Iva Popova Hamza A. A. Gardi ETIT - KIT, Germany IIIT at ETIT - KIT, Germany Abstract Credit card fraud remains a significant challenge due to class imbalance and fraudsters mimicking legitimate behavior. This study evaluates five machine learning models - Logistic Regression, Random Forest, XGBoost, K - Nearest Neighbors (KNN), and Multi - Lay er Perceptron (MLP) on a real - world dataset using undersampling, SMOTE, and a hybrid approach. Our models are evaluated on the original imbalanced test set to better reflect real - world performance. Results show that the hybrid method achieves the best bala nce between recall and precision, especially improving MLP and KNN performance. I ntroduction Financial fraud is a significant issue that has been continuously increasing over the past few years due to the ever - growing volume of online transactions conduc ted with credit cards. Credit card fraud (CCF) refers to a type of fraud in which an individual other than the cardholder unlawfully conducts transactions using a card that is stolen, lost, or otherwise misused [ 1 ]. CCF has resulted in billions of dollars in losses for banks and other online payment platforms. According to the Federal Trade Commission (FTC), there were 449,076 reports of CCF in 2024, representing a 7.8% increase from the previous year [ 2 ]. Given this trend, new methods must be employed to c apture patterns and dependencies in the data.

Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process, researchers have in the past shown interest in automatically detecting fraud through ERP system data. Current research in this area, however, is hindered by the fact that ERP system data is not publicly available for the development and comparison of fraud detection methods. We therefore endeavour to generate public ERP system data that includes both normal business operation and fraud. We propose a strategy for generating ERP system data through a serious game, model a variety of fraud scenarios in cooperation with auditing experts, and generate data from a simulated make-to-stock production company with multiple research participants. We aggregate the generated data into ready to used datasets for fraud detection in ERP systems, and supply both the raw and aggregated data to the general public to allow for open development and comparison of fraud detection approaches on ERP system data.

A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit.

Credit card frauds are at an ever-increasing rate and have become a major problem in the financial sector. Because of these frauds, card users are hesitant in making purchases and both the merchants and financial institutions bear heavy losses. Some major challenges in credit card frauds involve the availability of public data, high class imbalance in data, changing nature of frauds and the high number of false alarms. Machine learning techniques have been used to detect credit card frauds but no fraud detection systems have been able to offer great efficiency to date. Recent development of deep learning has been applied to solve complex problems in various areas. This paper presents a thorough study of deep learning methods for the credit card fraud detection problem and compare their performance with various machine learning algorithms on three different financial datasets. Experimental results show great performance of the proposed deep learning methods against traditional machine learning models and imply that the proposed approaches can be implemented effectively for real-world credit card fraud detection systems.

Fraud detection is the most important step for a risk management process to prevent a recurrence. High volumes of fraud can be damaging revenue and reputation. Fortunately, it is possible to deal with fraud before it happens. Therefore, I would like to investigate the performance of the machine learning algorithms on a credit card fraud data set. The dataset contains transactions made by credit cards in September 2013 by European cardholders.

This is the last blogpost of this series. We've already talked about conceptual model targets and model performance targets, now it is time to discuss the importance of data in building and evaluating models. More specifically, we will talk about three things: data quality, splitting data for evaluation, and sampling. Before we jump in, let me remind you that in the context of today's post, a model refers to a decision-generating process that applies logical or statistical techniques to transform the data it is provided into a meaningful output. I'll start with the obvious: good data quality is the foundation for producing accurate (and useful) findings from modelling.

Medicare fraud is quite, unfortunately, an ongoing epidemic according to a recent study. Machine learning has recently become a very useful tool in rooting out a variety of Medicare fraud that has been occurring, however. It's estimated that Medicare fraud is responsible for almost $65 billion in losses each year. With AI going through a wide range of cases it could be possible to prevent some of these effects from happening. According to researchers at Florida Atlantic University, it may be possible to use machine learning to identify instances of fraud effectively.

Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.

Robot investigators could be widely used in future to examine documents in complex cases, the head of the Serious Fraud Office (SFO) has suggested. David Green said he would like to see the possibility of employing artificial intelligence "carefully examined" after using technology to sift through a cache of 30 million documents disclosed by Rolls-Royce during a major investigation. He also said it was now "pretty clear" that his agency would continue as an independent body after the government dropped plans to have it taken into the National Crime Agency. The SFO director set out how the Rolls-Royce documents had been examined by a computer algorithm which had the ability to learn as it went along. The technology was trying to find legally privileged documents which could not be used in the case, but Mr Green suggested that in future similar methods could be used to identify useful evidence in investigations.

Teradata has announced today that Danske Bank, a financial services leader in the Nordics, has worked with Think Big Analytics, a Teradata company, to create and launch a state-of-art, AI-driven fraud detection platform that is already expected to meet 100 percent ROI in its first year of production. The engine uses machine leaning to analyze tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. By significantly reducing the cost of investigating false-positives, Danske Bank increases its overall efficiency and is now poised for substantial savings. "Application fraud is a critical, top of the agenda issue for banks, and there is evidence that criminals are becoming savvier by the day; employing sophisticated machine learning techniques to attack, so it's critical to use advanced techniques, such as machine learning to catch them," said Nadeem Gulzar, Head of Advanced Analytics, Danske Bank. "The bank understands that fraud is set to get worse in the near and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications. We recognize the need to use cutting-edge techniques to engage fraudsters not where they are today, but where they will be tomorrow. Using AI, we've already reduced false positives by 50 percent and as such have been able to reallocate half the fraud detection unit to higher value responsibilities."