The economic activity of an organization is always associated with different types of risks. Financial statements serve as an information base for their identification, assessment, and control over their level. Almost all known risks affect the reliability of financial and completeness of non-financial reporting. Today the issues concerning approaches to assessing risk situations, ways of reflecting information about them in the reporting of companies remain topical. The emergence of new types of risks poses new challenges for business entities. It includes reducing or avoiding negative consequences from the onset of risks; unification of information about them in financial and non-financial reporting; search for methods of evaluation, control, and management of their new types. During the study, the author conducted a bibliographic study of the economic literature in the field of disclosure of information about risks in reporting, using methods of generalization, systematization, grouping, analysis, and synthesis.

Modern entrepreneurship and business cannot exist, develop, and make profits without applying digital technologies. Neurocomputerization of the production process, for example, helps to solve a wide range of issues related to sales, management, and expansion. This proves beneficial not only for the companies themselves, but also for the state economy as a whole, since this process retransmits and transforms the basic and obsolete methods and means of managing and producing tangible and intangible assets into new projects and architectures. Fintech and internet companies have taken over the banking industry for good. Currently, in order to compete in the banking services market, banks must invest time and resources in multi-channeling; actively develop digital products and technologies; conduct marketing research on customer behavior; modernize websites and applications; improve and stabilize credit and financial scoring, etc. Today, many traditional banks believe that participating in the information race for the consumer in the IT field is becoming a matter of “life and death” for their business, because the Internet giants are getting better at processing huge databases, thus ensuring more accurate and faster targeting. The modern banking industry is constantly developing neural network technologies and optimizing huge layers of information and digital activities of all its structures. Hence, there is a system of pluses and minuses for the financial and credit sphere. A major advantage of the digital web transformation of conventional banking methods and approaches is that the consumer is connected with the client base and attached to the financial product. This trend is aimed at ensuring overall development of IT tools.

The Fourth Industrial Revolution and the introduction of the Industry 4.0 innova-tions create a unique opportunity for enterprises to increase productivity, opera-tional efficiency and rational use of resources, scale up successful pilot projects and acquire qualitatively new expertise for employees. The current stage of mod-ernisation of production can provide a significant impetus for economic growth and is a priority for governments and business leaders. Therefore, it is important to study project management strategies in the context of digital transformation in order to implement them in the enterprises of Kazakhstan. The purpose of the pa-per is to investigate the features and specifics of project management in modern conditions and search for methods of sustainable development of the state. The leading research method was the analysis, which made it possible to identify global trends in the field of project management for the implementation of innovations, as well as to assess the current state of enterprises in Kazakhstan after the Gov-ernment of the Republic of Kazakhstan approved the State Program "Digital Ka-zakhstan". As a result of the study, the features of project management for creat-ing a platform for the implementation of the Industry 4.0 for sustainable devel-opment of the state were identified. The authors concluded that the knowledge in-tensive business services need a deep understanding of user preferences and needs. Data-driven business models are the entry point for the digital transformation of enterprises.

Financial inclusion is a concept aimed at ensuring the accessibility of financial services and products to all segments of society, including vulnerable segments of population and rural remote areas. This process is of strategic importance for reducing poverty, improving quality of life and stimulating sustainable economic growth. The relevance of this topic remains despite the significant successes of financial organizations in terms of expanding their capabilities to provide financial services to various categories of clients. Digital technologies play a significant role in this process, thanks to which access to financial services has become more universal and convenient. The article discusses the key aspects of financial inclusion, the methodological basis for assessing the level of financial inclusion, and also analyzes the availability of financial products and services in the regions of Kazakhstan. The main goal of this study is to quantitatively assess the level of financial inclusion in different regions of Kazakhstan for a more detailed study of differences in the level of financial inclusion of territorial units. The study has used data from the National Bank of Kazakhstan and the Bureau of National Statistics. Methods of statistical analysis, quantitative analysis of indicators, including the approach of weighted values and data normalization, as well as cartographic analysis of the geographical accessibility of financial services (heat maps) were used. The results obtained make it possible to identify factors affecting the availability of financial products in the regions and offer recommendations for reducing territorial imbalances.

The integration of artificial intelligence (AI) and machine learning (ML) into financial risk management is accelerating globally and in Kazakhstan. These technologies enhance forecast accuracy and automate key processes, while simultaneously posing challenges for model transparency, decision ethics, and regulatory oversight. According to the National Bank of the Republic of Kazakhstan, about 31% of financial organizations are already using AI, while among second-tier banks the share of users reaches 60%. At the same time, only a small portion of organizations have integrated AI across all key business functions, indicating that most market participants are at an early stage of digital maturity [1]. This study presents a 2010–2025 systematic literature review conducted under PRISMA principles, combining international evidence with the Kazakhstani context of applying AI/ML to various categories of financial risk (credit, market, operational, fraud/AML). Bibliometric and thematic analyses indicate a sharp post-2015 increase in publications, diffusion of complex architectures (deep neural networks, ensemble methods), and rising attention to explainable AI (XAI). Contemporary ML algorithms deliver substantial improvements in the accuracy, speed, and reliability of risk forecasts relative to traditional approaches [2], while limitations in interpretability and production-level implementation persist. The operational robustness of solutions requires MLOps practices (version control, automated deployment of software and models into production environments, and model monitoring and validation). The scientific novelty of the article lies in the comprehensive systematization of AI methods by types of financial risks (credit, market, operational, fraud/AML) with consideration of XAI, as well as the development of a structured roadmap for AI implementation for banks and regulators. The practical significance lies in a set of specific recommendations for the development of data infrastructure, model risk management processes, XAI tools, and SupTech solutions designed for use by financial organizations and supervisory authorities in Kazakhstan.