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This article offers an examination of the transformative impact of artificial intelligence (AI) on marketing within the financial services sector, beginning with an exploration of the industry's evolution. As marketing strategies in financial services have shifted from traditional methods to digital-first, data-driven approaches, AI has emerged as a pivotal force driving efficiency, personalization, and customer engagement. The study delves into specific AI applications, such as predictive analytics for understanding customer behavior, machine learning models for dynamic customer segmentation, and automation tools that streamline targeted campaigns. Through a series of case studies, the article showcases real-world examples of AI-driven marketing successes in financial services, illustrating how companies are achieving enhanced customer satisfaction, increased operational efficiency, and data-driven decision-making. Despite these benefits, the discussion also highlights the potential drawbacks of AI adoption in this space, including ethical considerations, data privacy challenges, and the risk of over reliance on automated systems that could lead to diminished human oversight. By addressing both the powerful advantages and the inherent risks of AI in financial marketing, this article provides a balanced view of its role in shaping the future of customer engagement and operational excellence in financial services.

In the present, risks are always present, but this thing doesn’t mean that the society can’t carry on the economic activity in conditions of a favorable result. Through the risks to which the entity is subject, an important place is the risk of bankruptcy. The level of this risk can be determined by using of large number of liquidity and solvability indicators on the base of dates from the balance of accounts and from the profit and loosing cont. In this paper is analyzed by comparison the risk of bankruptcy of companies listed at Bucharest Stock Exchange from three sectors of activity: production, commerce and tourism. It is analyzed the evolution of score Z, indicator representative for the risk of bankruptcy, on a period of 14 years (2002-2015), enough for every society and sector. The sample of 15 enterprises has been formed on basis of information available for this research. After analysis of results, we notice that the best results concerning the risk of bankruptcy are recorded in the commerce sector and on the last place is situated the tourism sector. In an future approach we will continue the research on a larger number of enterprises, and we will analyze the cause that lead to bankruptcy of societies, emphasizing to elaborate a systems which lead to a lower level the risk of bankruptcy.