Every second, Fraud Detection Systems (FDS) check streams of thousands of credit or debit card transactions. Most of the models in production and in the literature rely on batch learning, wasting part of the most recent data. Incremental learning may be beneficial in terms of computational/architectural cost and confidentiality (e.g. avoiding the storage of sensitive data). The full paper focuses on two questions related to the use of incremental learning for FDS: (1) Can incremental learning be as competitive as batch and retraining approaches? (2) Can combining incremental, ensembles, diversity, and transfer learning lead to efficient models under concept drift? We investigate those elements and provide an experimental evaluation on a real-life case study including more than 150 days of e-commerce transactions (or 50 million transactions).

The state-of-the-art feature-engineering method for fraud classification of electronic payments uses manually engineered feature aggregates, i.e. descriptive statistics of the transaction history. However, this approach has limitations, primarily that of being dependent on expensive human expert knowledge. There have been attempts to replace manual aggregation through automatic feature extraction approaches. They, however, do not consider the specific structure of the manual aggregates. In this paper, we define the novel Neural Aggregate Generator (NAG), a neural network based feature extraction module that learns feature aggregates end-to-end on the fraud classification task. In contrast to other automatic feature extraction approaches, the network architecture of the NAG closely mimics the structure of feature aggregates. Furthermore, the NAG extends learnable aggregates over traditional ones through soft feature value matching, and relative weighting of the importance of different feature constraints. We compare the performance of the NAG to the state-of-the-art approaches on a real-world dataset with millions of transactions. More precisely, we show that features generated with the NAG lead to improved results over manual aggregates for fraud classification, thus demonstrating its viability to replace them. Moreover, we compare the NAG to other automatic approaches such as the LSTM or a generic CNN. Here we also observe improved results, with the NAG requiring far less parameters. Unlike the LSTM or the CNN, our approach also provides further interpretability through the inspection of its parameters.

Fraud detection is an important research area where machine learning has a significant role to play. An important task in that context, on which the quality of the results obtained depends, is feature engineering. Unfortunately, this is very time and human consuming. Thus, in this article, we present the DuSVAE model that consists of a generative model that takes into account the sequential nature of the data. It combines two variational autoencoders that can generate a condensed representation of the input sequential data that can then be processed by a classifier to label each new sequence as fraudulent or genuine. The experiments we carried out on a large real-word dataset, from the Worldline company, demonstrate the ability of our system to better detect frauds in credit card transactions without any feature engineering effort.

Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall). Heavily dependent on the class prior, such metrics make it difficult to interpret the variation of a model’s performance over different subpopulations/subperiods in a dataset. In this paper, we propose a way to calibrate the metrics so that they can be made invariant to the prior. We conduct a large number of experiments on balanced and imbalanced data to assess the behavior of calibrated metrics and show that they improve interpretability and provide a better control over what is really measured. We describe specific real-world use-cases where calibration is beneficial such as, for instance, model monitoring in production, reporting, or fairness evaluation.

Anomaly detection is a research area where machine learning can have a significant impact. Many efforts have been made in this area for years and conference proceedings are full of outstanding work presenting algorithms that are all more efficient than each other. Nevertheless, this paper states that it is just as important to consider in depth the datasets on which we will work later as it is to develop high-performance algorithms. Thus, in this paper, we present the context of a real case of anomaly detection and what work needs to be done on the dataset well before considering the development of new algorithms to process it. We expect that this work and various general references provided can be useful for many other researchers who will have to work on real datasets and not only on datasets from famous repositories that have already been pre-processed and for which we know all the characteristics.

The inferences of a machine learning algorithm are naturally limited by the available data. In many real-world applications, the provided internal data is domain-specific and we use external background knowledge to derive or add new features. Semantic networks, like linked open data, provide a largely unused treasure trove of background knowledge. This drives a recent surge of interest in unsupervised methods to automatically extract such semantic background knowledge and inject it into machine learning algorithms. In this work, we describe the general process of extracting knowledge from semantic networks through vector space embeddings. The locations in the vector space then reflect relations in the original semantic network. We perform this extraction for geographic background knowledge and inject it into a neural network for the complicated real-world task of credit-card fraud detection. This improves the performance by 11.2%.