• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• The Journal of Information Systems
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• v.18 no.4
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• pp.41-57
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• 2009

Although financial information is a great influence upon determining of the group which use them, detection of management fraud and earning manipulation is a difficult task using normal audit procedures and corporate credit evaluation processes, due to the shortage of knowledge concerning the characteristics of management fraud, and the limitation of time and cost. These limitations suggest the need of systemic process for !he effective risk of earning manipulation for credit evaluators, external auditors, financial analysts, and regulators. Moot researches on management fraud have examined how various characteristics of the company's management features affect the occurrence of corporate fraud. This study examines financial characteristics of companies engaged in fraudulent financial reporting and suggests a model and system for detecting GAAP violations to improve reliability of accounting information and transparency of their management. Since the detection of management fraud has limited proven theory, this study used the detecting method of outlier(upper, and lower bound) financial ratio, as a real-field application. The strength of outlier detecting method is its use of easiness and understandability. In the suggested model, 14 variables of the 7 useful variable categories among the 76 financial ratio variables are examined through the distribution analysis as possible indicators of fraudulent financial statements accounts. The developed model from these variables show a 80.82% of hit ratio for the holdout sample. This model was developed as a financial outlier detecting system for a financial institution. External auditors, financial analysts, regulators, and other users of financial statements might use this model to pre-screen potential earnings manipulators in the credit evaluation system. Especially, this model will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings and to improve the quality of financial statements.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.75-84
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• 2020

The study investigates and measures the impact of financial restructuring on overall financial performance of commercial banks in Vietnam. Survey data for this research were collected from audited financial reports of 28 commercial banks in Vietnam, for the period from 2008 to 2018. In the study, we have built a model of econometric regression with the dependent variable being financial results measured through ROA and ROE. The research methods used include Pooled Ordinary Least Square Model (POLS), Fixed Effects Model (FEM), Random Effects Model (REM), and different Generalized Method of Moments (GMM). The results showed that the account payables restructuring and owners' equity restructuring are much needed. Increasing the owners' equity, decreasing the account payables would improve the overall financial performance, bad debts restructuring to decrease bad debts would also improve the financial performance as well. However, the financial restructuring in the period 2012-2015 and 2016-2018, indeed worsen the financial performance during those times. The findings of this study suggest that the evaluation of the financial performance after restructuring of commercial banks in Vietnam must be based on longer data. At the same time, it is necessary to examine differences between various banking groups to draw accurate conclusions on financial performance.

• Journal of Families and Better Life
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• v.11 no.2
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• pp.195-207
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• 1993

This study was performed to analyze a path model that investigated the relations among financial statements financial management and financial satisfaction of urban households. For this purpose conceptual framework based on the system theory was suggested. Empirical test's results showed some evidence has been provided that supported that conceptual frame-work. The level of living and gap between the level and standard of living measure were vital in the model investigating financial management and financial satisfaction, There were statistically significant direct effects of them on financial management however there were many indirect effects that mediate through them as well Also The financial management was accepted in the model investigating financial satisfaction. There was statistically significant direct effect on it and indirect effect that mediated through the financial management.

• Journal of Families and Better Life
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• v.20 no.1
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• pp.125-138
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• 2002

In this paper, we discuss issues related with conceptual definition and measurement of financial management behavior. Specifically, we examine the relationships among the management behavior relating to throughput, inputs, and outputs, based on the financial management systems model. Fer the analysis, we have reviewed articles that dealt with financial management behavior utilizing financial management systems model. The articles we reviewed appeared between the year 1986 to 2000, in four journals published in Korea (Journal of Korean Home Economics Association, Journal of Korean Home Management Association, Journal of Consumer Studies, and Journal of Korean Family Resource Management) as well as five home economics journals published in the U.S. The implications for the future research of financial management are also Presented.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.751-759
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• 2020

This study examines the impact of mergers on the financial performance of the Jordanian public shareholding companies. The study employs data collected for a sample of 10 Jordanian non-financial public firms that were engaged in legal horizontal merger deals between 2000 and 2013. The data was collected from the published annual financial reports of the merging companies and comparative companies for three years before the merger and three years after the merger. Event study methodology was applied to examine the data. Four measures of financial performance (FP) were used, which are return on assets (ROA), return on equity (ROE), earnings per share (EPS), and net profit margin (NPM). Two methods were used in the analysis - the change model and the intercept model using financial performance raw data and industry-adjusted data. The findings in general showed no significant impact of mergers on the financial performance of merging firms using the change model. However, by using the intercept model, significant impact of mergers on the financial performance was found on the sample of the study. The significant impact was found for mergers on the raw ROE of the merging firms, and on the ROA and NPM of the industry-adjusted firms.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.382-388
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• 2011

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.

• Korea Journal of Hospital Management
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• v.16 no.3
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• pp.57-74
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• 2011

The purpose of this study is to analyze the relationship between the BSC model's non-financial factors such as learning and growth, internal process, customer and financial factor in general hospitals. To achieve research purpose, the data were collected from 293 employees of 5 hospitals using a standardized questionnaires which were constructed to include BSC model, and applied the structural equation modeling to examine the relationship between non-financial and financial factor. The results show that the learning and growth factor of the model has positive effects of the internal process and customer factor. The internal process and customer factor are strongly related to financial factor. Hospitals have to know non-financial factor which has positively relate to financial factor. Therefore, the results of this study help to enhance the health care center to become aligned and focused on implementing the long-term competitive strategy. This study proposes an effective performance indicators for general hospitals and it is expected to be likely to have positive influence upon enhancing services of general hospitals.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.1-12
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• 2021

Altman's Z-score is used to measure a company's financial health and to predict the probability that a company will collapse within 2 years. It is proven to be very accurate to forecast bankruptcy in a wide variety of contexts and markets. The goal of this study is to use Altman's Z-score model to forecast insolvency in non-financial publicly traded enterprises. Non-financial firms are a significant industry in Malaysia, and current trends of consolidation and long-term government subsidies make assessing the financial health of such businesses critical not just for the owners, but also for other stakeholders. The sample of this study includes 84 listed companies in the Kuala Lumpur Stock Exchange. Of the 84 companies, 52 are considered high risk, and 32 are considered low-risk companies. Secondary data for the analysis was gathered from chosen companies' financial reports. The findings of this study show that the Altman model may be used to forecast a company's financial collapse. It dispelled any reservations about the model's legitimacy and the utility of applying it to predict the likelihood of bankruptcy in a company. The findings of this study have significant consequences for investors, creditors, and corporate management. Portfolio managers may make better selections by not investing in companies that have proved to be in danger of failing if they understand the variables that contribute to corporate distress.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.