Published Article

ABSTRACT

In the evolving landscape of financial management, the convergence of Big Data and Artificial Intelligence (AI) has emerged as a transformative force, promising enhanced decision-making, risk mitigation, and strategic foresight. This study critically analyzes the potency of integrating AI-driven technologies for search of entrepreneurial business. It explores how this integration facilitates real-time data processing, predictive modeling, and automated financial operations, thereby improving efficiency, accuracy, and responsiveness to market dynamics. The analysis considers both the technical capabilities and the strategic implications of such systems, highlighting their role in fraud detection, investment forecasting, credit scoring, and portfolio optimization. However, the study also interrogates the challenges that accompany this integration, including data privacy concerns, algorithmic biases, scalability issues, and the dependency on quality data inputs. Through a multidisciplinary lens, this research underscores that while the integrated system holds immense potential, its efficacy is contingent upon ethical governance, robust infrastructure, and continuous refinement. Ultimately, this paper advocates for a balanced approach that harnesses the strengths of Big Data and AI while addressing the inherent risks to achieve effective and sustainable financial management.