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Deep Learning For Financial Risk Assessment And Fraud Detection
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Explainable Ai In Fraud Detection
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Fraud Detection Using Machine Learning
Entered: 23 August 2022 / Modified: 18 September 2022 / Approved: 20 September 2022 / Published: 26 September 2022
Financial fraud, seen as a deceptive tactic to obtain financial gain, has recently become a widespread threat in companies and organizations. Traditional methods such as manual checks and inspections are costly and time-consuming to detect these fraudulent activities. With the advent of artificial intelligence, machine learning approaches can be intelligently used to detect fraudulent transactions by analyzing large amounts of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Specifically, the review used Kitchenham’s approach, which uses well-defined protocols to extract and collect relevant articles; then reports on the results obtained. Several studies have been compiled based on well-known search strategies from popular electronic data libraries. After inclusion/exclusion criteria, 93 articles were selected, synthesized and analyzed. A review of common ML techniques used for fraud detection summarizes the most common types of fraud metrics and evaluation. The reviewed articles indicated that support vector machine (SVM) and artificial neural network (ANN) are common ML algorithms used for fraud detection, and credit card fraud is the most common type of fraud using ML methods. are treated. The paper finally presents the main problems, shortcomings and limitations in the field of financial fraud detection and suggests possible directions for future research.
Financial fraud is the use of financial advantage in illegal and fraudulent ways [1, 2]. Financial fraud can occur in various industries such as insurance, banking, tax and corporate sectors . Recently, financial transaction fraud , money laundering and other types of financial fraud  have become a major problem among companies and industries . Despite many efforts to reduce financial fraud, its persistence has a negative impact on the economy and society, as large amounts of money are lost to fraud every day . Many fraud detection approaches have been introduced many years ago . Most of the traditional methods are manual, which is not only time-consuming, expensive and uncertain, but also impractical . More studies are conducted to reduce the losses due to fraudulent activities, but they are not effective . With the development of artificial intelligence (AI), machine learning and data mining approaches have been used to detect fraudulent activities in the financial sector [8, 9]. Both unpublished and supervised methods have been used to predict cheating activity [4, 10]. Classification methods are the most popular method for detecting fraudulent financial transactions. In this scenario, the first stage of model training uses a dataset with class labels and feature vectors. The trained model is then used to classify the test samples in the next step [1, 2, 5].
Fraud Risk Management
Thus, this study attempts to identify machine learning techniques for financial transaction fraud and analyze gaps to discover research trends in this field. Recently, some investigations have been conducted to detect fraudulent financial activities [11, 12, 13]. For example, Delamire et al.  reviewed different categories of credit card fraud activities, including bankruptcy and counterfeiting, and proposed appropriate solutions. Similarly, Zhang and Zhou  investigated ML methods for fraudulent transactions involving the stock market and other fraud detection processes in financial sectors. Raj and Portia.  investigated several ML approaches used for credit card fraud detection. Phua et al.  conducted an extensive study to apply data mining and machine learning techniques to fraud detection in various fields, including credit card fraud, insurance fraud, and telephone subscription fraud.
Recently, there has been a significant increase in fraud activity in the health sector . Abdullah and others.  presented a review to investigate different approaches based on statistical approaches to detect fraudulent activities in healthcare. Popat and Chaudhary  presented a comprehensive work on credit card fraud detection. The authors provide a detailed analysis of different ML classification methods with their methodology and challenges. Ryman-Tub et al.  reviewed several innovative methods for fraud detection in payment cards using transaction volume. The study found that only eight approaches have practical value for use in industry. A study by Albashrawi and Lowell  analyzed a number of studies over ten years related to the use of transaction processing techniques in the financial sector. However, it was not comprehensive enough because they ignored the evaluation method and the advantages and disadvantages of data processing methods.
Despite the few existing studies in this area, most of the studies focus on specific areas of finance, such as credit card fraud detection , online banking fraud , bank loan management fraud , and fraud. . on payment cards . Therefore, there is a need for research that covers all common areas of financial fraud activity to fill the gap in this area. Recently, a study was published to evaluate methods for detecting fraud in financial records . The authors summarize the previous multidisciplinary literature on financial reporting fraud. However, there are many differences between their work and our review. First, their primary goal is to integrate research from multiple fields, including information systems, analytics, and accounting. On the other hand, we aim to identify financial fraud transactions based on machine learning techniques and discover datasets that can be used in ML financial fraud detection. In addition, we considered conference papers in our study, when they were not available. This study reviews current machine learning (ML) techniques used to detect fraud in financial transactions. Additionally, SLR can guide researchers in their choice of using ML-based fraud detection methods in financial transactions and datasets used to predict fraudulent activity in financial transactions.
Machine Learning For Financial Risk Management With Python: Algorithms
The remainder of this paper is organized as follows: Section 2 describes the research methodology, including search criteria, study selection, data extraction, and quality assessment. The results of the SLR and the answers to the research questions are presented in section 3. Controversies and potential problems that undermined the validity of this review are discussed in Sections 4 and 5, respectively. Finally, we present the results of the study in section 6.
This paper uses an SLR approach, which is a detailed approach that collects and analyzes all studies that address specific research questions [ 22 ]. It is used to identify and integrate information that focuses on specific topics to reduce bias [ 17 , 22 ], provide reviews with high-quality evidence, and review researchers’ judgments and conclusions [ 22 ]. This SLR study is based on the study of , which includes three basic steps: planning the audit, conducting the audit, and reporting the audit. The main stages of SLR are shown in figure 1.
The planning phase presents the processes of preparing and developing the SLR, which includes the definition of the research objective and the development of the research protocol [2, 24]. To obtain relevant documents, an automatic search was performed in relevant digital databases [25, 26]. Other similar data are not considered as data from primary sources. These libraries are due to their popularity and rich sources of articles related to research questions
Model Risk Managers Eye Benefits Of Machine Learning
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