This article provides the main essence of bank liabilities, operations related to the organization of bank resources, sources of funds raised, as well as information necessary for assessing deposits, loans and other liabilities.

Corporate investment in Artificial Intelligence (AI) has become a critical driver of economic growth and technological innovation. This article examines the role of key macroeconomic indicators—Gross Domestic Product (GDP), Information and Communications Technology (ICT) exports, and patents—in influencing corporate investments in AI. Through a comprehensive literature review and analysis of data from leading AI-adopting countries, the study reveals a strong correlation between GDP growth, robust ICT exports, and high levels of patent activity with increased corporate AI investment. The findings highlight that countries with higher GDPs, advanced ICT infrastructure, and significant patent output in AI-related fields create an environment conducive to innovation and AI adoption. As AI technologies continue to evolve, understanding the relationship between these economic indicators and corporate AI investment is essential for fostering sustainable growth and maintaining a competitive edge in the global market.

Artificial intelligence integration in US insurance underwriting is revolutionizing the way risk is assessed, costs are made efficient and fraud is detected, such use raises many ethical and economic tradeoffs. A key problem of AI powered actuarial models is that speed and accuracy in the underwriting is enhanced, biases within the algorithms, transparency of the algorithms, trust of the consumer and regulatory oversight are issues that can still prevent the advancement of AI in underwriting.  this research study uses a quantitative research approach in studying the impact of AI underwriting models through using survey data and data analysis as well as real life case studies in evaluating gains in efficiency, ethical risks and regulatory consideration. Findings indicate that AI can dramatically lower the cost of underwriting and enhance the rate of detecting fraud while consumers remain very skeptical about fully automated underwritten models, looking most positively upon hybrid AI and human models. Important factors that affect adoption of AI in underwriting are regulatory oversight and mitigation of bias. The study argues that the existence of explainable AI frameworks, the presence of the data governance and compliance measures are all necessary to strike a balance between efficiency and fairness. Overcoming these challenges, AI-powered underwriting can contribute to the country’s economic growth, improve consumer trust and be aligned with the country’s changing U.S. regulatory frameworks. These insights can benefit insurers, policymakers and regulatory bodies in responsible development of fair, efficient and transparent AI underwriting models for the U.S. insurance industry.