An Application of Ensemble Random Forest Classifier for Detecting Financial Statement Manipulation of Indian Listed Companies

Patel, Hiral A.; Parikh, Satyen M.; Patel, Amit; Parikh, Abhishek

doi:10.1007/978-981-13-1280-9_33

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…The respective cost obtained ranged between 0.340 and 0.406 for the RF and DTNB model, with statistically insignificant differences to FURIA. These results confirm that RF is considered a state-of-the-art method in this domain [31]. Note that these results must be compared with those in [12] with caution, given that we used a different feature selection method.…”

Section: Resultssupporting

confidence: 75%

“…However, different levels of granularity should be examined to provide empirical evidence. It is also suggested that the ensembles of fuzzy rule-based systems are investigated in future studies because ensemble methods have shown promising results for crisp rule-based systems in the related literature [31]. Further investigation into semantic interpretability of the fuzzy rule-based systems is also strongly recommended.…”

Section: Resultsmentioning

confidence: 99%

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Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Hájek

2019

IFIP Advances in Information and Communication Technology

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Systems for detecting financial statement frauds have attracted considerable interest in computational intelligence research. Diverse classification methods have been employed to perform automatic detection of fraudulent companies. However, previous research has aimed to develop highly accurate detection systems, while neglecting the interpretability of those systems. Here we propose a novel fuzzy rule-based detection system that integrates a feature selection component and rule extraction to achieve a highly interpretable system in terms of rule complexity and granularity. Specifically, we use a genetic feature selection to remove irrelevant attributes and then we perform a comparative analysis of state-of-the-art fuzzy rule-based systems, including FURIA and evolutionary fuzzy rule-based systems. Here, we show that using such systems leads not only to competitive accuracy but also to desirable interpretability. This finding has important implications for auditors and other users of the detection systems of financial statement fraud.

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Section: Resultssupporting

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Hájek

2019

IFIP Advances in Information and Communication Technology

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“…RF is highly efficient for handling obscure and unknown data and functions well, even if a dataset is large and complex (Rahmati et al, 2019). RF has been used in many fields and has demonstrated very high forecast accuracy (Chen et al, 2018;Masetic and Subasi, 2018;Hong et al, 2016;Pham et al, 2017;Al-Quraishi et al, 2018;Heddam and Kisi, 2018;Pourghasemi and Rahmati, 2018;Kim et al, 2018;Patel et al, 2019;Hashimoto et al, 2019. It consists of two major internal phases: the algorithm constructs copious bootstrap trials, which are considered calibration sets, and builds categorization conditions for every tree.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…Isfahan is an industrial city with many factories, and heavy human traffic might assist higher rates of spread around the urban area. It has been shown that low-income citizens migrating from the countryside for work have driven disease-spread to highdensity urban areas in African countries (Patel et al, 2019).…”

Section: Goodness-of-fit For the Relationship Between The Number Of Diagnosed Infections And The Number Of Days After The First Appearancmentioning

confidence: 99%

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

Pourghasemi¹,

Pouyan²,

Heidari³

et al. 2020

International Journal of Infectious Diseases

120

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Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the

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“…The studies in this SLR utilized Logistic Regression (LR) method frequently in their researches. In particular, 16 studies employed logistic regression [53,86,62,58,11,51,34,82,57,83,74,42,12,37,35,76]. Logistic regression models are applicable in multi-class and binary classifications.…”

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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An Application of Ensemble Random Forest Classifier for Detecting Financial Statement Manipulation of Indian Listed Companies

Cited by 13 publications

References 12 publications

Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)

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

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