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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">turan</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник университета «Туран»</journal-title><trans-title-group xml:lang="en"><trans-title>Bulletin of "Turan" University</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1562-2959</issn><issn pub-type="epub">2959-1236</issn><publisher><publisher-name>Университет «Туран»</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.46914/1562-2959-2025-1-2-222-233</article-id><article-id custom-type="elpub" pub-id-type="custom">turan-4695</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЭКОНОМИКА: ИСТОРИЯ, ТЕОРИЯ, ПРАКТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ECONOMY: HISTORY, THEORY, PRACTICE</subject></subj-group></article-categories><title-group><article-title>Методы разработки экспертных систем в корпоративных информационных системах: повышение экономической устойчивости предприятия</article-title><trans-title-group xml:lang="en"><trans-title>Methods for developing expert systems in corporate information systems: enhancing enterprise economic stability</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0006-8922-8627</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мауленов</surname><given-names>Т. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Maulenov</surname><given-names>T. N.</given-names></name></name-alternatives><bio xml:lang="ru"><p>м.т.н.</p><p>Алматы</p></bio><bio xml:lang="en"><p>m.t.s.</p><p>Almaty</p></bio><email xlink:type="simple">23241509@turan-edu.kz</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0007-7537-0339</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мауленов</surname><given-names>Н. О.</given-names></name><name name-style="western" xml:lang="en"><surname>Мaulenov</surname><given-names>N. O.</given-names></name></name-alternatives><bio xml:lang="ru"><p>к.т.н., доцент.</p><p>Алматы</p></bio><bio xml:lang="en"><p>c.t.s., associate professor.</p><p>Almaty</p></bio><email xlink:type="simple">maulenov74@mail.ru</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0000-5053-8667</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Даушебаев</surname><given-names>А. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Daushebayev</surname><given-names>A. B.</given-names></name></name-alternatives><bio xml:lang="ru"><p>м.т.н., преподаватель.</p><p>Алматы</p></bio><bio xml:lang="en"><p>m.t.s., teacher.</p><p>Almaty</p></bio><email xlink:type="simple">d_almaz@inbox.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Университет «Туран»<country>Казахстан</country></aff><aff xml:lang="en">Turan University<country>Kazakhstan</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru">Q University<country>Казахстан</country></aff><aff xml:lang="en">Q University<country>Kazakhstan</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>29</day><month>06</month><year>2025</year></pub-date><volume>0</volume><issue>2</issue><fpage>222</fpage><lpage>233</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Мауленов Т.Н., Мауленов Н.О., Даушебаев А.Б., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Мауленов Т.Н., Мауленов Н.О., Даушебаев А.Б.</copyright-holder><copyright-holder xml:lang="en">Maulenov T.N., Мaulenov N.O., Daushebayev A.B.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vestnik.turan-edu.kz/jour/article/view/4695">https://vestnik.turan-edu.kz/jour/article/view/4695</self-uri><abstract><p>Данное исследование направлено на углубленный анализ влияния экспертных систем, интегрированных в корпоративные информационные системы, на экономическую устойчивость предприятий. В условиях современной бизнес-среды роль экспертных систем значительно возрастает, так как они служат ценным инструментом для стратегического принятия решений, управления рисками и повышения операционной эффективности. В статье рассматривается применение современных методов, таких как нейронные сети, нечеткая логика, коллективный интеллект и байесовские сети, причем эффективность и ограничения каждого из них оцениваются посредством сравнительного анализа. В исследовании использовались инструменты машинного обучения, разработанные на современных языках программирования Python и R, при этом библиотеки scikit-learn и TensorFlow показали наилучшие результаты. Эффективность систем оценивалась с использованием количественных показателей на основе мнений отраслевых экспертов и данных реальных предприятий. Авторы отмечают, что внедрение экспертных систем доступно не только для крупных компаний, но и для малого и среднего бизнеса. Это исследование можно рассматривать как вклад в региональные и международные научные работы по изучению синергии КИС и ЭС. Результаты показывают, что экспертные системы оказывают существенное положительное влияние на финансовые показатели предприятий: текущая ликвидность увеличивается на 20%, чистая прибыль – на 23%, затраты снижаются на 12%, а долговая нагрузка уменьшается на 25%. Кроме того, в статье обсуждаются особенности и возможности внедрения экспертных систем в условиях Казахстана, предлагаются практические рекомендации по ускорению цифровой трансформации. Также подчеркивается важность формирования культуры управления на основе данных в рамках цифровой экосистемы.</p></abstract><trans-abstract xml:lang="en"><p>This study aims to provide an in-depth analysis of the impact of expert systems integrated into corporate information systems on the economic stability of enterprises. In the modern business environment, the role of expert systems is significantly increasing, as they serve as valuable tools for strategic decision-making, risk management, and improving operational efficiency. This article explores the use of modern methods such as artificial neural networks, fuzzy logic, swarm intelligence, and Bayesian networks, with each method’s effectiveness and limitations evaluated through comparative analysis. The research employed machine learning tools developed in modern programming languages like Python and R, with libraries such as scikit-learn and TensorFlow showing notable results. In addition, the efficiency of the systems was assessed using specific metrics based on expert opinions and real enterprise data. The authors emphasize that the implementation of expert systems is accessible not only to large enterprises but also to small and medium-sized businesses. This study can be seen as a contribution to regional and international scientific work exploring the synergy between CIS and ES. The findings show that expert systems have a significantly positive impact on companies' financial indicators, including a 20% increase in current liquidity, a 23% rise in net profit, a 12% reduction in expenses, and a 25% decrease in debt load. Furthermore, the article discusses the specific features and opportunities of implementing expert systems in the context of Kazakhstan, offering practical recommendations for accelerating the digital transformation process. The study also highlights the importance of building a data-driven management culture within the digital ecosystem.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>экспертные системы</kwd><kwd>корпоративные информационные системы</kwd><kwd>экономическая устойчивость</kwd><kwd>искусственный интеллект</kwd><kwd>нейронные сети</kwd><kwd>нечеткая логика</kwd><kwd>коллективный интеллект</kwd><kwd>байесовские сети</kwd></kwd-group><kwd-group xml:lang="en"><kwd>expert systems</kwd><kwd>corporate information systems</kwd><kwd>economic stability</kwd><kwd>artificial intelligence</kwd><kwd>neural networks</kwd><kwd>fuzzy logic</kwd><kwd>swarm intelligence</kwd><kwd>Bayesian networks</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Jackson P. Introduction to Expert Systems. 3rd ed. Addison-Wesley, 1999. P. 455–458.</mixed-citation><mixed-citation xml:lang="en">Jackson P. 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