The classification performance of ensemble decision tree classifiers: a case study of detecting fraud in credit card transactions
- Authors: Chogugudza, Mcdonald
- Date: 2022-11
- Subjects: fraud , Commercial fraud , Accounting fraud
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27590 , vital:69317
- Description: In this dissertation, we propose ensemble decision tree classifiers as an ideal classification technique for solving the problem of fraud in the domain of credit card transactions. Ensemble tree classifiers have been applied in many areas like speech recognition, image recognition and medical diagnostics and have shown excellent results. At the centre of fraud, credit card fraud has been a major concern. The rise in credit card fraud is largely attributed to the nature in which it can be done. A fraudster does not need to always be physically present to commit fraud making it the number one target for criminals. Card-Not-Present refers to this type of fraud where an electronic transaction can be conducted without the need for a client to be present. This can be done via telephonic calls or the web. To be able to come up with better classifiers it was important for the researcher to first investigate what causes misclassifications in fraud detection systems. A systematic literature review was done to uncover the factors that have been identified as causes of misclassifications. It was discovered that many factors lead to misclassifications and several authors have proposed techniques to handle these factors. However, there is no universal techniques for addressing factors that lead to misclassifications as different domains have different datasets which require different techniques. This study investigates how parameters involved in modelling fraud detection systems impact the classification performance of ensemble decision tree classifiers. The factors that were investigated include sample size, sampling technique, learning method and choice of split criterion and how they affect classification performance. A series of experiments were conducted to investigate how the aforementioned factors contributed to better classifiers. Ecommerce data from Vesta corporation made available on Kaggle was used in the experiments. The data was split into two sets, one for training the models and the other for testing the performance of the models. Accuracy, confusion matrix, precision and recall were used as performance measures. Our results showed that a larger sample size resulted in better classifiers. This is attributed to models having more instances to learn from which covers most patterns of fraudulent transactions. The sampling technique was shown to be pivotal in classification performance as under sampling showed a great reduction in performance as it achieved a maximum accuracy of 89.6223 while oversampling produced increased performance with maximum accuracy of 99.9531. Furthermore, our results showed that the choice of split criterion impacts the performance of ensemble tree classifiers. The use of entropy as the choice of split criterion resulted in better classifiers compared to the use of the Gini index. However, the downside is that entropy requires more time to execute compared to the Gini index. Lastly, the learning method proved to impact the performance of ensemble classifiers. Models that used supervised learning had better performance compared to those that use unsupervised learning in detecting credit card fraud. The conclusions from this research are insightful when designing fraud detection systems that use ensemble decision tree classifiers as base learners. , Thesis (Msci) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Authors: Chogugudza, Mcdonald
- Date: 2022-11
- Subjects: fraud , Commercial fraud , Accounting fraud
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27590 , vital:69317
- Description: In this dissertation, we propose ensemble decision tree classifiers as an ideal classification technique for solving the problem of fraud in the domain of credit card transactions. Ensemble tree classifiers have been applied in many areas like speech recognition, image recognition and medical diagnostics and have shown excellent results. At the centre of fraud, credit card fraud has been a major concern. The rise in credit card fraud is largely attributed to the nature in which it can be done. A fraudster does not need to always be physically present to commit fraud making it the number one target for criminals. Card-Not-Present refers to this type of fraud where an electronic transaction can be conducted without the need for a client to be present. This can be done via telephonic calls or the web. To be able to come up with better classifiers it was important for the researcher to first investigate what causes misclassifications in fraud detection systems. A systematic literature review was done to uncover the factors that have been identified as causes of misclassifications. It was discovered that many factors lead to misclassifications and several authors have proposed techniques to handle these factors. However, there is no universal techniques for addressing factors that lead to misclassifications as different domains have different datasets which require different techniques. This study investigates how parameters involved in modelling fraud detection systems impact the classification performance of ensemble decision tree classifiers. The factors that were investigated include sample size, sampling technique, learning method and choice of split criterion and how they affect classification performance. A series of experiments were conducted to investigate how the aforementioned factors contributed to better classifiers. Ecommerce data from Vesta corporation made available on Kaggle was used in the experiments. The data was split into two sets, one for training the models and the other for testing the performance of the models. Accuracy, confusion matrix, precision and recall were used as performance measures. Our results showed that a larger sample size resulted in better classifiers. This is attributed to models having more instances to learn from which covers most patterns of fraudulent transactions. The sampling technique was shown to be pivotal in classification performance as under sampling showed a great reduction in performance as it achieved a maximum accuracy of 89.6223 while oversampling produced increased performance with maximum accuracy of 99.9531. Furthermore, our results showed that the choice of split criterion impacts the performance of ensemble tree classifiers. The use of entropy as the choice of split criterion resulted in better classifiers compared to the use of the Gini index. However, the downside is that entropy requires more time to execute compared to the Gini index. Lastly, the learning method proved to impact the performance of ensemble classifiers. Models that used supervised learning had better performance compared to those that use unsupervised learning in detecting credit card fraud. The conclusions from this research are insightful when designing fraud detection systems that use ensemble decision tree classifiers as base learners. , Thesis (Msci) -- Faculty of Science and Agriculture, 2022
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The auditor's duty of reasonable care and skill and the expectation to detect fraud
- Kujinga, Benjamin Tanyaradzwa
- Authors: Kujinga, Benjamin Tanyaradzwa
- Date: 2008
- Subjects: Auditing Standards , Accounting fraud , Financial statements -- Law and legislation
- Language: English
- Type: Thesis , Masters , LLM
- Identifier: vital:11115 , http://hdl.handle.net/10353/104 , Auditing Standards , Accounting fraud , Financial statements -- Law and legislation
- Description: Auditors perform a very important task within the context of the affairs of a company because financial reporting can only serve its purpose if stakeholders can rely on its accuracy and reliability. An auditor’s duty is to opine whether an entity’s financial reporting has been done according to the requirements of the law. The responsibility of reporting according to the law lies with an entity’s directors. Auditors cannot issue an absolute assurance as to the lawfulness and reliability of an entity’s financial reporting. However when it is subsequently discovered that the financial reporting was incorrect and that fraud has occurred auditors are often blamed and sued for enormous amounts of money for failing to detect material anomalies in the financial reports. These actions are based on the fact that auditors have a duty to exercise reasonable care and skill in the performance of their duties and through their failure to act as such, have caused financial harm to the clients or third parties. The fact that auditors are only required by law to exercise reasonable care and skill and perform an audit according to the standards of the reasonable auditor and not the most meticulous one, is often not regarded or is sometimes deliberately ignored. This clearly represents a problem in our law, namely that the presence of fraud in financial reports does not in itself suggest negligence on the part of the auditor but is apparently often perceived to do so. This research shows that the auditor’s duty of reasonable care and skill does not necessarily entail the duty to detect fraud. The elements of the duty of reasonable care and skill are identified from case law, legislation and international auditing standards. In order to limit the liability of auditors in general it is important to focus also on the elements of fault (negligence), wrongfulness and causation. This research shows that negligence cannot be established merely by the presence of fraud or material misstatements in financial statements. The responsibility for fair financial reporting lies with the directors. This research gives prominence to this fact which often seems to be ignored for convenience and in order to place the blame on the auditors. This research implicitly asks the question, why are auditors being held responsible for material misstatements in a company’s financial statements and not the directors? Guidelines for determining the extent of an auditor’s liability in this regard are formulated in this research.
- Full Text:
- Authors: Kujinga, Benjamin Tanyaradzwa
- Date: 2008
- Subjects: Auditing Standards , Accounting fraud , Financial statements -- Law and legislation
- Language: English
- Type: Thesis , Masters , LLM
- Identifier: vital:11115 , http://hdl.handle.net/10353/104 , Auditing Standards , Accounting fraud , Financial statements -- Law and legislation
- Description: Auditors perform a very important task within the context of the affairs of a company because financial reporting can only serve its purpose if stakeholders can rely on its accuracy and reliability. An auditor’s duty is to opine whether an entity’s financial reporting has been done according to the requirements of the law. The responsibility of reporting according to the law lies with an entity’s directors. Auditors cannot issue an absolute assurance as to the lawfulness and reliability of an entity’s financial reporting. However when it is subsequently discovered that the financial reporting was incorrect and that fraud has occurred auditors are often blamed and sued for enormous amounts of money for failing to detect material anomalies in the financial reports. These actions are based on the fact that auditors have a duty to exercise reasonable care and skill in the performance of their duties and through their failure to act as such, have caused financial harm to the clients or third parties. The fact that auditors are only required by law to exercise reasonable care and skill and perform an audit according to the standards of the reasonable auditor and not the most meticulous one, is often not regarded or is sometimes deliberately ignored. This clearly represents a problem in our law, namely that the presence of fraud in financial reports does not in itself suggest negligence on the part of the auditor but is apparently often perceived to do so. This research shows that the auditor’s duty of reasonable care and skill does not necessarily entail the duty to detect fraud. The elements of the duty of reasonable care and skill are identified from case law, legislation and international auditing standards. In order to limit the liability of auditors in general it is important to focus also on the elements of fault (negligence), wrongfulness and causation. This research shows that negligence cannot be established merely by the presence of fraud or material misstatements in financial statements. The responsibility for fair financial reporting lies with the directors. This research gives prominence to this fact which often seems to be ignored for convenience and in order to place the blame on the auditors. This research implicitly asks the question, why are auditors being held responsible for material misstatements in a company’s financial statements and not the directors? Guidelines for determining the extent of an auditor’s liability in this regard are formulated in this research.
- Full Text:
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