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A Novel Model For Financial Fraud Detection Using Machine Learning Techniques

Rahmati, Mahdieh | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55952 (44)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Khedmati, Majid
  7. Abstract:
  8. Today, e-commerce systems are used by both types of users. Therefore, the systems will be exposed to systematic fraud, and fraud is one of the main sources of financial losses for organizations. Therefore, it is very important for organizations to use accurate methods to detect fraud. this field is one of the most important applications of data mining in finance. There are various challenges in fraud detection projects, and this research has divided these challenges into three categories, which are: data pre-processing due to the imbalance data set, the accuracy of the machine learning model, and uncertainty. In the first part, both oversampling and undersampling methods will be used in order to solve the challenge of unbalanced data set. In the second part, an innovative combined unsupervised and supervised method will be used to improve the accuracy of the machine learning model. Then we will use deep learning model to increase the complexity. Increasing complexity is not always desirable and this will be evaluated. Another serious challenge in the field of fraud detection that prevents the creation of efficient and reliable models in the real world is the issue of uncertainty. This means that fraudsters in the real world are always changing their methods, and therefore the model must be designed in such a way that it has an acceptable performance in the face of completely new observations and different from the statistical distribution of the training data, because the process of updating the deep learning model with the occurrence of new and different observations by experts is very time-consuming. In the first part, the oversampling method had better results. In the second part, the use of the innovative method led to the improvement of the accuracy of the model compared to five well-known models (decision tree, random forest, support vector machine, logistic regression and k-nearest neighbor) in the literature. In the third part, the implementation of the dropout approach in the neural network increased the accuracy of this complex model
  9. Keywords:
  10. Infraction Detection ; Deep Learning ; Machine Learning ; Financial Fraud Detection (FFD) ; Credit Card Fraud ; Financial Fraud

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