Artificial Intelligence (AI) is rapidly transforming financial systems across the globe, particularly in emerging markets where traditional risk assessment methods often fail due to data limitations and market volatility. This study examines the role of AI-based financial risk prediction models in improving decision-making, enhancing credit evaluation, and minimizing investment uncertainty in developing economies. The research explores how machine learning algorithms, predictive analytics, and big data integration contribute to identifying potential financial risks such as credit defaults, market fluctuations, and fraud detection. Emerging markets often face challenges such as inconsistent financial records, lack of transparency, and dynamic regulatory environments, making AI-driven solutions highly relevant. The study highlights the effectiveness of AI in improving forecasting accuracy, reducing human bias, and enabling proactive risk management. However, it also considers limitations such as data privacy concerns and technological barriers. The findings suggest that AI-based risk prediction systems can significantly strengthen financial stability and support sustainable economic growth in emerging markets.

Market volatility is a reality in contemporary economic environments, and it affects the way managers process information, make risk assessments, and make strategic decisions. This paper investigates the effect of market volatility on managerial decision-making, using the behavioral economics approach, and it highlights the importance of psychological factors, emotions, and cognitive limitations in managerial decision-making. The paper investigates how factors such as uncertainty, loss aversion, overconfidence, herding, and risk perception influence decision-making during market instability. The study also investigates how managers strike a balance between short-term survival and long-term organizational success when faced with changes in demand, prices, or financial conditions. The study uses both theoretical and practical approaches to identify the patterns of decision-making under pressure and to point out the behavioral aspects that can be both helpful and harmful to business success. The results of the study are expected to provide insights into the human aspects of strategic decision-making and to provide frameworks for improving decision quality in dynamic market environments.

This paper assesses the usefulness of an integrated Systematic Investment Plan-Systematic Withdrawal Plan (SIP-SWP) as a model of long-term wealth development and long-term retirement wealth generation in both equity and debt mutual funds. The study is founded on primary survey data and the secondary historical data on NAV data from 2010–2025. Return performance, withdrawal sustainability and investor behaviour were measured using statistical tests that included CAGR, standard deviation, t-test, chi-square test and Monte Carlo simulation. The results indicate that equity funds perform better in promoting accumulation of wealth because of their greater return potential whereas debt funds would be more stable during the withdrawal period. There was a strong correlation between the expectations of returns and fund preference which shows that the view of investors affects the asset allocation decisions. The analysis endorses the idea of balanced equity–debt allocation and the orderly SIP-to-SWP transition plan to enhance the sustainability of retirement corpus. The proposed model is an AI- assisted and zero-brokerage retirement tool that helps plan asset allocation and inflation-adjusted retirement income.

Startups play a vital role in accelerating India’s economic development by contributing to job creation, innovation, capital formation, and regional growth. They support the expansion of new industries, enhance productivity, and encourage competition, which ultimately strengthens the country’s GDP and overall economic structure. The rise of startups in sectors such as fintech, e-commerce, edtech, agritech, and healthcare has created new market opportunities, increased digital adoption, and attracted both domestic and foreign investments. By promoting entrepreneurship and supporting small-scale innovation, startups also contribute to balanced regional development and reduce dependence on traditional employment sectors. This research paper focuses specifically on the contribution of startups to India’s economic development. The study will use both primary and secondary data collection methods. Primary data will be collected through surveys and questionnaires from Young Entrepreneurs, Students , and other relevant respondents . Secondary data will be gathered from government reports, startup databases, research journals, and publications from organizations such as DPIIT, NASSCOM, and RBI. The paper will also evaluate the strengths and weaknesses of startups in the context of economic development. Key strengths include employment generation, innovation-driven growth, increased foreign investment, and the development of new business ecosystems. However, startups also face weaknesses such as high failure rates, funding challenges, regulatory barriers, and limited access to skilled resources in certain regions.

This study looks at the role and impact of AI-powered predictive consumer behavior models in the kalyan- dombivli municipal corporation KDMC area it explores how businesses use AI technology to analyze consumer data predict buying habits and offer personalized services in a more digital marketplace as e-commerce platforms digital payment systems and data-driven marketing strategies grow rapidly consumer decisions have become more tech-focused this shift creates a need to understand how predictive models are adopted and how effective they are at the regional level the study aims to clarify the concept and functioning of AI-powered predictive consumer behavior models check the awareness and use of AI tools among businesses in the KDMC area evaluate how AI supports marketing and decision-making and identify the challenges and limits of predicting consumer behavior through AI technology this research uses a mixed-method approach it collects primary data from 64 respondents including professionals business owners and consumers in the KDMC area it also looks at secondary data from academic literature and industry sources the findings show that businesses increasingly use AI tools for product research recommendations and purchase support especially among younger consumers and working professionals however while AI improves customer engagement and marketing success concerns about data privacy transparency and trust in algorithms significantly affect how consumers accept AI systems the research further shows that AI-powered predictive models improve business competitiveness and help in making informed decisions yet challenges like limited knowledge of technology infrastructure issues and ethical concerns prevent full implementation the study concludes that successfully adopting AI-powered predictive consumer behavior models in KDMC requires better transparency responsible data practices and stronger consumer trust it also calls for strategic adjustments to fit local market needs this topic is important because artificial intelligence is quickly changing consumer behavior and business practices in the digital economy however little research has looked at its effects at the municipal level especially in emerging urban markets like KDMC this study was chosen to provide a unique perspective by focusing on a local market rather than broader national or global trends by examining region-specific consumer behavior patterns levels of technology adoption and implementation challenges the research offers new insights that set it apart from existing studies enhancing its relevance and originality

As digital technology becomes more common in government, there is a growing demand for Civic-tech solutions that can actually fix real-world problems. This paper looks at the Layered Authentication and Voting Integrity System (LAVIS), a new B2G (Business to Government ) Gov-tech startup model designed to create a highly secure, offline-first electoral process. Finding the right balance between human- centric design and sustainable innovation, LAVIS uses a five-step authentication process: a physical KYC check, temporary RFID smart cards, live facial recognition, biometric scanning, and a deactivated cryptographic QR audit trail. Because the system tries to eliminate current gaps and loopholes. Offline database structure segregated into state, constituency, and booth levels ( SDB - CDB - PBD) , it essentially neutralizes remote cyber threats and mass data tampering. This study explores the technical viability of LAVIS and ensures it is legally compliance . Ultimately, it shows how digitally synergized startups can eliminate voter impersonation, cut long-term costs, and help restore public faith in the democratic process.

Sustainability means meeting our present needs without compromising the ability of future generations to meet theirs. It is build on components such as: • Social Sustainability • Economic Sustainability • Environmental Sustainability Let’s take view of sustainability in terms of fashion industry. In fashion industry to keep up with the trend, the brands keep on manufacturing the goods neglecting factors such as social, economic and environment leading to disbalance of the nature. Therefore it is important to keep balance in all the aspects of sustainability. Sustainability in Fashion industry, especially, in bags and accessories sustainability focuses on reducing environmental impact while maintaining ethical and profitable production. Digital platforms plays a crucial role in today’s world in maintaining balance in sustainability, in fashion industry. Use of technology in the processes of sustainability management may help the fashion industry to develop a new, responsible and circular economy. Through this research, it is possible to suggest changes that would change the fashion industry in long run and therefore significantly decrease the negative impact of fashion on the environment, increase the ethical aspects of production, and make the fashion consumption and production processes more sustainable. Implementing the suggested changes would help reduce the negative environmental consequences associated with fashion production, such as excessive water consumption, chemical pollution, textile waste, and greenhouse gas emissions. In addition, it also looks at how the use of technology can enhance ethical standards, including fair labour conditions and supply chain management. Besides, providing brands and manufacturers with information about new technologies in sustainable fashion, this study helps consumers to make better and more environmentally friendly decisions. Overall, this research demonstrates that through evidence-based recommendations and coordinated efforts among policymakers, brands, manufacturers, and consumers, the fashion industry can move toward a more sustainable, ethical, and environmentally responsible future.