworks, a quantitative cross-sectional design has been employed. Data were gathered from 384 professionals including fraud analysts, cybersecurity experts, and compliance officers. Machine learning (ML) algorithms such as Logistic Regression, Random Forest, SVM, SEM, ANN coupled with the employment of structured questionnaires were conducted to evaluate twelve variables dealing with technical and adoption aspects. The uncovered results highlight first the efficiency of detection, the lowering of false alarms, the easing of real-time adaptability, and the gaining of robustness through scalable frameworks. The mentioned outcomes are envisaged to provide real-world AI implementations in leading reliability and regulation-alignment for the safer next-generation FinTech ecosystems.
WealthTech's rapid scaling has radically changed financial planning and investment practices. However, the robo-advisory platforms of today are still failing in the aspect of providing effective personalization and establishing a great level of trust with the decision support systems. In order to help solve these problems, the present research is examining the deployment of AI-powered WealthTech technologies by leveraging survey data of 384 individual investors collected through probability-based on Cochran sampling method. The data was collected using structured questionnaires consisting of 12 coded variables, and the reliability of the data was verified by a Cronbach's Alpha value of 0.89. The range of the techniques that have been used for data processing include statistics and AI/ML, such as clustering, logistic regression, canonical correlation analysis, multinomial logistic regression, and structural equation modeling, and they have been applied for studying the investor segmentation, behavioral tendencies, personalization requirements, and the role of AI in the financial decision- making process. The results show the characteristics of the investor clusters which are defined by the risk tolerance and investment objectives, also, the findings reveal the associations between financial experience and the choice of certain financial instruments.
Green branding has developed as a strategic & planned approach for establishments looking for competitive advantage despite the fact to address environmental sustainability. In the period of ecological imbalance & climate change, organizations are gradually adopting green branding-based strategies to line up with sustainable development goals (SDGs). Green branding generally denotes to endorsing goods & services based on eco-friendly advantages & sustainability assurances. Todays’ consumers are quite ecologically conscious & preferring brands that validate environmental responsibility. Considering the behavioral forms of consumers towards green brands or green products is important for adopting strategic decision-making. This research analyzes the association in between initiatives of green branding & sustainable consumer behaviour (SCB). The study also discovers how green_brand_trust (GBT), environmental_concern (EC), eco_awareness (EcoA) & perceived green product quality (PQ) impact consumers’ green_purchase_intention (GPI). Total 120 responses received through structured questionnaire whereas Cronbach alpha, KMO & Bartlett’s Test analysis, factor analysis, correlation test, regression analysis, chi_square test along-with ANOVA analysis were used as a statistical tool. The findings of the research disclose that green_brand_trust (GBT) & environmental_concern (EC) significantly impact sustainable consumer behaviour (SCB). The research also provides decision-making inferences for marketers targeting to reinforce green positioning approaches.
The wellness tourism industry in the world is booming at an unprecedented rate and is expected to rise 1 trillion USD by 2025. India stands in a perfect position to boost on this growth thanks to its rich history of Ayurvedic therapies and Yoga, which is 5,000 years old, and massive policy backing in the Heal in India project. The market has a strong growth potential with a CAGR of more than 6.3% until 2030, but inherent volatility of the market due to external shocks, fast changing consumer preference towards personalised and technology enabled wellness and critical barriers to market implementation of lack of standardisation, and limited awareness of brands globally. Predictive modelling is essential to ensure that the policies of the policymakers improve the investment and accreditation of infrastructure. It is a hybrid study between a time-tested Autoregressive Integrated Moving Average (ARIMA) model and the Gated Recurrent Unit (GRU) deep learning network that can predict the market worth of Indian wellness tourism. ARIMA finds linear patterns in historical data, whereas GRU model is better at identifying and measuring non-linear relationships caused by other factors, such as major government policies, demand boom after pandemics, and technology adoption cycles. Through the integration of these future projections, this paper offers accurate, contextually sensitive predictions, which present invaluable, practical information to all stakeholders.
