Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This paper proposes a novel two-phase learning framework to address these challenges. In the first phase, the Isolation Forest algorithm performs unsupervised anomaly detection to identify potential fraud cases and reduce the volume of data requiring further analysis. In the second phase, a self-training Support Vector Machine (SVM) refines the predictions using both labeled and high-confidence pseudo-labeled samples, enabling robust semi-supervised learning. The proposed method is evaluated on the DataCo Smart Supply Chain Dataset, a comprehensive real-world supply chain dataset with fraud indicators. It achieves an F1-score of 0.817 while maintaining a false positive rate below 3.0%. These results demonstrate the effectiveness and efficiency of combining unsupervised pre-filtering with semi-supervised refinement for supply chain fraud detection under real-world constraints, though we acknowledge limitations regarding concept drift and the need for comparison with deep learning approaches.

In a transport network, the onboard occupancy is key for gaining insights into travelers' habits and adjusting the offer. Traditionally, operators have relied on field studies to evaluate ridership of a typical workday. However, automated fare collection (AFC) and automatic passenger counting (APC) data, which provide complete temporal coverage, are often available but underexploited. It should be noted, however, that each data source comes with its own biases: AFC data may not account for fraud, while not all vehicles are equipped with APC systems. This paper introduces the unified occupancy method, a geostatistical model to extrapolate occupancy to every course of a public transportation network by combining AFC and APC data with partial coverage. Unified occupancy completes missing APC information for courses on lines where other courses have APC measures, as well as for courses on lines where no APC data is available at all. The accuracy of this method is evaluated on real data from several public transportation networks in France.

The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. However, we argue that such a formulation can lead to sub-optimal solutions. The design requirements for these fraud models requires that they are robust to the high-class imbalance in the data, adaptive to changes in fraud patterns, maintain a balance between the fraud rate and the decline rate to maximize revenue, and be amenable to asynchronous feedback since usually there is a significant lag between the transaction and the fraud realization. To achieve this, we formulate fraud detection as a sequential decision-making problem by including the utility maximization within the model in the form of the reward function. The historical decline rate and fraud rate define the state of the system with a binary action space composed of approving or declining the transaction. In this study, we primarily focus on utility maximization and explore different reward functions to this end. The performance of the proposed Reinforcement Learning system has been evaluated for two publicly available fraud datasets using Deep Q-learning and compared with different classifiers. We aim to address the rest of the issues in future work.

The legislation lays out very specific thresholds for fraud. If a merchant's PSP's fraud rate is above these thresholds then the merchant will be required to challenge the user for another form of authentication. This will open up an opportunity for competitive advantage in the payments market for those PSPs whose fraud rates are below the threshold. For this reason, Ravelin grasped the opportunity and it has extended machine learning capabilities to PSPs to ensure their merchants are below the PSD2 fraud thresholds. The company's product Ravelin Enterprise uses machine learning models to score a merchant's every customer interaction for fraud risk, forming a complete picture of a shopper's risk profile before they reach checkout.

Using advanced algorithms, Mitra captures over 200 parameters and flags every transaction as red or green in real time. According to global information services company Experian's Asia Pacific Fraud Insights Report – 2017, of the 10 countries analysed in Asia Pacific, Indian consumers are the most vulnerable to online frauds. The results were based on analysis of fraud trends, and 48 percent of Indian consumers have experienced fraud at the retail level, either directly or indirectly, the report said. The report also highlighted that frauds are on the rise in all segments of lending and the highest frauds are in consumer loans (2.2 percent), credit cards (0.8 percent) and personal loans (0.72 percent). Gurugram-based ThirdWatch aims to change this scenario.

Stripe builds products that enable hundreds of thousands of e-commerce companies, SaaS businesses, on-demand marketplaces, nonprofits, and platforms to conduct business online. One inescapable facet of online commerce--and one that, unfortunately, frequently comes as an unpleasant surprise--is fraud. Unlike businesses that accept payments in person, internet businesses are liable for fraudulent purchases--this despite the fact that they are no more experts on fraud than their brick-and-mortar counterparts. As a result, many internet businesses have had to build up teams of fraud analysts and expend engineering effort on fraud detection systems. At Stripe, we want to help businesses focus on their product and customer experiences and not on fraud, so we've developed Stripe Radar, a suite of modern tools for fraud detection and prevention. The goal of this guide is to provide more detail on the machine learning that powers the core of Radar, explain how we think about the efficacy and performance of fraud detection systems, and describe how other tools in the Radar suite can help businesses optimize their outcomes.