title: Ð¡ÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð²ÑÐ±Ð¾ÑÐ¾ÑÐ½ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² Ð³Ð¾ÑÑÐ´Ð°ÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¼ Ð°ÑÐ´Ð¸ÑÐµ: ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¸ÑÐµÑÐºÐ¸Ðµ Ð°ÑÐ¿ÐµÐºÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Statistical sampling studies in public audit: theoretical and methodological aspects
Creators: name: ÐÐ°Ð½ Ð¦Ð·Ð¸Ð½Ñ Ð°Ð½Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ Ð²ÑÐ±Ð¾ÑÐºÐ°
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸
neural networks
statistical sampling
artificial intelligence
regression algorithms

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description: The purpose of the research is promoting optimization and innovation of statistical sampling methods in government auditing. Tasks of the work: - In-depth study of the theoretical foundations of statistical sampling. -Analysis of traditional and artificial statistical sampling methods in government audit. - Analyze the companyâs financial statements, collect and prepare data. - Cleaning and processing of received data for further analysis. - Selection of key features using principal component analysis. - Modeling neural networks. - Comparison and analysis of regression algorithms and neural network BP-lstm in auditing. - Evaluation of two algorithms. The research was based on research and includes international auditing standards, Russian auditing rules, and audit materials of the relevant companies. Research methods: statistical study of the selected industry, a dialectical and systematic approach to the research topic, manifested in the study of the application of statistical sampling methods in audit practice. The main results of the study: After completing the training of the BP-LSTM neural network model, the final result of our model was 88.4%, that is, our model was able to correctly predict the probability of financial fraud in JSC Russian Railways by 88.4%. After completing the prediction, the auditor needs to further check the data predicted by the neural network model to ensure that the audit was performed correctly. The practical significance of the work lies in the fact that the introduction of artificial intelligence and more effective risk management can significantly increase the efficiency of audits in the Russian audit service. Conclusions. In the context of the theoretical and methodological study of state audit samples, traditional and artificially intelligent audit methods are presented. Important conclusions are drawn about the current state and performance of JSCo âRussian Railwaysâ. Key trends and indicators are identified, the impact of various factors on the railways performance is assessed, and possible risks are identified. The introduction of artificial intelligence and more effective risk management can significantly improve the audit efficiency of the Russian audit service.