Australia’s largest bank, Commonwealth Bank (CBA) has a large data and analytics function that focuses on building a brighter future for all using data and decision science. In this work, we focus on creating better services for CBA customers by developing a next generation recommender system that brings the most relevant merchant reward offers that can help customers save money. Our recommender provides CBA cardholders with cashback offers from merchants, who have different objectives when they create offers. This work describes a multi-stakeholder, multi-objective problem in the context of CommBank Rewards (CBR) and describes how we developed a system that balances the objectives of the bank, its customers, and the many objectives from merchants into a single recommender system.

Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data. In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data. We explore how the extracted information can be combined with the static features in order to improve the classification performance. We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.

A key problem for facilitators of online communication and social networks is to identify users whose activity is likely to change in the near future. Such predictions may serve as basis for targeted campaigns aimed at sustaining or increasing overall user engagement in the network. A common approach to this problem is to apply machine learning methods to make predictions at the level of individuals. These approaches consider only information about each individual user and, thus, do not exploit the social connections and structure of the network. In this paper, we approach the problem of activity change prediction at the level of communities rather than individuals. We develop predictive models of activity change over communities obtained using state-of-art community detection methods and compare their predictive power with each other and against the single-user baseline and ego networks. The results show that community-level prediction models achieve higher prediction accuracy than the traditional single-user approach, whereas a local community detection algorithm outperforms a global modularity-based method.

Hyper graph is a data structure that captures many-to-many relations. It comes up in various contexts, one of those being the task of detecting fraudulent users of an on-line system given known associations between the users and types of activities they take part in. In this work we explore three approaches for applying general-purpose machine learning methods to such data. We evaluate the proposed approaches on a real-life dataset of customers and achieve promising results.