DESIGN AND SIMULATION OF AN EFFICIENT MODEL FOR CREDIT CARDS FRAUD DETECTION

In this study a model which can improve the accuracy and reliability of credit card fraud detection was proposed. This is with a few to mitigating contentious issues regarding online transaction of credit card, such as  amount of transactions that have resulted in payment default and the number of credit card fraud cases that have been recorded, all of which have put the economy in jeopardy.   To address this challenge,sample dataset was sourced from online repository database of Kaggle. The feature extraction on the data was performed using Principal Component Analysis (PCA). The credit card fraud detection model was designed using Neuro-fuzzy logic technique, clustering was done using Hierarchical Density Based Spatial Clustering of Application with Noise (HDBSCAN) .The simulation of the proposed model was done in Python programming environment.The performance evaluation of the model was carried out by comparing the proposed model with Neuro-Fuzzy (NF) technique using performance metrics such as precision, recall, F1-score and accuracy.  The simulation result showed that the proposed model (NF + HDBSCAN) had precision of 98.75%, recall of 98.70%, F1-Score of 97.65% and accuracy 99.75% . NF had Precision of 94.60%, recall of 94.50%, F1-Score of 95.50% and accuracy 95.70% using training dataset. Likewise, when test dataset were used, the proposed (NF + HDBSCAN) had precision of 93.50%, recall of 95.50%, F1-Score of 94.50% and accuracy 95.50%. NF had Precision of 92.50%, recall of 93.00%, F1-Score of 94.00% and accuracy 93.50%.  The simulation results of the proposed model was viable, reliable and showed possibility of being designed as module which could be  integrated into the existing credit card design for lowering fraud rate and assisting fraud investigators

Mining Bad Credit Card Accounts from OLAP and OLTP

Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as "good" when it is actually bad, could lead to loss of revenue - and marking an account as "bad" when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification.Comment: Conference proceedings of ICCDA, 201

A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection

Now a day’s credit card transactions have been gaining popularity with the growth of e-commerce and shows tremendous opportunity for the future. Therefore, due to surge of credit card transaction, it is a crying need to secure it . Though the vendors and credit card providing authorities are showing dedication to secure the details of these transactions, researchers are searching new scopes or techniques to ensure absolute security which is the demand of time. To detect credit card fraud, along with other technologies, applications of machine learning and computational intelligence can be used and plays a vital role. For detecting credit card anomaly, this paper analyzes and compares some popular classifier algorithms. Moreover, this paper focuses on the performance of the classifiers. UCSD -FICO Data Mining Contest 2009 dataset were used to measure the performance of the classifiers. The final results of the experiment suggest that (1) meta and tree classifiers perform better than other types of classifiers, (2) though classification accuracy rate is high but fraud detection success rate is low. Finally, fraud detection rate  should be taken into consideration to assess the performance of the classifiers in a credit card fraud detection system

Unsupervised quantum machine learning for fraud detection

We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD). First, we establish classical benchmarks based on supervised and unsupervised machine learning methods, where average precision is chosen as a robust metric for detecting anomalous data. We focus on kernel-based approaches for ease of direct comparison, basing our unsupervised modelling on one-class support vector machines (OC-SVM). Next, we employ quantum kernels of different type for performing anomaly detection, and observe that quantum FD can challenge equivalent classical protocols at increasing number of features (equal to the number of qubits for data embedding). Performing simulations with registers up to 20 qubits, we find that quantum kernels with re-uploading demonstrate better average precision, with the advantage increasing with system size. Specifically, at 20 qubits we reach the quantum-classical separation of average precision being equal to 15%. We discuss the prospects of fraud detection with near- and mid-term quantum hardware, and describe possible future improvements.Comment: 7 pages, 4 figure

Detecting credit card fraud: An analysis of fraud detection techniques

Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced machine learning techniques and other supportive algorithms to detect and prevent fraud in the future. This work analyzes the various machine learning techniques for their ability to efficiently detect fraud and explores additional state-of-the-art techniques to assist with their performance. This work also proposes a generalized strategy to detect fraud regardless of a dataset\u27s features or unique characteristics. The high performing models discovered through this generalized strategy lay the foundation to build additional models based on state-of-the-art methods. This work expands on the issues of fraud detection, such as missing data and unbalanced datasets, and highlights models that combat these issues. Furthermore, state-of-the-art techniques, such as adapting to concept drift, are employed to combat fraud adaptation

End-to-end neural network architecture for fraud scoring in card payments

[EN] Millions of euros are lost every year due to fraudulent card transactions. The design and implementation of efficient fraud detection methods is mandatory to minimize such losses. In this paper, we present a neural network based system for fraud detection in banking systems. We use a real world dataset, and describe an end-to-end solution from the practitioner's perspective, by focusing on the following crucial aspects: unbalancedness, data processing and cost metric evaluation. Our analysis shows that the proposed solution achieves comparable performance values with state-of-the-art proprietary and costly solutions. (c) 2017 Elsevier B.V. All rights reserved.Gomez, J.; Arévalo, J.; Paredes Palacios, R.; Nin, J. (2018). End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognition Letters. 105:175-181. https://doi.org/10.1016/j.patrec.2017.08.024S17518110

A New Generative Adversarial Network for Improving Classification Performance for Imbalanced Data

Data is a common issue in many industries, particularly in fields such as fraud detection and medical diagnosis. Imbalanced data refers to datasets where the distribution of classes is not equal, resulting in an over- representation of one class and an under-representation of another. This can lead to biassed and inaccurate machine learning models, as the algorithm may be inclined to favour the majority class and overlook important patterns in the minority class. Various sectors have utilised deep neural networks for data synthesis. However, according to research papers in these fields, balanced data outperforms imbalanced data when it comes to deep neural networks. Although deep generative approaches, such as Generative Adversarial Networks (GANs), are an efficient method of augmenting high-dimensional data, there is a lack of research on their effectiveness with credit card or breast cancer data and the current methods demonstrate limitations. Our research focuses on obtaining a great number of sets of data that are valid and resemble the minority class, in this case, fraudulent or malignant samples. Having more data like this can be used to train a binary classifier so it's effective against fraud or cancer diagnosis. To overcome challenges opposed to existing methods we have developed a novel GAN-based method called K-CGAN, which has been tested on credit card fraud and breast cancer data. K- CGAN is designed to generate synthetic data that resembles the minority class, effectively balancing the dataset and improving the performance of binary classifiers. Our research demonstrates the effectiveness of K-CGAN in handling complex data imbalance problems often encountered in practical applications. In addition, the experiments performed on different datasets indicate that K-CGAN can be used for various purposes. The application of machine learning algorithms in various industries has become increasingly popular in recent years. However, the quality and quantity of available data are crucial factors that directly impact the accuracy and reliability of these models. The scarcity and imbalance of datasets in certain domains pose challenges for researchers and practitioners, and the need for effective solutions is more pressing than ever. In this context, K- CGAN provides a promising approach to address data imbalance and improve the performance of machine learning models. Our results show that K-CGAN can be applied to different datasets with different characteristics, making it a valuable tool for data scientists and practitioners in various fields

Credit card fraud detection using a hierarchical behavior-knowledge space model

Data Availability: All relevant benchmark data are within the manuscript, given in references [24], [25], and [26]. Relevant real data records are available from a public repository: https://doi.org/10.6084/m9.figshare.17030138.Copyright: © 2022 Nandi et al. With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.Funding: The author(s) received no specific funding for this work