In this study, we propose an explainable artificial intelligence framework for banking compliance that integrates traditional machine learning models with large language models to support Anti-Money Laundering and Know Your Customer decision-making. The proposed approach emphasizes regulatory transparency, auditability, and human-centered interpretability while maintaining strong predictive performance. Using an open-source financial dataset from the UCI Machine Learning Repository, we demonstrate how explainable modeling and natural language reasoning can enhance compliance decision support systems. This research aligns with U.S. national interests by improving financial system integrity, reducing compliance costs, and strengthening risk monitoring capabilities in regulated institutions.

Entrepreneurial marketing has developed as a unique area that bridges entrepreneurship and marketing, but its foundational ideas are often unclear. Traditional marketing models rely on structured planning, stable market conditions, and formal research. In contrast, small and medium-sized businesses frequently face uncertainty, limited resources, and shifting customer expectations. This paper revisits the theoretical foundations of entrepreneurial marketing, clarifying its main aspects and examining its connection to traditional marketing ideas. Special attention is given to the classic 4Ps framework in the context of modern small businesses, where decision-making is usually informal, intuitive, and driven by opportunity. Drawing on recent studies from entrepreneurship, strategic marketing, and innovation, the paper suggests a model that views entrepreneurial marketing as a process influenced by recognizing opportunities, using resources effectively, building customer relationships, and experimenting adaptively. This model builds on existing research by highlighting the relationship between how entrepreneurs think, market changes, and the abilities of firms. The paper ends with suggestions for developing theory and future research paths.

The rapid diffusion of artificial intelligence (AI) across financial markets, corporate strategy, and broader economic systems represents one of the most consequential technological shifts of the twenty-first century. This research article develops an integrated academic analysis of how AI reshapes investment decision-making, environmental, social, and governance (ESG) integration, firm value creation, and long-run economic growth. Drawing strictly on the provided literature, the study synthesizes insights from economics, finance, technology management, and innovation studies to construct a comprehensive framework explaining AI’s multifaceted role in modern investment ecosystems. Particular emphasis is placed on AI-enabled ESG investing, portfolio optimization, and performance enhancement, alongside the strategic investments and alliances formed by global technology leaders. The methodology relies on an extensive qualitative synthesis of empirical findings, theoretical models, and institutional evidence, allowing for deep conceptual elaboration without reliance on mathematical formalism. Results indicate that AI contributes to superior information processing, dynamic risk assessment, and adaptive portfolio management while simultaneously reshaping corporate governance practices and sustainability outcomes. At the macroeconomic level, AI acts as a general-purpose technology with the potential to accelerate productivity growth, though accompanied by distributional and regulatory challenges. The discussion critically evaluates limitations related to data quality, model opacity, intellectual property, and ethical considerations, while outlining future research directions. The study concludes that AI’s transformative impact on investment and economic growth is neither automatic nor uniform but contingent on institutional readiness, governance frameworks, and responsible innovation practices.