The growing importance of corporate social responsibility (CSR) in the financial and investment sectors has led asset managers to reassess how they integrate social and environmental factors into their investment decisions. This article investigates the perceptions of asset managers on the challenges and opportunities presented by CSR in the context of modern asset management. Through a combination of literature review, qualitative interviews, and case studies, the research explores how asset managers navigate the complexities of CSR, including balancing financial goals with ethical considerations, managing risks, and identifying growth opportunities. The findings suggest that while CSR presents certain challenges, including data inconsistencies and regulatory uncertainties, it also offers opportunities for innovation, improved stakeholder relationships, and long-term value creation. The article concludes with insights into how asset managers can better address CSR-related concerns and leverage them to enhance their investment strategies.

In the United States, artificial intelligence (AI) has become a transformative force in the business analytics area related to financial risk assessment for banking and insurance industries. The aim of this research is to assess adoption, effectiveness and challenges of AI driven risk assessment models, by analyzing data collected through a survey, which was distributed to 200 financial professionals across the U.S. According to the findings, AI plays an important role in increasing the accuracy of fraud detection, reducing credit risk, predicting market risk, minimizing operational risk and other decisions and optimizing cost efficiency at the financial institutions. The adoption of AI technology in improving the efficiency of the pharmaceutical industry is hindered by some key barriers such as concerns about data privacy, compliance regulations, high implementation costs and shortage of AI specialists. According to the results, financial institutions need to expand governance frameworks to ensure the regulatory alignment and ethics in using AI in a transparent way while maintaining safe risk assessment model. The contribution of this study to the current debates on AI and finance risk management, as well as implications for both the policymakers and financial industry practitioners, might include practical advice and recommendations to financial institutions and researchers on better integrating AI in banking and insurance risk assessment systems.