Volume 8.
Issue 06.

The digital transformation of financial services has significantly altered how insurance products are marketed, distributed, and purchased. Electronic purchasing platforms have emerged as strategic channels through which insurance providers can improve accessibility, reduce operational costs, and enhance customer experience. Despite the growing availability of digital insurance services, customer adoption remains inconsistent across markets due to technological, organizational, behavioral, and security-related factors. This study evaluates customer adoption of electronic purchasing platforms for general insurance policies within Professional Insurance Services. The research synthesizes existing literature on technology acceptance, e-commerce adoption, digital insurance systems, online consumer behavior, and organizational effectiveness to develop an integrated framework for understanding adoption behavior. A quantitative research design is proposed, employing survey-based data collection and statistical analysis to examine the relationships among perceived usefulness, ease of use, trust, website quality, perceived risk, service quality, cybersecurity concerns, and customer purchase intentions. The findings indicate that website functionality, service quality, trustworthiness, and perceived convenience significantly influence adoption decisions, while perceived risk and cybersecurity concerns negatively affect acceptance. The study contributes to the growing body of knowledge on digital insurance adoption by integrating technological and consumer behavior perspectives. Practical implications are provided for insurance organizations seeking to enhance customer engagement, improve platform design, and increase electronic policy purchases. The study further identifies strategic opportunities for Professional Insurance Services to strengthen digital competitiveness through customer-centered platform optimization and enhanced digital trust mechanisms.

This аrticle аnаlyzes the rоle оf technоlоgy integrаtiоn in the mоdern bаnking аnd finаnciаl system оf Uzbekistаn аnd the prоspects fоr its develоpment. The аrticle discusses the use оf blоckchаin suppоrt, clоud technоlоgy аnd mоbile аpplicаtiоns tо simplify аnd аccelerаte the develоpment оf bаnking. The аrticle discusses the wаys tо imprоve the level оf emplоyees аnd entities, ensure the аctivity оf the pоpulаtiоn аnd business, аnd mаnаge cоrruptiоn prоcesses. The аrticle аnаlyzes the wаys tо аchieve digitаl trаnsfоrmаtiоn in the finаnciаl sectоr оf Uzbekistаn thrоugh prаcticаl exаmples, the current stаte аnd prоmising directiоns. Finаlly, it shоws whаt strаtegic rоle cоmputer technоlоgies plаy in the innоvаtive develоpment оf bаnking аnd finаnce.

This article examines the impact of the shadow and hidden economy on sustainable economic development through an integral assessment approach. The study is based on the theoretical and methodological foundations of the non-observed, informal and shadow economy developed by international organizations and previous empirical studies. The research proposes a factor-based integral framework that combines indicators related to fiscal stability, informal employment, cash circulation, institutional transparency, investment activity and socio-economic sustainability. Using the case of Uzbekistan, the article assesses how shadow and hidden economic activity affects the quality of sustainable economic development by reducing tax revenues, weakening formal employment, distorting statistical information and limiting the effectiveness of public policy. The study applies normalization, weighted aggregation and regression-based analysis to construct an integral index reflecting the pressure of the shadow and hidden economy on sustainable development. The results indicate that an increase in the shadow and hidden economy pressure has a negative effect on sustainable economic development, particularly through fiscal losses, lower institutional efficiency and reduced transparency of economic activity. The proposed integral assessment approach can be used as a methodological tool for monitoring hidden economic risks and developing policy measures aimed at formalization, digitalization, tax administration improvement and long-term sustainable growth.

The exponential growth of enterprise artificial intelligence (AI) systems has brought about new levels of efficiency in decision-making, automation, and predictive analytics in fields of finance, healthcare, and supply chain management. Nevertheless, this proliferation has also increased the fear about algorithmic responsibility, especially with regard to unclear decision-making, inherent biases, cybersecurity risks, and non-compliance with regulations. Although there has been an increasing amount of research on AI governance, the current frameworks are still highly fragmented because, in most cases, they tend to focus on risk management, cybersecurity, and ethical matters separately, but not as one system. This project will create an overall governance framework operationalizing algorithmic accountability in enterprise AI settings through a systematic combination of risk analytics, cybersecurity measures, and compliance mechanisms. The study takes a conceptual and analysis approach, which summarizes the findings of the peer reviewed articles, international regulatory guidelines, and industry best practices. Comparative analysis of existing frameworks - such as those suggested by international standard-setting organizations - has shown some essential gaps in cross-domain integration, real-time monitoring, and enforceable accountability facilities. The suggested framework presents a multi-level governance structure that aligns the technical protection with the organizational supervision and auditing of ethical processes. The integration of quantitative risk assessment models, AI-specific cybersecurity controls, like adversarial robustness and secure model lifecycle management, and embedded ethical compliance mechanisms, including fairness, transparency, and explainability, are among the key contributions. The results show that a combined governance strategy is an effective way to improve the transparency, auditability, and resiliency of enterprise AI systems. The study will make a contribution to the developing discussion of AI regulation by providing a model that is scalable and implementation-focused, which can be used to help organizations make decisions and achieve regulatory alignment. The framework offers implementable lessons to businesses aiming to use AI in a responsible manner without compromising to the new global standards.

This article assesses the factors affecting sustainable economic growth in Uzbekistan for the period 2010-2024 using econometric methods. Investment in fixed capital is found to be the strongest and most reliable factor of GDP growth. Foreign trade turnover has a positive, but secondary, effect. The number of economically active population is statistically insignificant, indicating that the quality of labor resources is more important than the quantity. The negative effect of inflation is not statistically proven. The model successfully passed all diagnostic tests (heteroscedasticity, autocorrelation, normality).

This article conducts a systematic analytical examination of central bank policy rate adjustments as a primary monetary transmission instrument influencing the cost of credit resources across developed, emerging, and transitional banking systems. The study investigates the theoretical foundations of monetary policy transmission, reviews the empirical literature on interest rate pass-through mechanisms, and applies a comparative analysis framework across eight national economies - the United States, the Eurozone, the United Kingdom, Japan, China, South Korea, Brazil, and Uzbekistan - spanning the policy cycle from 2021 to 2024. Drawing on data from the Bank for International Settlements (BIS), the International Monetary Fund (IMF), the World Bank, and national central bank statistical publications, the research documents substantial cross-country heterogeneity in the speed, magnitude, and completeness of interest rate transmission to retail lending rates. The analysis identifies structural banking sector characteristics, financial market depth, inflation regimes, exchange rate flexibility, and institutional credibility as the principal determinants of transmission efficiency. Special attention is devoted to Uzbekistan’s monetary policy experience under the Central Bank of Uzbekistan (CBU), contextualizing domestic policy choices within the broader international evidence base. The article concludes with a set of policy recommendations directed at strengthening monetary transmission mechanisms in banking systems characterized by structural rigidities, shallow financial markets, and elevated dollarization.

This article presents a comprehensive scientific analysis of innovative approaches to managing the investment and credit capabilities of commercial banks in the context of rapid digital transformation. The study examines digital banking technologies, artificial intelligence, Big Data analytics, blockchain platforms, and automated credit scoring systems as instruments for enhancing credit risk management, investment decision-making, and overall banking competitiveness. Comparative analysis of international banking experiences across the United States, Germany, Singapore, China, the UAE, and South Korea reveals best practices transferable to emerging economies. Drawing on primary regulatory documents, IMF, World Bank, BIS, and OECD reports, the article evaluates ongoing reforms in Uzbekistan’s banking sector and formulates strategic recommendations for improving investment and credit capabilities within a sustainable development framework.