On Big Data-Based Fraud Detection Method for Financial Statements of Business Groups

2017 ◽  
Author(s):  
Yuh-Jen Chen ◽  
Chun-Han Wu
Keyword(s):  
Big Data ◽  
Detection Method ◽  
Business Groups ◽  
Fraud Detection ◽  
Financial Statements

Fraud detection for financial statements of business groups

2019 ◽  
Vol 32 ◽  
pp. 1-23 ◽  
Author(s):  
Yuh-Jen Chen ◽  
Wan-Ching Liou ◽  
Yuh-Min Chen ◽  
Jyun-Han Wu
Keyword(s):  
Business Groups ◽  
Fraud Detection ◽  
Financial Statements

scholarly journals Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 800
Author(s):  
Jongchan Park ◽  
Min-Hyun Kim ◽  
Dong-Geol Choi
Keyword(s):  
Deep Learning ◽  
Simple Technique ◽  
Detection Method ◽  
Fraud Detection ◽  
Complementary Information ◽  
Multiple Modalities ◽  
Vending Machines ◽  
Emotion Intensity ◽  
The Relationship ◽  
Extract Information

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.

Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session

2019 ◽  
Vol 34 (3) ◽  
pp. 324-337 ◽  
Author(s):  
Jiali Tang ◽  
Khondkar E. Karim
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Idea Generation ◽  
Fraud Detection ◽  
Content Type ◽  
Collective Wisdom ◽  
Fraud Risk ◽  
Collection Data ◽  
Audit Team

PurposeThis paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.Design/methodology/approachThe authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.FindingsThe existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.Originality/valueThe proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

scholarly journals Kecurangan Dalam Laporan Keuangan “Menguji Teori Froud Triangle”

2018 ◽  
Vol 19 (1) ◽  
pp. 77
Author(s):  
Langgeng Prayitno Utomo
Keyword(s):  
Logistic Regression ◽  
Fraud Detection ◽  
Financial Statements ◽  
External Pressure ◽  
Fraud Triangle ◽  
Triangle Theory ◽  
Fraudulent Financial Statements

This study aims to examine the factors that affect the fraudulent financial statements of the company. Fraud detection of financial statements using fraud triangle theory. Based on the theory of fraud triangle there are three factors: pressure, opportunity, and rationalization are used as parameters to detect fraud. The sample of this study used 44 companies in 3 years of observation, where the company is divided into companies that are indications of fraud and not by doing the analysis using the calculation of the underlying M-score, this study used logistic regression, the result that the indication of fraud in this study only can be obtained from external pressure factors on pressure variables and the effectiveness of monitoring on the opportunity variables, this study fails to establish influence in three factors at once ie pressure, opportunity, and rational

scholarly journals PENGARUH SKEPTISME PROFESIONAL DAN PENGALAMAN AUDITOR TERHADAP KEMAMPUAN AUDITOR EKSTERNAL DALAM MENDETEKSI KECURANGAN (SURVEI PADA AUDITOR BPK RI PERWAKILAN SULAWESI UTARA)

2018 ◽  
Vol 13 (02) ◽  
Author(s):  
Monica Mokoagouw ◽  
Lintje Kalangi ◽  
Natalia Gerungai
Keyword(s):  
Linear Regression ◽  
Fraud Detection ◽  
Financial Statements ◽  
Primary Data ◽  
Professional Skepticism ◽  
Detection Ability ◽  
North Sulawesi ◽  
Competent Person ◽  
External Auditor ◽  
Positive Effect

External auditor is an independent and competent person who can examine financial statements and be responsible for the opinions given. This research aims to examine the effect of professional skepticism and auditor’s experience on fraud detection ability of external auditor. The sample in this research are all auditors who work in Badan Pemeriksa Keuangan (BPK) Republik Indonesia (The Indonesia’s Supreme Audit Institution) Reprensentative of North Sulawesi Province. The data of this research is using primary data. The data was collected  by distributing  questionnaires directly to auditors of BPK RI Representative of North Sulawesi Province. This research is using the multiple linear regression analsys with SPSS 23.  The result indicate that: 1) Professional Skepticism has positive effect towards Fraud Detection Ability of External Auditor. 2) Auditor’s Experience has positive effect towards Fraud Detection Ability of External Auditor. 3) Professional Skepticism and Auditor’s Experience simultaneously have positive effect towards Fraud Detection Ability of External Auditor. Keywords: Professional Skepticism, Auditor’s experience, Fraud Detection Ability of External Auditor

SmartFD: A Real Big Data Application for Electrical Fraud Detection

2018 ◽  
pp. 120-130
Author(s):  
D. Gutiérrez-Avilés ◽  
J. A. Fábregas ◽  
J. Tejedor ◽  
F. Martínez-Álvarez ◽  
A. Troncoso ◽  
...  
Keyword(s):  
Big Data ◽  
Fraud Detection ◽  
Data Application ◽  
Big Data Application
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