Modelling Customer Behaviour with Topic Models for Retail Analytics

Topic modelling is a scalable statistical framework that can model highly dimensional grouped data while keeping explanatory power. In the domain of grocery retail analytics, topic models have not been thoroughly explored. In this thesis, I show that topic models are powerful techniques to identify customer behaviours and summarise customer transactional data, providing valuable commercial value. This thesis has two objectives. First, to identify grocery shopping patterns that describe British food consumption, taking into account regional diversity and temporal variability. Second, to provide new methodologies that address the challenges of training topic models with grocery transactional data. These objectives are fulfilled across 3 research parts. In the first part, I introduce a framework to evaluate and summarise topic models. I propose to evaluate topic models in four aspects: generalisation, interpretability, distinctiveness and credibility. In this manner, topic models should represent the grocery transactional data fairly, providing coherent, distinctive and highly reliable grocery themes. Using a user study, I discuss thresholds that guide interpretation of topic coherence and similarity. We propose a clustering methodology to identify topics of low uncertainty by fusing multiple posterior samples. In the second part, I reinterpret the segmented topic model (STM) to accommodate grocery store metadata and identify spatially driven customer behaviours. This novel application harnesses store hierarchy over transactions to learn topics that are relevant within stores due to customised product assortments. Linear Gaussian Process regression complements the analysis to account for spatial autocorrelation and to investigate topics' spatial prevalence across the United Kingdom. In the third part, I propose a variation of the STM, the Sequential STM (SeqSTM), to accommodate time sequence over transactions and to learn time-specific customer behaviours. This model is inspired by the STM and the dynamic mixture model (DMM); however, the former does not naturally account for temporal sequence and the latter does not accommodate transactions' dependency on time variables. SeqSTM is suitable for learning topics where product assortment varies with respect to time, and where transactions are exchangeable within time slices. In this thesis, I identify customer behaviours that characterise British grocery retail. For instance, topics reveal natural groups of products that are used in the preparation of specific dishes, convey diets or outdoor activities, that are characteristic of festivities, household or pet ownership, that show a preference for brands, price or quality, etc. I have observed that customer behaviours vary regionally due to product availability and/or preference for specific products. In this manner, each constitutional country of the UK, the northern and the southern regions of England and London show a preference for different products. Finally, I show that customer behaviours may respond to seasonal product availability and/or are motivated by seasonal weather. For instance, consumption of tropical fruits around summer and of high-calorie foods during cold months

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

A review of clustering techniques and developments

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Cross channel fraud detection framework in financial services using recurrent neural networks

The reliability and performance of real time fraud detection techniques has been a major concern for the financial institutions as traditional fraud detection models couldn’t cope with the emerging new and innovative attacks that deceive banks. The problems are further exacerbated with evolving customer behaviour as existing fraud detection models unable to cope with class imbalance problem and longer feedback loop. This thesis looks at the holistic view of fraud detection and proposes a conceptual fraud detection framework that can detect anomalous transaction quickly and accurately, as well as dynamically evolve to maintain the efficiency with minimum input from subject matter expert. The framework is used to analyse Internet Banking (IB) transactions and contextual information to reduce the false positives and improve fraud detection rates. Based on the proposed framework, Long Short-Term Memory (LSTM) based Recurrent Neural Network model for detecting fraud in remote banking is implemented and performance is evaluated against Support Vector Machine (SVM) and Markov models. The main research element is to model events as state vectors so that sequence-based learning can be applied, followed by a weak classifier to deal with noise. Firstly, the study focuses on Feature Engineering where along raw attributes such as IP Address, Amount and other, two novel features for remote banking fraud are evaluated, i.e., the time spend on a page and the time between page transition. The second focus is on modelling which is performed on an anonymised real-life dataset, provided by a large financial institution in Europe. The results of the modelling demonstrate that given the labelled dataset all models can detect payment fraud with acceptable accuracy. Various tests proved that the LSTM model achieves a F1 score of 97.7% whereas the SVM and Markov model achieve 93.5% and 95.0% respectively. As the time elapsed, the LSTM model performance significantly improves as the sequence of events became larger. As the dataset increases that time it takes to train traditional models becomes a bottleneck. This proves the hypothesis that the events across banking channels can be modelled as time series data and then sequence-based learners such as Recurrent Neural Network (RNN) can be applied to improve or reduce the False Positive Rate (FPR) and False Negative Rate (FNR)