scholarly journals An optimized deep neural network-based financial statement fraud detection in text mining

2021 ◽  
Vol 10 (4) ◽  
pp. 77-105
Author(s):  
Ajit Kr. Singh Yadav ◽  
Marpe Sora
Keyword(s):  
Neural Network ◽  
Text Mining ◽  
Deep Neural Network ◽  
Fraud Detection ◽  
Financial Statement ◽  
Financial Statement Fraud

Financial Statement Fraud Detection and Investigation in Digital Environment

2020 ◽  
pp. 187-214
Author(s):  
Radiah Othman
Keyword(s):  
Fraud Detection ◽  
Financial Statement ◽  
Digital Environment ◽  
Financial Statement Fraud ◽  
Human Element ◽  
Do So

This chapter discusses some of the stages involved in detecting and investigating financial statement fraud in the digital environment. It emphasizes the human element aspects – the fraudster and the forensic investigator, their skills and capability. The explanation focuses specifically on the context of financial statement fraud detection and investigation which requires the accounting knowledge of the investigator. It also highlights the importance of the investigator to be a skeptic and have an inquiring mind of who had the opportunity to perpetuate the fraud, how the fraud could have been perpetrated, and the motive(s) to do so.

scholarly journals An Ensemble Model for Financial Statement Fraud Detection

2021 ◽  
Author(s):  
Ahmed M. Khedr ◽  
Magdi El Bannany ◽  
Sakeena Kanakkayil
Keyword(s):  
Class Imbalance ◽  
Fraud Detection ◽  
Financial Statements ◽  
Global Market ◽  
Financial Statement ◽  
Machine Learning Techniques ◽  
Accounting Fraud ◽  
Financial Statement Fraud ◽  
Mena Region ◽  
Ensemble Technique

Fraudulent financial statements are deliberate furnishing and/or reporting incorrect statistics, and this has become a major economic and social concern as the global market is witnessing an upsurge in financial accounting fraud, costing businesses billions of dollars a year. Identifying companies that manipulate financial statements remains a challenge for auditors, as fraud strategies have become increasingly sophisticated over the years. We evaluate machine learning techniques for financial statement fraud detection, particularly a powerful ensemble technique, the XGBoost algorithm, that help to identify fraud on a set of sample companies drawn from the MENA region. The issue of the class imbalance in the dataset is addressed by applying the SMOTE algorithm. We found that XGBoost algorithm outperformed other algorithms in this study: Logistic Regression (LR), Decision Tree (DT), Vector Machine Support (SVM), Adaboost, and RandomForest. The XGBoost algorithm is then optimised to obtain the optimum performance.

scholarly journals Olympus Corporation Financial Statement Fraud Case Study: The Role That National Culture Plays On Detecting And Deterring Fraud

2014 ◽  
Vol 10 (2) ◽  
pp. 175-184 ◽  
Author(s):  
Anita R. Morgan ◽  
Cori Burnside
Keyword(s):  
Corporate Governance ◽  
National Culture ◽  
Corporate Culture ◽  
Fraud Detection ◽  
Financial Statement ◽  
Japanese Culture ◽  
Financial Statement Fraud ◽  
The Impact ◽  
Insight Into

Recent cases provide insight into the role that an unethical corporate culture plays in financial statement fraud. The case of financial statement fraud in Olympus Corporation, a Japanese firm, provides the opportunity to examine how national culture plays a role in corporate governance and fraud detection. This case study focuses on the impact of Japanese culture on the corporate culture of The Olympus Corporation, and how that corporate culture resulted in financial statement fraud.

scholarly journals Factors Affecting Fraudulent Statement in Forensic Accounting Perspective

2019 ◽  
Vol 9 (1) ◽  
pp. 28-32
Keyword(s):  
Financial Stability ◽  
Linear Regression Analysis ◽  
Control Variable ◽  
Fraud Detection ◽  
Financial Statement ◽  
Manufacturing Companies ◽  
Financial Statement Fraud ◽  
Net Worth ◽  
Forensic Accounting ◽  
Equity Ratio

Nowadays the awareness of loss from fraud has been shifted from blue collared employee theft to white collared management fraudulent statement. Forensic accounting becomes one of the solutions to detect this fraudulent statement. On the basic of our premise, the purpose of research to explore empirical evidence regarding financial statement fraud detection factors with net worth method as control variable. Our independent variables were debt to equity ratio, change in net assets, and return on asset. The research was quantitatively on food and beverage manufacturing companies listed on the Indonesia Stock Exchange. We use financial statement year end audited from 2013-2017. We used purposive sample, when selecting the samples. Total of 55 company reports samples were used in this research. We analyzed the data using statistical multiple linear regression analysis. We used statistical software to do the regression, in order to answer the research questions and test the hypothesis. Fraudulent reporting fraud was examined using proxies Beneish M-score. After the statistical test, this research concludes that financial distress factor proxy Debt to Equity Ratio (DER) has no significant effect on fraud detection. Other factor that is financial stability was proxies by changes in total assets (ACHANGE), and financial targets in the proxy of Return on Assets (ROA) both have significant impact on the detection of financial statement fraud.

Feature Selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies

2020 ◽  
pp. 097215092092866
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Fraud Detection ◽  
Original Data ◽  
Financial Data ◽  
Financial Statement ◽  
Financial Fraud ◽  
Financial Statement Fraud ◽  
Financial Variables ◽  
Input Dataset

The financial fraud detection problem involves analysis of the large financial datasets. Financial statement fraud detection process is concentrated on two major aspects: first, identification of the financial variables and ratios, also termed as features. Second, applying the data mining methods to classify the organizations into two broad categories: fraudulent and non-fraudulent organizations. If the input dataset contains large number of irrelevant and correlated features, the computational load of the machine learning technique increases and the effectiveness of the classification outcomes decreases. The feature selection process selects a subset of most significant attributes or variables that can be the representative of original data. This selected subset can help in learning the pattern in data at much less time and with accuracy, in order to produce useful information for decision-making. This article briefly states the methods applied in the prior studies for selecting the features for financial statement fraud detection. This article also presents an approach to feature selection using correlation-based filter selection methods in which feature selection is performed based on ensemble model, and tests the outcome of the approach by applying the mean ratio analysis on financial data of Indian companies.

scholarly journals Antibody Watch: Text mining antibody specificity from the literature

2021 ◽  
Vol 17 (5) ◽  
pp. e1008967
Author(s):  
Chun-Nan Hsu ◽  
Chia-Hui Chang ◽  
Thamolwan Poopradubsil ◽  
Amanda Lo ◽  
Karen A. William ◽  
...  
Keyword(s):  
Neural Network ◽  
Text Mining ◽  
Knowledge Base ◽  
Experimental Evaluation ◽  
Deep Neural Network ◽  
Network System ◽  
Antibody Specificity ◽  
Joint Task ◽  
Neural Network System ◽  
Reliable Knowledge

Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many proposals have been developed to deal with the problem of antibody specificity, it is still challenging to cover the millions of antibodies that are available to researchers. In this study, we investigate the feasibility of automatically generating alerts to users of problematic antibodies by extracting statements about antibody specificity reported in the literature. The extracted alerts can be used to construct an “Antibody Watch” knowledge base containing supporting statements of problematic antibodies. We developed a deep neural network system and tested its performance with a corpus of more than two thousand articles that reported uses of antibodies. We divided the problem into two tasks. Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic. The second task is to link each of these snippets to one or more antibodies mentioned in the snippet. The experimental evaluation shows that our system can accurately perform the classification task with 0.925 weighted F1-score, linking with 0.962 accuracy, and 0.914 weighted F1 when combined to complete the joint task. We leveraged Research Resource Identifiers (RRID) to precisely identify antibodies linked to the extracted specificity snippets. The result shows that it is feasible to construct a reliable knowledge base about problematic antibodies by text mining.

scholarly journals DETECTING FINANCIAL STATEMENTS FRAUD IN SELECTED PHARMACEUTICAL COMPANIES IN INDIA

2020 ◽  
pp. 1-4
Author(s):  
Chetana R. Marvadi
Keyword(s):  
Earnings Quality ◽  
Fraud Detection ◽  
Financial Statements ◽  
Business Environment ◽  
Lessons Learned ◽  
Financial Statement ◽  
Pharmaceutical Companies ◽  
Financial Statement Fraud ◽  
Creative Accounting ◽  
The Common

In the business environment, rms are expected to disclose accurate and reliable nancial information. Financial statement fraud is actions which are taken to intentionally distort a company's reported nancial performance. Major corporate nancial statement Frauds get away in the name of creative accounting. But, they need to be studied for lessons learned and strategies to avoid or reduce the incidence of such frauds in the future. It is essential for shareholders, particularly the common man who does not have any access to the company except reported nancial numbers. This research paper attempts to detect the practices of nancial statement fraud in the Pharmaceutical Sector in India for investors' interest using Earnings quality, De Angelo and Beneish models of fraud detection. The result conrms the presence of nancial statement fraud in the companies under study. It is therefore expected that the study will help to improves investor's belief of a company's performance, as reected in their nancial numbers.

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