A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique

Ali, Amal Al; Khedr, Ahmed M.; El‐Bannany, Magdi; Kanakkayil, Sakeena

doi:10.3390/app13042272

Cited by 15 publications

(6 citation statements)

References 53 publications

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“…ML and DL models can be used parallelly to build a model with improved capabilities, as in Hybrid Learning (HL). They can also be combined, and the output of one model is the input for the second, and that is called Ensembled Learning (EL) as in [49] [29]. Accordingly, the training process of CADM uses an HL approach for enhanced performance.…”

Section: Big Data In Accounting and Auditingmentioning

confidence: 99%

CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

Bineid,

Beloff,

White

et al. 2023

Annals of Computer Science and Information Systems

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Global financial scandals have demonstrated the harmful impact of creative accounting, a practice where managers creatively manipulate financial reports to conceal a company's actual performance and influence stakeholders' decision-making. Studies showed that Saudi-listed companies use it in preparing financial statements. Despite posing a significant risk to the Saudi financial market, detecting it using ordinary auditing procedures remains challenging. Big data analytics has provided practical applications in auditing, and recently, the employment of Deep Learning in fraud detection has delivered remarkably accurate results. Still, limited research has considered it in detecting creative accounting. This study proposes a novel framework using a hybrid learning approach. It suggests training on a simulated dataset of financial statements prepared (i.e., deliberately manipulated) based on financial statements available in the literature for supervised learning. It is then tested on real-world financial reports from the Saudi Open Data and Saudi Statistics. Our framework contributes to the literature with a new governing approach to limit creative accounting and improve financial reporting quality.

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Section: Big Data In Accounting and Auditingmentioning

confidence: 99%

CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

Bineid,

Beloff,

White

et al. 2023

Annals of Computer Science and Information Systems

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“…These papers collectively contribute to the field of data-driven applications and predictive modeling across various domains, like a Decision Support System for the Marketing domain [11]. They offer an advanced model for detecting financial statement fraud using XGBoost [12], introduce innovative neural network models for stock price prediction [13], analyze factors influencing tourist offer prices [14], develop predictive models for healthcare patient influx [15], and propose intelligent decision forest models for customer churn prediction in the telecom industry [16]. They also address customer churn prediction in noncontractual B2B settings [17], improve legal judgment prediction through graph neural networks [18], enhance car sales forecasts using online sentiment data and deep learning [19], introduce a reinforcement learning framework for options trading [20], and predict the charge of a legal case using a novel graph convolutional network [21].…”

Section: Category 2: Marketing and Business Decision Supportmentioning

confidence: 99%

Methods and Applications of Data Mining in Business Domains

Amrit,

Abdi

2023

Applied Sciences

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“…The models with the Gini and Entr suffixes indicate the measures used to determine how a decision tree node splits. These measures are referred to as the Gini index and Entropy, which is a measure of the purity of the split [42]. XGBoost is an extreme gradient-boosted tree with an ensemble algorithm, with each tree boosting misclassified attributes of the previous tree [43].…”

Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Paladino,

Hughes,

Perera

et al. 2023

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Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care.

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A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique

Cited by 15 publications

References 53 publications

CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

Methods and Applications of Data Mining in Business Domains

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

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