http://bijournal.hse.ru en-us Copyright 2024 Wed, 19 Jun 2024 10:27:59 +0300 https://bijournal.hse.ru/en/2024--2 Vol 18/934275719.html       The article is devoted to issues of controlling the operational risks of a credit institution associated with the actions of personnel. Operational risk control is an important aspect of a credit institution’s business. Despite the fact that the Bank of Russia in regulatory documents described in detail the set of actions that banks should take to control operational risks, in practice credit institutions experience serious difficulties in dealing with operational risk associated with the actions of personnel. This may be explained, first, by the difficulty of identifying and formalizing the specified risk. One of the main sources of operational risks associated with personnel actions is employees’ lack of qualifications. This can lead to reduced availability and quality of services provided by credit institutions, as well as possible financial and reputational losses. The purpose of the research conducted by the authors is to improve the system of control of operational risks in a credit institution using artificial intelligence technologies, including the development of tools for assessing in an automated mode the level of criticality of the influence of personnel competence on the occurrence of operational risk events. To achieve this goal, an artificial neural network (ANN) was developed using the high-level Keras library in Python. This paper defines a set of key indicators that have the most significant impact on the possibility of operational risk associated with the actions of the personnel in a credit institution. The article presents the results of checking the generated sets of training and test data using application software packages that implement mathematical methods to assess the consistency of the generated data sets. The paper presents graphs showing the results of training and testing of the artificial neural network that has been constructed. The results obtained are new and may allow credit institutions to significantly increase the efficiency of their work by digitalizing the solution of tasks to control the level of operational risk associated with the actions of personnel. https://bijournal.hse.ru/en/2024--2 Vol 18/934278001.html       Modern product retrieval systems are becoming increasingly complex due to the use of extra product representations, such as user behavior, language semantics and product images. However, adding new information and complicating machine learning models does not necessarily lead to an improvement in online and business search performance, since after retrieval the product list is ranked, which introduces its own bias. Nevertheless, the business performance of a product search will be worse from ranking an incomplete list of products than a complete one, and the relevance of search results will not improve from perfect sorting of products that do not match the search query. Therefore, the main quality indicators for the products retrieval phase remain Recall and Precision at the k threshold. This paper compares several architectures of product retrieval systems in product search for e-commerce. To do this, the concepts of threshold Recall and Precision for information retrieval are investigated and the dependence of these measures on the order of issuance is revealed. An automatic procedure has been developed for calculating R@k and P@k, which allows us to compare the effectiveness of information retrieval systems. The proposed automatic procedure has been tested on the WANDS public dataset for several key architectures. The obtained values R@1000 = 84% ± 9% and P@10 = 67% ± 17% are at the level of SOTA models. https://bijournal.hse.ru/en/2024--2 Vol 18/934278564.html       In the modern economy, the success of a business is largely determined by the company’s ability to analyze consumer preferences, consumer attitudes towards the company’s products, as well as the ability to quickly respond to changing preferences or negative trends. Social listening is a technology for analyzing conversations, text messages and any kind of mention of a company, its products or brand. Currently, it is most effective to carry out social listening by monitoring social networks (VKontakte, etc.), which are the largest sources of text messages from millions of users. The purpose of this work is to analyze the practices of using social listening technology, as well as common approaches to the use of social networks by domestic and foreign companies. Based on the specialized software developed by the authors, an analysis of more than 50 000 news reports published in 2021–2024 was carried out on companies of different levels and specialization. Using linguistic analysis of the corpus of text messages for various companies and sectors of the economy, the most common words were identified, thematic modeling was carried out, and the dynamics of news reports and their relationship with external factors were studied. https://bijournal.hse.ru/en/2024--2 Vol 18/934287225.html       The management of urban areas requires the development of an effective strategy for the evolution of transportation infrastructure to ensure the smooth flow of traffic and pedestrians. A crucial component of this infrastructure is the traffic light system, which plays a vital role in traffic control and traffic safety. Improving the efficiency of traffic control systems in intelligent transportation systems (ITS) has a significant impact on a city’s economy. As a result, the cost of fuel for road users can be reduced, and their level of social comfort can be improved, among other benefits. This paper proposes a novel approach to optimizing traffic flows in smart cities, based on the combined use of the genetic optimization algorithm and the ITS simulation model we developed. The proposed method aims to enhance the efficiency of existing traffic control systems and achieve optimal traffic flow patterns, thereby contributing to a more sustainable and efficient urban environment. The optimization algorithm shown here aggregates the objective functions using a simulation model of a real region of the Moscow road network. The model includes intersections, pedestrian crossings and other features that are implemented in the AnyLogic system. The research aims to create a decision-support system for managing urban transport infrastructure. This system will be used to optimize the duration of traffic light phases in order to minimize the time vehicles spend passing through key nodes in the urban road network. It will also optimize pedestrian flow, reducing the impact of traffic on the environment and improving fuel efficiency. By applying this approach, the capacity of the street network can be significantly increased. Additionally, the negative effects of traffic flow on the environment can be reduced by optimizing fuel use and reducing waiting times at intersections managed by traffic lights. The research methodology involves the development of a hybrid evolutionary search algorithm, the creation of a simulation model for transportation and pedestrian flows in the AnyLogic and a series of optimization experiments that demonstrate the effectiveness of the proposed approach when applied to the modeling of complex urban transportation systems. https://bijournal.hse.ru/en/2024--2 Vol 18/934290064.html       This article is devoted to the problem of theoretical and information support for decision-making in strategic management of raw material procurement processes. The study is timely, because there is currently significant volatility in prices for raw materials. This poses very difficult challenges for managers. Finding solutions is one of the most important areas of business informatics. This article discusses a procurement strategy in two stages: at the beginning and middle of the month. The price of raw materials is known only at the beginning of the month. Price is a continuous random variable. You can predict only the interval of its change. Here the interval is directly used to determine the purchase volume at a known price. The authors derived a functional dependence of the maximum risk according to Savage on the amount of purchased raw materials at the beginning of the month. As a result, it was possible to establish the amount of raw materials to be purchased at the beginning of the month to reduce maximum risk to a minimum. Using the example of corn purchases, we carried out a comparative analysis of possible methods for determining these intervals based on an analysis of price time series. The findings are useful for managers of processing enterprises. This work is the first to solve the problem of minimizing the maximum risk when purchasing raw materials. https://bijournal.hse.ru/en/2024--2 Vol 18/934298815.html       Bibliometric analysis is a widely used technique for investigating and studying scientific information. There is no previous research that explains bibliometric analysis related to the adoption of big data analytics in auditing. Thus, this research will fill the gap in previous research to examine bibliometric analysis related to the adoption of big data analytics in auditing. This paper employs bibliometric analysis on Scopus-indexed journals to examine the topic of big data analytics in audits, utilizing the VOSviewer tool. The objective of utilizing bibliometric analysis in this research is to ascertain the progression of articles concerning the application of big data analytics in the field of auditing. This article discusses the development of the number of publications and citations, the trend of publication researchers, the country of publication articles, the relationship between researchers, and the relationship between words with the topic of big data analytics in the period 2010–2022. This research reveals areas of application of big data analysis adoption in auditing. Qualitative research, especially library research, is the best method widely used among writers. This study provides several useful insights into the meaning of big data and data analysis, the benefits of using big data analysis in the audit process, and how the audit process can be made easier with big data analysis. Among the most interesting insights, the results suggest that big data implies vast amounts of data that exceed the limits of what can be stored and processed. Thus, the use of data analytics helps auditors reduce cognitive errors arising from large and diverse data sets. This bibliometric research presents the number of articles and citations of research publications, which authors and countries have the most research on this topic, and the keywords/terminologies that appear most frequently as well as the meaning of these keywords/terminologies.