Data mining application to detect financial fraud in Indonesia's public companies

Rizki, Adila Afifah; Surjandari, Isti; Wayasti, Reggia Aldiana

doi:10.1109/icsitech.2017.8257111

Cited by 22 publications

(8 citation statements)

References 7 publications

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“…However, there was only one article which employed PNN [62] and SOM [18] neural network approaches. Unfortunately, three articles did not mention the learning way of their neural network approach [10,66,82].…”

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

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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.

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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

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“…In addition, the work in [50] proposed the use of NN to correlate information from a variety of technological sources and databases in order to identify suspicious account activity. The work in [52] applied data mining algorithms, such as a SVM and ANNs, to detect financial fraud. The authors stated that the essential indicators of financial fraud are profitability and efficiency.…”

Section: Card Transactions From An Indonesian Bankmentioning

confidence: 99%

“…[45,46,49,56,57,60,63,67,69] obtained the highest score of 2.5, which represents 83.33% of the maximum score that a preliminary study could obtain; on the other hand, Refs. [38,39,41,44,48,[50][51][52][53]55,59,65] obtained a score of 2, that represents 66.67% of the maximum score. Refs.…”

Section: Quality Assessmentmentioning

confidence: 99%

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

2021

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Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.

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“…There are various studies abroad using different methods such as feature selection (Ravisankar et al, 2011;Rizki et al, 2017;Yao et al, 2018), neural networks (Jan, 2018;Kirkos et al, 2007;Lin et al, 2015;Perols, 2011;Ravisankar et al, 2011;Rizki et al, 2017), genetic algorithms (Hoogs et al, 2007;Ravisankar et al, 2011), decision trees (Bai et al, 2008;Jan, 2018;Lin et al, 2015;Perols, 2011;Yao et al, 2018), fuzzy (Lenard et al, 2007), logistic regression (Lin et al, 2015;Perols, 2011) and support vector machines (Perols, 2011;Rizki et al, 2017).…”

Section: Literature On Researchmentioning

confidence: 99%

Identifying the Companies With the Risk of Financial Statement Fraud by Data Mining

Kirda

Özçelik

2021

Muhasebe Ve Vergi Uygulamaları Dergisi

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Finansal tablo hilesi, şirketlerin finansal tablolarındaki verileri kendi çıkarları doğrultusunda değiştirerek yayınlamalarıdır. Kurumlara, paydaşlara ve ekonomik yapıya ciddi zararlar veren finansal tablo hilelerinin tespit edilmesi önemli bir problemdir. Bunun için çeşitli denetim mekanizmaları bulunmaktadır. Ancak zaman içerisinde geliştirilebilecek hile yöntemlerine karşı yenilikçi denetim yöntemlerine ihtiyaç duyulmaktadır.Veri madenciliği, finansal tablo hilelerinin tespitinde umut vadeden bir alandır. Veri madenciliğinin sınıflandırma analizinde sınıflandırma metotlarıyla mevcut verilerden örüntüler elde edilir ve bunlar görülmemiş birimlerin sınıflandırılmasında kullanılır. Bu çalışmada veri madenciliğinin sınıflandırma metotları ile finansal tablo hilesi riski taşıyan şirketlerin tespiti üzerine bir araştırma yapılmıştır.Veriler Borsa İstanbul'da 2014-2018 arasında işlem gören şirketlerin yayınladıkları finansal tablolardan elde edilmiştir. İlk olarak yedi sınıflandırılma metodu kullanılmış, en başarılı üçü seçilmiştir. Sonraki aşamada başarım değerlerinin geliştirilmesi amacıyla hiper parametre optimizasyonu yapılmıştır.Sınıflandırma metotlarından K-Nearest Neighbor ile yüzde 91,73, Random Forest ile yüzde 90,51 ve XGBoost ile yüzde 90,37 doğruluk oranlarına ulaşılmış, en iyi tahmin oranı K-Nearest Neighbor ile elde edilmiştir. Son kısımda rasgele alt örnekleme yöntemiyle yapılan karşılaştırmalarda da en iyi performans değerleri K-Nearest Neighbor ile elde edilmiştir.

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Data mining application to detect financial fraud in Indonesia's public companies

Cited by 22 publications

References 7 publications

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

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

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Identifying the Companies With the Risk of Financial Statement Fraud by Data Mining

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