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Traditional monetary unit sampling (MUS) in financial auditing leaves detection risk close to 38 per cent while examining only one fifth of the account population, a structural limitation that magnifies the agency problem between public-sector entities and citizens. This study formalizes detection risk as DR = 1 - Recall, bridging machine-learning classifier performance with the ISA 200 audit assurance standard, and proposes a four-layer agentic artificial-intelligence (AI) framework that orchestrates the entire audit workflow without human intervention between successive layers. The framework was applied to 9,907 account-year observations and 3,329,189 individual journal entries (total volume MNT 16.34 trillion) drawn from the general ledger of a large Mongolian state-owned thermal power utility for the fiscal years 2023 to 2025 and was further generalized to seven additional public-sector entities (n = 7,021 accounts). At the account level the ensemble Random-Forest agent reached F1 = 0.978 and reduced detection risk to 2.01 per cent, a 19-fold improvement over MUS. At the transaction level the 15-criterion ensemble flagged 166,459 anomalous entries (5.00 per cent), of which 2,885 constituted very high-risk records. A novel aggregation-masking effect of Benford’s law is reported: 1,247 accounts that appeared statistically normal at the account level concealed clusters of anomalous transactions, a finding only retrievable through dual-level analysis. SHAP-based feature attribution was mapped to a feature-to-ISA ontology (ISA 240, 315, 505, 520) that enabled the autonomous generation of ISA-compliant working-paper text. Six independent validation procedures, including a temporal hold-out, a seven-sector test, and triangulation with two qualified opinions issued by the National Audit Office of Mongolia, confirmed robustness. An open-source web prototype (Audit AI v10.0) demonstrates zero-cost deployability. The study contributes to the broader scientific-culture debate on the ethics, accountability and global human values that should govern the transfer of algorithmic decision-making into public-sector assurance.
This study aims to analyze a company’s sales data using data mining techniques to uncover patterns in consumer behavior and product sales trends. Clustering methods (K-Means and K-Nearest Neighbors) and association rules were applied using IBM SPSS Modeler 18.6. The performance and effectiveness of these methods were compared to evaluating their utility in sales data analysis. Clustering results demonstrated the potential to segment consumers based on shared characteristics, thereby supporting data-driven marketing strategy development. Association rule analysis revealed product combinations that are frequently purchased together, offering additional insights to enhance sales strategies. The findings of the study highlight the value of data mining techniques in improving business decision-making and underscore the potential for incorporating larger datasets and more advanced machine learning methods in future analyses.
This research addresses the question of how artificial intelligence (AI) is being adopted in Mongolia’s auditing sector by integrating theoretical approaches with data-driven evidence. Through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM), the research evaluates how auditors’ digital skills, professional qualifications, and personal attributes affect their intention to employ AI in practice. The results highlight digital competence as the most decisive factor, whereas individual traits and expertise have little direct effect. The SWOT analysis points to both opportunities and barriers: while auditors are eager to adopt AI, the lack of training programs, underdeveloped infrastructure, and unclear legal environment slow down progress. This research makes both conceptual and practical contributions by suggesting ways for policymakers, industry leaders, and educational institutions to facilitate AI-enabled auditing in Mongolia.
The purpose of this study is to analyze the impact of key macroeconomic factors and the time-lag effects of Sustainable Development Goals (SDGs) on their implementation in Mongolia using modern machine learning (ML) techniques. The analysis employs annual data from 2000–2024, including macroeconomic indicators, the SDG Index, and performance metrics for 17 goals. Core variables include GDP growth, inflation, unemployment, exports, fiscal revenue, and government expenditure. In 2025, Mongolia’s SDG Index score reached 66.7, ranking 100th globally, which is close to the regional average but still reflects weak performance in environmental goals. To explain SDG Index fluctuations, four ML models - Linear Regression, Gradient Boosting, Support Vector Regression (SVR), and Artificial Neural Network (ANN) - were compared. Gradient Boosting achieved the highest accuracy (R² = 0.844, RMSE = 0.8735), explaining 84.4% of the variance. SHAP analysis revealed that the most influential factors for SDG performance are health (SDG3), exports, poverty reduction (SDG1), internet usage (SDG9), and food security (SDG2), while inflation and unemployment exert negative effects. Time-lag analysis shows that foundational service goals—SDG3 (health), SDG6 (water and sanitation), SDG11 (urban development), SDG9 (infrastructure), and SDG1 (poverty reduction)—have strong positive one-year lag effects on the SDG Index. In contrast, environmental goals (SDG12–15) exhibit persistent negative lag effects, indicating that ecosystem degradation slows SDG progress in subsequent years. SDG4 (education) and SDG7 (energy) display weak lag impacts, with long-term delayed effects. Additionally, K-Means clustering grouped SDG indicators into three development patterns: (i) economy-driven, (ii) social-service-driven, and (iii) environment-challenged clusters. Findings suggest that improving SDG implementation in Mongolia requires export diversification, investment in health services, inflation control, and strategic reforms in environmental policy.
The advancement of artificial intelligence (AI) is ushering in a new era in auditing by enhancing decision-making, fraud detection, and risk assessment processes. This study presents a systematic literature review of 30 prominent academic works published between 2017 and 2024. Findings suggest that while AI can significantly improve audit efficiency and accuracy, several challenges remain—such as data security, professional ethics, human factors, and regulatory compliance. The study concludes that a collaborative model, where AI augments rather than replaces auditors, offers a more feasible and ethical approach to AI integration in auditing practices.
Энэхүү судалгаанд өгөгдөл олборлолтын аргуудыг ашиглан компанийн борлуулалтын өгөгдөлд дүн шинжилгээ хийж, хэрэглэгчдийн зан төлөв, бүтээгдэхүүний борлуулалтын хандлагыг илрүүлэх зорилго тавив. Судалгааны хүрээнд IBM SPSS Modeler програмыг ашиглан кластерчлалын (K-Means, K-Nearest Neighbors) болон холбоо хамаарлын дүрмийн шинжилгээ (Market Basket Analysis) хийсэн бөгөөд тухайн аргуудын үр нөлөөг харьцуулан судлав. Кластерчлалын үр дүнд хэрэглэгчдийг ижил шинж чанараар бүлэглэх боломжтой болсон бөгөөд маркетингийн стратеги боловсруулахад чиглэсэн өгөгдөлд суурилсан шийдвэр гаргалтыг дэмжих суурь тавигдлаа. Мөн борлуулалтын өгөгдөлд хийсэн холбоо хамаарлын дүрмийн шинжилгээгээр хамт худалдагдах хандлагатай бүтээгдэхүүнүүдийг тодорхойлж, нэмэлт борлуулалтын боломжуудыг илрүүлэв. Судалгааны үр дүн нь өгөгдөлд суурилсан бизнесийн шийдвэр гаргалтыг боловсронгуй болгоход үнэтэй хувь нэмэр оруулах бөгөөд цаашид илүү өргөн хүрээтэй өгөгдөл болон дэвшилтэт машин сургалтын аргуудыг ашиглан гүнзгийрүүлэн судлах боломжтой юм.
Ил тод байдал, энгийн байдал, блокчэйн системийн ачаар криптовалют сүүлийн жилүүдэд олон нийтэд танигдах болсон. Энэхүү зах зээлд хөрөнгө оруулахаас өмнө түүний зах зээлийн мөн чанарыг шинжлэх нь маш чухал. Криптовалютын үүсэл хөгжил, ирээдүйн чиг хандлага, Монгол болон бусад орнууд дэх криптовалютын хэрэглээг харьцуулах, биткойн болон бусад койны хамаарлыг судалж дүгнэхэд энэхүү судалгааны ажлын гол зорилго оршино. Энэ зорилгоор сүүлийн таван жилийн биткойн болон дотоод гадаадын долоон алткойнуудын (Ethereum, Ripple, Solana, Matic, ARDX, IHC, OBOT) өдөр тутмын хаалтын үнийн мэдээлэлд үндэслэн, биткойн болон алткойнуудын хоорондын хамааралд дүн шинжилгээ хийлээ. Судалгааны үр дүнг харуулбал: (1) Йохансен ко-интеграцийн тест (Johansen Co-integration) ашиглан хамаарлыг шинжлээд сонгосон криптовалют хооронд урт хугацааны хамаарал байна гэж дүгнэлээ. (2) Корреляцийн шинжилгээгээр ETH, MATIC, XRP болон OBOT-ын хувьд өндөр хамааралтай байна. (3) VEC Granger Causality Test-ын үр дүн нь BTC нь SOLANA, OBOT болон IHC-г үүсгэдэг, харин XRP, MATIC, ETH болон ARDX үүсгэдэггүй болохыг харуулсан. Биткойн нь алткойны үнийн хөдөлгөөнд чухал үүрэг гүйцэтгэдэг гэж энэхүү ажлаар нэгтгэн дүгнэсэн болно.
2017 оноос эхлэн Монгол улсад үнэ шилжилтийн ойлголт дэлгэрч Эдийн засгийн хамтын ажиллагаа, хөгжлийн байгууллага (ЭЗХАХБ)-ын “Үндэстэн дамнасан аж ахуйн нэгжүүд болон Татварын албадад зориулсан ЭЗХАХБ-ын Үнэ шилжилтийн талаарх зөвлөмж”-н шинэчлэгдсэн найруулгын дагуу үнэ шилжилтийн стандартыг дотоод хуулинд авч хэрэгжүүлж байна. Харин 2020 оноос эхлэн Монголд аж ахуйн нэгж (ААН)-үүд үнэ шилжилтийн тайлан гаргах шаардлагатай болсон. Үүнтэй холбоотойгоор үнэ шилжилт хийсэн аж ахуйн нэгжүүдийн санхүүгийн үр дүнтэй холбоотойгоор уул уурхайн компаниудын үнэ шилжилттэй холбоотой асуудлыг судалж, үнэ шилжүүлгэд тулгарч буй бэрхшээлүүдийг шийдвэрлэх боломж, эдийн засагт үзүүлж буй нөлөөллийг тооцоолж дүгнэлт гарган судаллаа.
ABSTRACT The tax environment is a critical factor for the business and economy. Mongolia is a developing Asian country that hugely depends on the mining sector. Therefore, the Mongolian Government strives to diversify its economy and make some efforts to improve to attract investors and enhance the growth of the economy. In recent years, Mongolia’s Government made radical tax reforms. The main goal of this research is to evaluate the tax environment and then tax reform and its impact on the economy in Mongolia’s case. Exposing some problems that should be regulated with the tax policy is important for the business environment. Therefore, this study evaluates the main indicators of the tax environment in support of business along with tax policy. KEYWORDS taxation, tax effect, level of tax burden, business environment, domestic manufacturers, tax reform
This paper examines the survey results on e-learning implementation, which covered 726 lecturers from Mongolian universities and colleges. The authors determined factors that influence e-learning course classification. We applied methods such as regression analysis and factor analysis. It revealed that institutional factors such as ownership, LMS, size, and personal factors such as age, gender, prior training, team, field of science, qualification, and locations influence the tendency to develop e-learning courses. The study is beneficial for policymakers and practitioners by broadening the understanding of institutional and personal factors influencing e-learning course development. Implications for practice and policy: 1) Mongolian higher education institutions primarily practice web-facilitated courses with few blended and online courses. 2) Institutional factors such as ownership, LMS, size, and personal factors such as age, gender, prior training, team, field of science, qualification, and locations influence the tendency to develop e-learning courses. 3) There is a strong need for faculty development as every third lecturer does not know about the learning theories and does not apply them to the e-learning course development. 4) The higher education management should address challenges faced by the faculty members. 72.65% of respondents raised environment and faculty development challenges.
The traditional lending service is being challenged by the fast pace of financial technology ('Fintech') development. In response to the digitalization, the incumbent banks need to develop platform-based online lending services in order to meet the customers' demands and to keep the competitive advantages. In Mongolia, the development of online lending is still in its infancy. Currently, there is no complete online lending service in Mongolian banks due to some unfavorable ecosystem, technology challenges, regulatory restrictions, and market flaws. Physical interaction is required somehow. A significant gap has to be filled, and much can be adapted and improved upon from international experience and expertise. This study aims to comprehend the emerging online lending platforms and how banks are transforming platform-oriented lending models by integrating with new financial technologies. Furthermore, the research will also figure out the challenges and opportunities in the implementation by analyzing a case study based on the primary data focused on expert interviews, observations, and discussion with specialists in one of the leading bank in Taiwan, literature studies, and secondary data collected. The research findings enable us to understand the critical elements of online lending business model in banks and how they play an essential role in building an efficient model. Based on the research key findings, further recommendations will be given to the Mongolian banks for introduction and future development of online lending business.
This paper aims to identify the critical cost drivers in the past project data and prediction for a software cost estimate with the help of data mining tools Weka. Cost drivers are multiplicative factors that determine the effort required to complete our software project. We used data mining tools Weka to identify the essential and standard cost drivers used to generate the estimate of a project.
TAX EFFECT ON ATTRACTIVENESS OF BUSINESS ENVIRONMENT ABSTRACT The main goal of the present survey lies in studying impact and making evaluation of the main indicators of the tax environment on support of business along with exposing some problems that should be regulated with the tax policy. The survey findings show that: when suitability of tax rates of 7 types of main taxes followed presently in the country to the current business environment was studied, a conclusion was made that tax rates were in appropriate and a change was necessary; when types of taxes and their burden, tax payment and reporting, tax privileges, duplication of taxes, tax equity and fairness indicators were studied for their suitability, all indicators except tax payment and reporting did not make a positive impact on the business environment; when formation of a favorable tax environment to support the national industry was examined, a conclusion was made about lack of such environment. When factors determining such situation were investigated in detail, a regression equation showed impact of other indicators excluding the Tax payment and reporting indicator; when compatibility of the Tax Law with the business environment and its impact was studied, a conclusion was made about its incompatibility with the present business environment. A multifactor regression assessment to study impact of tax indicators and tax rates was made.
ABSTRACT The primary goal of this study is to assess practices and services provided by tax agencies from an ethical standpoint, to research and evaluate the impact of the ethical problems on supporting businesses, to compare the findings with practices in other public administration agencies and to identify areas for improvement. The findings of the study indicate that: (1) Regarding the suitability of tax services for business environment, it currently looks not very supportive for business entities although it does appear to be average or slightly above average. (2) Six indicators of tax service ethics are carefully examined: knowledge and skills of tax staff; their interpersonal skills and ethics; consistency of rules on corruption and bribery within tax agencies; bureaucracy; and relationships among departments and units. When the data are combined and represented at the national level, in UB city and local population, the findings indicate that tax agencies demonstrate indicators of moderate ethical problems when compared to those of other government bodies across the country. (3) As the impact assessment of the above six indicators implies, interpersonal skills and inter-departmental relationship indicators in tax agencies in UB, knowledge and skills, interpersonal skills, and ethics indicators in the case of local tax agencies demonstrate having impact as the regressive equation suggests. (4) When ethics in tax agencies is thoroughly examined in terms of corruption, bribery and bureaucracy and their nationwide, UB city and local population aggregate distribution is presented, no indicators of corruption and bribery affecting the operations and services of tax agencies are observed; however, as regression equation recommends, a bureaucratic impact is detected.
ABSTRACT The main goal of the present survey lies in studying impact and making evaluation of the main indicators of the tax environment on support of business along with exposing some problems that should be regulated with the tax policy. The survey findings show that: when suitability of tax rates of 7 types of main taxes followed presently in the country to the current business environment was studied, a conclusion was made that tax rates were in appropriate and a change was necessary; when types of taxes and their burden, tax payment and reporting, tax privileges, duplication of taxes, tax equity and fairness indicators were studied for their suitability, all indicators except tax payment and reporting did not make a positive impact on the business environment; when formation of a favorable tax environment to support the national industry was examined, a conclusion was made about lack of such environment.
Abstract Regardless of the size of the organization operations, it collects a large amount of information such as market, consumer, sales, finance and human resources. Therefore, it is important to analyze this information and use it to make informed decisions. The purpose of this study was to compare the suitability of a suitable sales analysis system for the company's operations in order to address the challenges of Company X's current sales data processing process, and to identify solutions. The result is a two-tier system for preparing an organization's sales report. The MS Excel Dashboard is suitable for managers, while the Business Intelligence (BI) system is suitable for managers, and the MS Power BI system is the most suitable of the BI systems.
The study focused on the outcomes of the online courses developed consistent with the requirements of educational system in Mongolia, including the National University of Mongolia and tailored to the needs of lifelong education for the public and students. The results of the survey are as follows: 1) In order to develop e-learning courses meeting the students’ needs, the Open edX system which is widely used in the world's educational system and which is also compatible with other systems was chosen as the comprehensive system to develop the e-courses; 2) The courses of 4 master's degree programs including Business Information Systems and Business English were developed by the team of developers consisting of content developer specialist teachers, e-content developer experts and system developers who placed the e-course contents in the edX systems and showed that developing online course is a result of collaborative work; 3) The flexibility of the training has been enhanced and improved after testing the contents of the online courses over the last semester. 4) The needs for improving regulations of delivering online courses are addressed and reported to the related authorities in order to reach broader audience.
