A compiler providing incremental scalability for web applications

International audienceTo develop a web application, one needs to choose between two programming models. The monolithic one favors features improvements, while the decentralized one favors performance improvements. To avoid this choice, we compile monolithic web applications into a high-level language compliant with a distributed model

Incremental learning strategies for credit cards fraud detection.

very second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies used in production are still based on batch learning, which is inadequate for two reasons: Models quickly become outdated and require sensitive data storage. The evolving nature of bank fraud enshrines the importance of having up-to-date models, and sensitive data retention makes companies vulnerable to infringements of the European General Data Protection Regulation. For these reasons, evaluating incremental learning strategies is recommended. This paper designs and evaluates incremental learning solutions for real-world fraud detection systems. The aim is to demonstrate the competitiveness of incremental learning over conventional batch approaches and, consequently, improve its accuracy employing ensemble learning, diversity and transfer learning. An experimental analysis is conducted on a full-scale case study including five months of e-commerce transactions and made available by our industry partner, Worldline

Transfer Learning Strategies for Credit Card Fraud Detection.

Credit card fraud jeopardizes the trust of customers in e-commerce transactions. This led in recent years to major advances in the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with short reaction time and high precision. Nevertheless, the heterogeneous nature of the fraud behavior makes it difficult to tailor existing systems to different contexts (e.g. new payment systems, different countries and/or population segments). Given the high cost (research, prototype development, and implementation in production) of designing data-driven FDSs, it is crucial for transactional companies to define procedures able to adapt existing pipelines to new challenges. From an AI/machine learning perspective, this is known as the problem of transfer learning. This paper discusses the design and implementation of transfer learning approaches for e-commerce credit card fraud detection and their assessment in a real setting. The case study, based on a six-month dataset (more than 200 million e-commerce transactions) provided by the industrial partner, relates to the transfer of detection models developed for a European country to another country. In particular, we present and discuss 15 transfer learning techniques (ranging from naive baselines to state-of-the-art and new approaches), making a critical and quantitative comparison in terms of precision for different transfer scenarios. Our contributions are twofold: (i) we show that the accuracy of many transfer methods is strongly dependent on the number of labeled samples in the target domain and (ii) we propose an ensemble solution to this problem based on self-supervised and semi-supervised domain adaptation classifiers. The thorough experimental assessment shows that this solution is both highly accurate and hardly sensitive to the number of labeled samples

Toward automatic update from callbacks to Promises

International audienceJavascript is the prevalent scripting language for the web. It lets web pages register callbacks to react to user events. A callback is a function to be invoked later with a result currently unavailable. This pattern also proved to respond efficiently to remote requests. Javascript is currently used to implement complete web applications. However, callbacks are ill-suited to arrange a large asynchronous execution flow. Promises are a more adapted alternative. They provide a unified control over both the synchronous and asynchronous execution flows. The next version of Javascript proposes to replace callbacks with Promises. This paper brings the first step toward a compiler to help developers prepare this shift. We present an equivalence between callbacks and Dues. The latter are a simpler specification of Promises developed for the purpose of this demonstration. From this equivalence, we implement a compiler to transform an imbrication of callbacks into a chain of Dues. This equivalence is limited to Node.js-style asynchronous callbacks declared in situ. We evaluate our compiler over 64 npm packages, 9 of them present compatible callbacks and compile successfully. We consider this shift to be a first step toward the merge of concepts from the data-flow programming model into the imperative programming model

Memorandum of evidence for the Health Committee inquiry into NHS responsibilities for meeting continuing health care needs

SIGLEAvailable from British Library Document Supply Centre- DSC:q95/21802 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Anomaly Detection, Consider your Dataset First, An illustration on Fraud Detection

International audienc

Combining unsupervised and supervised learning in credit card fraud detection

Supervised learning techniques are widely employed in credit card fraud detection, as they make use of the assumption that fraudulent patterns can be learned from an analysis of past transactions. The task becomes challenging, however, when it has to take account of changes in customer behavior and fraudsters’ ability to invent novel fraud patterns. In this context, unsupervised learning techniques can help the fraud detection systems to find anomalies. In this paper we present a hybrid technique that combines supervised and unsupervised techniques to improve the fraud detection accuracy. Unsupervised outlier scores, computed at different levels of granularity, are compared and tested on a real, annotated, credit card fraud detection dataset. Experimental results show that the combination is efficient and does indeed improve the accuracy of the detection.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Incremental learning strategies for credit cards fraud detection

Every second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies used in production are still based on batch learning, which is inadequate for two reasons: Models quickly become outdated and require sensitive data storage. The evolving nature of bank fraud enshrines the importance of having up-to-date models, and sensitive data retention makes companies vulnerable to infringements of the European General Data Protection Regulation. For these reasons, evaluating incremental learning strategies is recommended. This paper designs and evaluates incremental learning solutions for real-world fraud detection systems. The aim is to demonstrate the competitiveness of incremental learning over conventional batch approaches and, consequently, improve its accuracy employing ensemble learning, diversity and transfer learning. An experimental analysis is conducted on a full-scale case study including five months of e-commerce transactions and made available by our industry partner, Worldline.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Assessment of catastrophic forgetting in continual credit card fraud detection

The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud detection, in collaboration with our industrial partner, Worldline. Transactional companies are more and more dependent on machine learning models such as deep learning anomaly detection models, as part of real-world fraud detection systems (FDS). We focus on continual learning to find the best model with respect to two objectives: to maximize the accuracy and to minimize the catastrophic forgetting phenomenon. For the latter, we proposed an evaluation procedure to quantify the forgetting in data streams with delayed feedback: the plasticity/stability visualization matrix. We also investigated six strategies and 13 methods on a real-size case study including five months of e-commerce credit card transactions. Finally, we discuss how the trade-off between plasticity and stability is set, in practice, in the case of FDS.SCOPUS: ar.jinfo:eu-repo/semantics/publishe