Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains

The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature

The growth of Credit Card (CC) fraud over the last few years necessitates the construction of fraud detection models that are both efficient and robust. This work investigated the use of machine learning models, especially ensemble methods, to improve the detection of CC fraud. We herein introduce an ensemble model that combines various classifiers to solve the dataset imbalance problem that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. Online transactions have become an essential aspect of life as universe becomes more technological and every industry leverages the web to grow enterprises. Online transactions have been increasing steadily, and this trend is expected to continue. Credit cards are a popular form of internet transaction, but with their widespread use comes a significant drawback: credit card fraud. Since banks are unable to screen every transaction, machine learning is essential to identifying credit card fraud. In our research, we used Kaggle to gather a dataset of 2,844,808 credit card transactions from a European Bank Dataset. There are 492 fraudulent transactions in it; to balance the dataset, we proposed hybrid resampling method and for the detection of credit card fraud, Random Forest Algorithm is employed. The evaluation of the model is evaluated based on accuracy, precision, recall, and F1-score.

Advancing financial fraud detection: Self-attention generative adversarial networks for precise and effective identification

At present times, credit card frauds appears as a major issue of stealing and fraud obligation using payment cards such as credit or debit cards. To prevent credit card frauds, several financial organizations were initiated to design effective models to detect the existence of frauds. The growth of Deep Learning (DL) models finds useful to examine the usual and unusual patterns along with separate transactions for raising an alert for probable frauds. This paper presents a novel DL based convolutional long short term memory (C-LSTM) model for credit fraud detection. The proposed C-LSTM model involves preprocessing, and classification. The preprocessed data undergo classification using the C-LSTM model to detect the occurrence of credit card frauds or not. The performance of the C-LSTM model is validated using German Credit and Kaggle's Credit Card Fraud Detection dataset. The attained experimental values portrayed that the C-LSTM model has shown proficient performance with the accuracy of 94% and 94.65% on the applied German credit and Credit card fraud detection dataset.

Financial fraud, such as money laundering, is known to be a serious process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. This kind of illegal activities involve complex networks of trade and financial transactions, which makes it difficult to detect the fraud entities and discover the features of fraud. Fortunately, trading/transaction network and features of entities in the network can be constructed from the complex networks of the trade and financial transactions. The trading/transaction network reveals the interaction between entities, and thus anomaly detection on trading networks can reveal the entities involved in the fraud activity; while features of entities are the description of entities, and anomaly detection on features can reflect details of the fraud activities. Thus, network and features provide complementary information for fraud detection, which has potential to improve fraud detection performance. However, the majority of existing methods focus on networks or features information separately, which does not utilize both information. In this paper, we propose a novel fraud detection framework, CoDetect, which can leverage both network information and feature information for financial fraud detection. In addition, the CoDetect can simultaneously detecting financial fraud activities and the feature patterns associated with the fraud activities. Extensive experiments on both synthetic data and real-world data demonstrate the efficiency and the effectiveness of the proposed framework in combating financial fraud, especially for money laundering.

Credit card fraud is a growing concern, and it poses a significant threat as individual information is being misused and causing a substantial monetary loss. Hence, the prevention of credit card fraud is crucial. Credit card fraud detection is used to differentiate the transactions, either as legitimate or fraudulent. Recently, different machine learning techniques have been implemented to detect credit card fraud. However, the main challenge with fraud detection is that the credit card data is highly skewed, with the fraudulent transactions as less as 1% of the total data. This study investigates the performance of the four supervised machine learning algorithms: logistic regression, support vector machine, decision tree, and random forest, along with different sampling techniques to better understand the fraud detection attributes and performance measures associated with it. This review is also concentrated on exploring different works where the model has a better value for all of the performance evaluation metrics: Recall, precision, F1-score, accuracy, MCC, AUC, and area under the precision-recall curve. This will detect credit card fraudulent transactions better and control credit card fraud.

Abstract: This chapter examines the transformative impact of AI-driven technologies in FinTech, focusing on revolutionizing fraud detection and risk management in the financial sector. By leveraging advanced machine learning algorithms, AI enables real-time monitoring and detection of fraudulent activities, such as credit card fraud, money laundering, and account takeovers, providing faster and more accurate responses. The chapter also explores how AI is enhancing predictive analytics in risk management, helping financial institutions forecast credit, market, and operational risks with greater precision. Through the integration of AI with regulatory compliance systems, anti-money laundering (AML) programs, and customer authentication technologies, the financial industry is becoming more secure and efficient. Ethical considerations, data privacy, and the future role of AI in shaping the financial landscape are also discussed. Keywords: AI-driven FinTech, fraud detection, risk management, machine learning, predictive analytics, credit risk, market risk, operational risk, real-time monitoring, anti-money laundering, customer authentication, financial security, data privacy, AI ethics, financial technology.

The purpose of this study is to respond to research gaps and inconsistencies in research findings by empirically testing how financial targets and external pressures affect the detection of financial statement fraud. The study utilizes a descriptive research design with a quantitative approach. A method of purposive sampling was used, focusing on companies listed on the Jakarta Islamic Index (JII) from 2019 to 2023. Statistical analysis was done using multiple regression analysis. The results show that financial targets have a significant effect on the detection of financial statement fraud, while external pressures do not have a significant effect. However, both financial targets and external pressures have a simultaneous effect on the detection of financial statement fraud. The practical implications, financial targets, and external pressures can have an impact on the detection of fraud within the organization. Under these conditions, fraud may be more difficult to detect because the organization's or individual's focus is more on achieving targets or meeting external expectations. Therefore, companies need to have a strong internal control system and independent audits to help identify and manage potential risks of financial statement fraud.

In today's financial landscape, fraud detection is a pressing concern that demands advanced technological solutions to protect assets and uphold consumer trust. This chapter examines the transformative role of machine learning (ML) in enhancing fraud detection mechanisms. It begins by outlining various types of financial fraud, such as credit card fraud, insurance fraud, identity theft, money laundering, and insider trading, underscoring the need for sophisticated detection techniques. The chapter details the ML methodologies used, including supervised and unsupervised learning algorithms, as well as deep learning techniques. It also covers data handling, implementation strategies, and real-world case studies that demonstrate the effectiveness of these approaches. Challenges like data privacy and model interpretability are addressed, along with solutions. Finally, it discusses future trends, including the integration of blockchain technology and advancements in AI, highlighting the evolving landscape of financial fraud detection

Credit Card Fraud (CCF) detection is a major challenge in financial security, especially in detecting unauthorized transactions. As fraudsters' patterns increase, traditional methods face challenges in detecting them. Hence, this paper proposes a Sea Lion - Self-Supervised Network (SL-SSNet) to improve detection accuracy. This research study aims to enhance performance by optimizing data quality feature extraction and achieving better results in fraud detection. The innovative approach for CCF detection using a hybrid optimization model combines the strengths of Sea Lion optimization and Self-Supervised Networks to improve both model accuracy and performance. Initially, the CCF dataset is collected from Kaggle. Then the data goes through a pre-processing phase where irrelevant data points, noise, and low-quality data are removed. The relevant data is selected in the next phase. Feature extraction is performed to select the most important and influential features related to fraud detection. The final phase is prediction and performance evaluation. The results show that the SL-SSNet model performs better than other methods in fraud detection. Specifically, the model achieved 99.98% accuracy, 82.46% precision, 97.23% recall, and 89.97% F1-score. These results prove the effectiveness and robustness of SL-SSNet in detecting fraudulent transactions.

A novel model for credit card fraud detection using Artificial Immune Systems

Credit card fraud is one of the worldwide problems which affects everyone. Credit card fraud detection is a distribution issue with the aim of automatically and adaptively categorizing genuine and fraudulent transactions. Any malicious behavior causing financial loss to the other party is classifying as fraud. For example, in poor nations, the use of digital currency, or even plastic money, is on the rise it has a history of defrauding people. They have a track record of scamming others. In recent years, credit card fraud has increased. Customers and institutions all over the world are paying billions of dollars. Fraudsters continue to thrive despite the multiple fraud-prevention devices in place. In this study report, we're seeking to come up with new ways to swindle people. As a result, combatting these scams demands the implementation of a sophisticated fraud detection system. Fraud is not only detected, but also prevented by the system. The systems must be able to learn from past fraud schemes and adapt to new ones. The notion of credit card fraud, as well as the many types of fraud, were discussed in this study. Several fraud detection methods, such as logistic regression, decision trees, and random forests, were studied. Existing and proposed theories for credit card fraud are carefully scrutinized, and these strategies are tested using quantitative metrics such as accuracy and discovery rate. The system showed a high level of fraud detection, equal classification, a high Matthews correlation coefficient, and a false alarm level. The crime of stealing sensitive information, supplanting, grazing or stealing data on the part of the merchant, lost or stolen cards, producing fake or counterfeit cards, making a real site, and removing or replacing a magnetic line on the card keeping user information are all examples of credit card fraud. The study came to the conclusion that existing models have flaws and proposes a new technique for fixing them. Obstacles to fraud detection are expected to change and metamorphosize into hidden impediments in the future, based on how fraudsters do these illicit behaviors.

Recently, the problem of financial fraud and its adverse effects on the quality of information disclosed and on the financial system in general has gained increasing attention from the media, the general public and regulators. Thus, the detection of financial fraud remains essential for the stability and continuity of companies. The role of the external auditor in the detection of financial fraud has been widely demonstrated in theoretical and empirical research. The purpose of this paper is to emphasize the responsibility of auditors in detecting and preventing fraud and the effectiveness of current audit procedures. Through a review of the literature, we have revealed the important role of external auditors in the detection and prevention of financial fraud. We have further explored the main models of financial fraud detection most used in previous studies.

The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.

Money laundering has affected the global economy for many years, and there are several methods of solving it presented in the literature. However, when tackling money laundering and financial fraud together there are few methods for solving them. Thus, this study aims to identify methods for anti‐money laundering (AML) and financial fraud detection (FFD). A systematic literature review was performed for analysis and research of the methods used, utilizing the SCOPUS and Web of Science databases. Of the 48 articles that aligned with the research theme, 20 used quantitative methods for AML and FFD solution, 13 were literature reviews, 7 used qualitative methods, and 8 used mixed methods. This study contributes by presenting a systematic literature review that fills two research gaps: lack of studies on AML and FFD, and the methods used to solve them. This will assist researchers in identifying gaps and related research.