Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach

Yao, JingTao; Pan, Yanqin; Yang, Shuiqing; Chen, Yuangao; Li, Yixiao

doi:10.3390/su11061579

Cited by 31 publications

(27 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Supervised learning techniques were selected for analysis more than unsupervised ones, with 65% of the articles from US, China, Taiwan and Spain [10]. A considerable number of studies that analysed the performance of classifiers on FSF detection have shown that SVM [11,12,13,14,15], NN [16,17,18,19], DT [20,21] perform well in FSF detection/prediction.…”

Section: Related Researchmentioning

confidence: 99%

An Ensemble Model for Financial Statement Fraud Detection

Khedr¹,

El‐Bannany²,

Kanakkayil³

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Related Researchmentioning

confidence: 99%

An Ensemble Model for Financial Statement Fraud Detection

Khedr¹,

El‐Bannany²,

Kanakkayil³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Consequently, even early studies (in 2012) examined CART for the FSFD problem and performed relatively well for this problem [32]. Furthermore, a most recent article used six different classifiers that one of them was the CART algorithm [83]. C4.5 algorithm operates as a decision tree classifier among ML/DM techniques.…”

Section: ) Technique Typesmentioning

confidence: 99%

Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review

Ashtiani

Raahemi

2022

IEEE Access

View full text Add to dashboard Cite

Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets, sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the area of fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves further study as it can show interesting results for intelligent fraud detection.

show abstract

“…Financial statements are the main reference point for investors, creditors and stakeholders in the decision-making process (Yao et al, 2019). Fraudulent financial reporting transactions have increased rapidly in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Income-Creating / Expense Reducing Activities Under Fraudulent Financial Reporting

Erdoğan¹,

Erdoğan²

2021

Mehmet Akif Ersoy Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

View full text Add to dashboard Cite

Son dönemlerde finansal skandallar ve finansal krizlerle birlikte ortaya çıkan ve mesleki hile türlerinden biri olan finansal tablo hileleri tüm paydaşlar gibi özellikle finans aktörlerini de yakından ilgilendirmektedir. Finansal tablo hileleri yapılış amacına göre farklılık göstermektedir. Hileyi gerçekleştiren kişilerin amacı işletme performansını yüksek göstermek ise işletmenin karlılığı, gelirleri veya varlıkları olduğundan yüksek ve/veya işletme gider veya zararları olduğundan düşük; hileyi gerçekleştiren kişilerin amacı vergiden kaçınmak ve/veya daha az kar payı dağıtmak şeklinde ise tam tersi uygulamalar yoluna gidilecek ve bu kapsamda gerçeğe uygun olmayan bir raporlama yapılacaktır. Bu açıdan ele alındığında finansal tablolar finans piyasalarında yer alan tüm finans aktörleri için diğer bir ifade ile yatırımcılar, tedarikçiler, alacaklılar ve diğer tüm paydaşlar için karar verme süreçlerinde yol gösterici niteliktedir. Dolayısıyla tüm paydaşlar açısından işletmeleri doğru ve gerçekçi finansal tablolar üzerinden değerlendirmek önem taşımaktadır. Bu çalışma da Antalya'da faaliyet gösteren bir işletmenin belirlenen varsayımlar temelinde hileli ve/veya hatalı finansal raporlama uygulamaları sonucunda oluşturulmuş finansal tabloları ve muhasebe kayıtları ile hile ve/veya hatadan arındırılmış finansal tablolarında yer alan mali kalemlerin karşılaştırma analiz ile değerlendirmesi yapılmıştır.

show abstract

Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach

Cited by 31 publications

References 22 publications

An Ensemble Model for Financial Statement Fraud Detection

An Ensemble Model for Financial Statement Fraud Detection

Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review

Analysis of Income-Creating / Expense Reducing Activities Under Fraudulent Financial Reporting

Contact Info

Product

Resources

About