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The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities.

Companies have been involved in scandals relating to financial fraud which present a large financial loss to their stakeholders. As high technology devices and online systems makes accounting transactions more complicated and easier to manipulate, process mining techniques to detecting fraud are under intense scrutiny by forensic auditors. The author analyses data of a Dutch Financial institute proposed by the Business Process Intelligence Challenge (BPIC) 2017, the BPIC provides a real life event logs. The process consists of 193849 events and 42995 cases. Various tools such as ProM (Process Mining) are been used for data analysis. The author choose process mining over other techniques because the data is recorded independently from the auditee, it an automated solution, less prone to human error, and less time consuming.

Nowadays, credit card fraud has become one of the most complex and vital issues in the world, even more than the past decades. Widespread use of credit cards is one of the most attractive forms of online transactions in the banking sector. Credit cards’ attractiveness is the ease of life for people, which allows customers to use their credit at any time, place, and amount, without carrying cash and without the hassle of carrying cash. This is to make it easy to pay for purchases made via the Internet, mobile phones, Automated teller machines (ATMs), etc. Meanwhile, financial information acts as the main factor of financial transactions in the market. Due to the popularity of using credit cards, various security challenges are increasing, and this issue has intensified fraud intending to obtain unauthorized financial benefits. Researchers have proposed different solutions for detecting and predicting credit card fraud, which has been successful. One of these methods is data mining and machine learning. The issue of accuracy in predicting problems is vital in this regard. In this study, we examine Ensemble Learning methods, including gradient boosting(LightGBM and LiteMORT), and combine them by averaging methods(Simple and Weighted Averaging methods) and then evaluate them. Combining these methods reduces error rates and increases efficiency and accuracy. By evaluating the models by Area under the curve(AUC), Recall, F1-score, Precision, and Accuracy criteria, we reached the best results of 95.20, 90.65, 91.67, 92.79, and 99.44 for the combination of LightGBM and LiteMORT using weighted averaging, respectively.

Financial statement fraud has been on the increase in the past two decades and includes prominent scandals such as Enron, WorldCom and more recently in South Africa, Steinhof. These scandals have led to billions of dollars being lost in the form of market capitalisation from diferent stock exchanges across the world. During this time, there has been an increase in the literature on applying automated methods to detecting financial statement fraud using publicly available data. This paper provides a survey of the literature on automated ifnancial statement fraud detection and identifies current gaps in the literature. The paper highlights a number of important considerations in the implementation of financial statement fraud detection decision support systems, including 1) the definition of fraud, 2) features used for detecting fraud, 3) region of the case study, dataset size and imbalance, 4) algorithms used for detection, 5) approach to feature selection / feature engineering, 6) treatment of missing data, and 7) performance measure used. The current study discusses how these and other implementation factors could be approached within the South African context.

Debit and credit card payments have become the preferred method of payment for consumers, replacing paper checks and cash. However, this shift has also led to an increase in concerns regarding identity theft and payment security. To address these challenges, it is crucial to develop an effective, secure, and reliable payment system. This research presents a comprehensive study on payment card fraud detection using deep learning techniques. The introduction highlights the significance of a strong financial system supported by a quick and secure payment system. It emphasizes the need for advanced methods to detect fraudulent activities in card transactions. The proposed methodology focuses on the conversion of a comma-separated values (CSV) dataset into quick response (QR) code images, enabling the application of deep neural networks and transfer learning. This representation enables leveraging pre-trained image-based architectures by encoding numeric transaction attributes into visual patterns suitable for convolutional neural networks. The feature extraction process involves the use of a convolutional neural network, specifically a residual network architecture. The results obtained through the under-sampling dataset balancing method revealed promising performance in terms of precision, accuracy, recall, and F1 score for the traditional models such as K-nearest neighbors (KNN), Decision Tree, Random Forest, AdaBoost, Bagging, and Gaussian Naïve Bayes. Furthermore, the proposed deep neural network model achieved high precision, indicating its effectiveness in detecting card fraud. The model also achieved high accuracy, recall, and F1 score, showcasing its superior performance compared to traditional machine learning models. In summary, this research contributes to the field of payment card fraud detection by leveraging deep learning techniques. The proposed methodology offers a sophisticated approach to detecting fraudulent activities in card payment systems, addressing the growing concerns of identity theft and payment security. By deploying the trained model in an Android application, real-time fraud detection becomes possible, further enhancing the security of card transactions. The findings of this study provide insights and avenues for future advancements in the field of payment card fraud detection.

Healthcare insurance fraud is a major problem, with an estimated $300 billion lost annually in the United States. Machine learning has been explored as a tool for fraud detection for over a decade, but challenges remain, including class imbalance, dataset diversity, and model interpretability. This survey reviews 22 supervised, unsupervised, and semi-supervised classification techniques published between December 2017 and October 2024 which are novel within the healthcare fraud detection domain. Supervised techniques, which make up a majority of the identified works, are divided into deep learning, graph-based, and meta-learning methods. We also find a small set of novel unsupervised and semi-supervised classifiers, which we examine with a focus on model explainability. We find that deep learning is a popular and effective approach for supervised learning, while research gaps remain in areas such as transfer learning and incremental learning for all three classification approaches. Most concerningly, we find few to no works which establish benchmarks across diverse techniques. We assert that addressing these gaps could lead to more effective, transparent, and adaptable fraud detection systems in healthcare insurance.

We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as a time series classification task. As financial transaction data streams grow in scale and complexity, traditional methods often require large labeled datasets and struggle with time series of irregular sampling frequencies and varying sequence lengths. To address these challenges, we extend conditional Generative Adversarial Networks (GANs) for targeted data augmentation, integrate Bayesian inference to obtain predictive distributions and quantify uncertainty, and leverage log-signatures for robust feature encoding of transaction histories. We propose a composite Wasserstein distance-based loss to align generated and real unlabeled samples while simultaneously maximizing classification accuracy on labeled data. Our approach is evaluated on the BankSim dataset, a widely used simulator for credit card transaction data, under varying proportions of labeled samples, demonstrating consistent improvements over benchmarks in both global statistical and domain-specific metrics. These findings highlight the effectiveness of GAN-driven semi-supervised learning with log-signatures for irregularly sampled time series and emphasize the importance of uncertainty-aware predictions.

In the financial industry, financial fraud is one of the ever-growing hazards with far concerns. Financial statements are the fundamental papers which replicate economic position of a corporation. Users of the financial information like public, creditors etc. are the major foundations of a decision-making process for financing stakeholders. Financial fraud has extremely damaged the sustainable growth of financial markets and enterprises. The amount of financial reporting fraud cases keeps on developing. Each incident is a thick hit to partners, banks, and financial specialists and it costs human progress significantly. One of the serious issues is to recognize the financial reporting fraud by utilizing formation of an active model. The aim of this paper is to identifying frauds using various text mining techniques and guard, the public's investments. This investigation will benefit auditors and financial governors.

PurposeThe best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO.Design/methodology/approachIn this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance, it will be the next parent, and the current parent becomes obsolete. Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.FindingsResults showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure.Originality/valueIn this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.