Download or read book Credit Card Fraud Detection Using Machine Learning with Integration of Contextual Knowledge written by Yvan Lucas and published by . This book was released on 2019 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The detection of credit card fraud has several features that make it a difficult task. First, attributes describing a transaction ignore sequential information. Secondly, purchasing behavior and fraud strategies can change over time, gradually making a decision function learned by an irrelevant classifier. We performed an exploratory analysis to quantify the day-by-day shift dataset and identified calendar periods that have different properties within the dataset. The main strategy for integrating sequential information is to create a set of attributes that are descriptive statistics obtained by aggregating cardholder transaction sequences. We used this method as a reference method for detecting credit card fraud. We have proposed a strategy for creating attributes based on Hidden Markov Models (HMMs) characterizing the transaction from different viewpoints in order to integrate a broad spectrum of sequential information within transactions. In fact, we model the authentic and fraudulent behaviors of merchants and cardholders according to two univariate characteristics: the date and the amount of transactions. Our multi-perspective approach based on HMM allows automated preprocessing of data to model temporal correlations. Experiments conducted on a large set of data from real-world credit card transactions (46 million transactions carried out by Belgian cardholders between March and May 2015) have shown that the proposed strategy for pre-processing data based on HMMs can detect more fraudulent transactions when combined with the Aggregate Data Pre-Processing strategy.

Download or read book Context aware Credit Card Fraud Detection written by Johannes Jurgovsky and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Credit Card Fraud Detection and Analysis Through Machine Learning written by Yogita Goyal and published by . This book was released on 2020-07-28 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Review on Credit Card Fraud Detection Using Data Mining Classification Techniques Machine Learning Algorithms written by Rahul Goyal and published by . This book was released on 2020 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining (DM) involves a core algorithm that enables data deeper than basic insights and knowledge. In fact, data mining is more part of knowledge discovery process. Credit card (CC) providers provide multiple cards to their customers. All credit card users must be genuine and sincere. Giving a card to any kind of mistake can lead to a financial crisis. Due to the rapid growth in cashless transactions, it is unlikely, Fake transactions can also be increased. A fraudulent transaction can be identified by studying credit cards of various behaviors as a previous transaction history data set. If there is any deviation from the available cost pattern, it is a bogus transaction. DM & machine learning techniques (MLT) are widely applied in credit card fraud detection (CCFD). In this survey paper we show an indication of various widely available DM & MLT for detecting credit card fraud.

Download or read book Fraud Analytics Using Descriptive Predictive and Social Network Techniques written by Bart Baesens and published by John Wiley & Sons. This book was released on 2015-08-17 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

Download or read book Anomaly Detection in Credit Card Transactions Using Machine Learning written by Meenu and published by . This book was released on 2020 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai. The Isolation Forest was not so much used and explored in the area of anomaly detection. The overall performance of the version evaluated primarily based on widely-accepted metrics: precision and recall. The test data used in our research come from Kaggle.

Download or read book Credit Card Fraud Detection Using Logistic Regression and Machine Learning Algorithms written by Haoyi Cheng and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is focused on detecting the probability of credit card fraud occurrence according to seven relative independent variables by using logistic regression, support vector machine, decision tree, and k-NN models. The dataset provided by Dhanush Narayanan R from Kaggle contains one million of data [1]. The final goal is to compare these four models and find the most accurate model.

Download or read book The Enhancement of Credit Card Fraud Detection Systems Using Machine Learning Methodology written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fraud Detection in Credit Cards Using Machine Learning written by Torphy Andres and published by . This book was released on 2023-04-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to thwart fraudsters, financial institutions must use current, advanced, customized predictive analytics to protect themselves. Data scientists and statisticians who understand machine learning and statistical methods are in increasingly high-demand and the demand for them is growing each year. Technically, machine learning is a subfield of artificial intelligence whereas statistics is subdivision of mathematics and many believe they only need in depth knowledge of one in order to be a predictive modeler. This fallacy leads to inefficient and/or inaccurate models, and sadly, many industries have not yet realized that the mathematics behind the model is just as important, if not more important, than the computer science needed to implement it. However, some businesses have and this thesis will hopefully help both industry and academia move further along in this direction.

Download or read book Future Issues with Credit Card Fraud Detection Techniques written by Marvin Namanda and published by GRIN Verlag. This book was released on 2016-05-20 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (undergraduate) from the year 2016 in the subject Business economics - Information Management, grade: 1, Federation University Australia, course: ITECH1006, language: English, abstract: Fraud is a contemporary ethical issue whose complexity is growing by day. The aims of this study are to identify the types of credit card fraud and to stipulate the future issues with the sector. The minor aim is to compare and analyze recent publication findings in future issues with credit card fraud detection. The significance of this paper is to allow the appreciation of the future issues with respect to credit card fraud detection techniques.

Download or read book The Enhancement of Credit Card Fraud Detection Systems Using Machine Learning Methodology written by Soheila Ehramikar and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In Canada, credit card fraud occurrences rose sharply in 1998 causing $147 million in losses. To address this problem, financial institutions (FIs) are employing preventive measures and fraud detection systems one of which is called FDS. Although FDS has shown good results in reducing fraud, the majority of cases being flagged by this system are 'False Positives ' resulting in substantial investigation costs and cardholder inconvenience. The possibilities of enhancing the current operation by introducing a post processing system constitute the objective of this research. The data used for the analysis was provided by one of the major Canadian banks. Based on variations and combinations of features and training class distributions, different sets of experiments were performed to explore the influence of these parameters on the performance of the prototype developed. The results indicate that the employed approach has a very good potential to improve on the existing system. However, further research is required including the development of prototype systems which should be enhanced by more extensive and informative data.

Download or read book Machine Learning Approach to Detect Fraudulent Banking Transactions written by Riwaj Kharel and published by GRIN Verlag. This book was released on 2022-09-22 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2022 in the subject Computer Sciences - Artificial Intelligence, grade: 3, University of Applied Sciences Berlin, course: Project management and Data Science, language: English, abstract: The study investigates whether a machine learning algorithm can be used to detect fraud attempts and how a fraud management system based on machine learning might work. For fraud detection, most institutions rely on rule-based systems with manual evaluation. Until recently, these systems had been performing admirably. However, as fraudsters become more sophisticated, traditional systems' outcomes are becoming inconsistent. Fraud usually comprises many methods that are used repeatedly that's why looking for patterns is a common emphasis for fraud detection. Data analysts can, for example, avoid insurance fraud by developing algorithms that recognize trends and abnormalities. AI techniques used to detect fraud include Data mining classifies, groups, and segments data to search through millions of transactions to find patterns and detect fraud. The scientific paper discusses machine learning methods to detect fraud detection with a case study and analysis of Kaggle datasets.

Download or read book Credit Card Fraud Detection Using Supervised Learning Algorithms written by Daniyal Baig and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Detecting Credit Card Fraud written by and published by . This book was released on 2020 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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's 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.

Download or read book Learning from Imbalanced Data Sets written by Alberto Fernández and published by Springer. This book was released on 2018-10-22 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Download or read book Data Balancing for Credit Card Fraud Detection Using Complementary Neural Networks and SMOTE Algorithm written by Vrushal Shah and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This Research presents an innovative approach towards detecting fraudulent credit card transactions. A commonly prevailing yet dominant problem faced in detection of fraudulent credit card transactions is the scarce occurrence of such fraudulent transactions with respect to legitimate (authorized) transactions. Therefore, any data that is recorded will always have a stark imbalance in the number of minority (fraudulent) and majority (legitimate) class samples. This imbalanced distribution of the training data among classes makes it hard for any learning algorithm to learn the features of the minority class. In this thesis work, we analyze the impact of applying class-balancing techniques on the training data namely oversampling (using SMOTE algorithm) for minority class and under sampling (using CMTNN) for majority class. The usage of most popular classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF) are processed on balanced data and which results to quantify the performance improvement provided by our approach. The experiments show that the hybrid approach which integrates Complementary Neural Network and Synthetic Minority Oversampling Technique gives a Quantitative performance in terms of Accuracy of 99% and 99.7% of AUC with Random Forest Classification Algorithm compared to simple undersampling and oversampling.

Download or read book Credit Card Fraud Detection Using Cortical Learning Algorithm written by Linda Oghenekaro and published by . This book was released on 2016-10-15 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: