Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

As supply chains (SCs) become more complex globally, businesses are looking for efficient business analytics (BA), business intelligence (BI), and artificial intelligence (AI) tools for managing supply-chain risk. The tools and methodologies proposed by the supply-chain risk management (SCRM) literature are mostly based on experts’ judgments, their knowledge, and past data. The expert evaluation-based approach could be partly or fully replaced by AI solutions, increasing objectivity, impartiality, and impersonality, reducing sources of human mistakes, biases, and inefficiencies in SCRM. However, the transition from BA to AI in SCRM is not a self-contained process; though attractive as a vision, it is not straightforward as a management or implementation process. The purpose of this research is to explore and define the conceptual grounds for transitioning from BA to AI in SCRM. The conceptual SCRM structure, its AI suitability, and implementation terms are defined theoretically based on a literature review. A single, in-depth business case study is employed to explore the theoretically defined terms of AI-based SCRM implementation. The proposed conceptual AI-suitable SCRM structure is defined by five principal building blocks: risk events, risk-event indicators, data-processing rules and algorithms, analytical techniques, and risk event probability forecasts. The study concludes that the business environment meets AI-based SCRM-implementation terms of data existence and access. Since data on risk events and negative outcomes are limited for machine learning, experts’ experience and knowledge might be utilised to build initial rules and data-processing algorithms for AI.

The importance of access to finance as a significant problem for business development is considered one of the measures representing the level of financial inclusion. Developing financial innovations, such as crowdfunding, mobile payments, AI-based credit scoring systems, and even blockchain technologies has an immense potential to increase financial inclusion, increase access to finance for individuals and businesses, enabling the unscored or unbanked population to become active members of financial markets. Studies have shown the positive impact of financial technology on individuals' financial inclusion and access to finance, but the development of the Fintech phenomenon does not itself imply improved access to finance for businesses. The spread and adoption of Fintech are taken as evidence of financial innovation This research aims to explore the expected interaction between financial innovation and financial inclusion in selected European countries. The data from the Global Fintech Ranking and Survey on the access to finance of enterprises (SAFE) is used in this study, examining them by comparing countries by their Fintech rankings and respective rank position obtained by measuring the importance of access to finance as a problem in business development. The analysis of 29 European countries indicates that theoretically defined expectations are met in most cases: access to finance is not an important problem for the companies from the countries that are ranked higher in the Fintech rating report. But part of the sampled countries deviates from this trend. These findings indicate that besides financial innovation other factors also may determine the financial inclusion of the companies.

When evaluating the competitive environment in the context of the pandemic, the pandemic’s global scale must be considered, since all products related to COVID-19 management have no regional competition but rather immediately switch to global competition and compete with similar products or substitutes. It should be noted that no products intended for managing the pandemic were available before its outburst. Even with the onset of the pandemic, not many specialized products, except for some general protection measures, were developed. However, in the two years after the pandemic began, competitive products intended for managing COVID-19 emerged into the market. The global scale of the pandemic automatically created worldwide demand for such products, which leads to a global shift in competitiveness. As a result, the competitive environment for products intended to manage COVID-19 is changing. Research purpose: To evaluate the competitive products—modern and robotized indoor disinfection equipment—available in the market and those under development. Research objectives: To develop a methodology for evaluating the competitive products and to select evaluation criteria and methods; to evaluate the competitive products in terms of their functionality and technical characteristics; and to research the channels and methods employed to launch competitive products into the market and provide analysis of best practice.

This paper contributes to the existent literature and the current discussions on regulatory changes towards bank exposure to interest rate risk in the banking book (IRRBB) aiming to provide the model on the computation of earning based gap analysis under unconditional cash flow for the European Bank Authority’s (EBA’s) category 4 banks (i. e. small non-complex domestic financial institutions). The problem, discussed in this paper, arises because the Final Standards issued by the Basel Committee on Banking Supervision do not determine the level of sophistication of the IRRBB measurement techniques. There are different explanations of consultants, some surveys, and recommendations, but no suggestions on the particular modeling towards regulation in IRRBB have been found. Another issue addressed here is the uneven capacity of creating risk assessment models of large international and small domestic financial insti-tutions due to the difference in human resources. We first discuss recent changes of regulation on interest rates in the banking book and the background of these changes. We then develop a methodology of the model for assessment of earning-based gap analysis under unconditional cash flows for the 4th category of banks (small, local banks). In addi-tion, the model with one of the Baltic domestic commercial bank’s simulated data is tested.