Since 2007 the Reserve Bank has conducted a Consumer Payments Survey (CPS) every three years, which provides comprehensive information on how Australians make their payments. The 2019 CPS was conducted just before the emergence of COVID-19 in Australia and gives a detailed snapshot of consumer payment behaviour prior to the changes in spending patterns induced by the pandemic. The survey provided further evidence that Australian consumers increasingly prefer to use electronic payment methods rather than cash for their day-to-day payments. Many people now tap their cards (or sometimes phones) even for small purchases. When paying with a card in person or online, consumers are more often choosing to use a debit card rather than a credit card. As a result, debit cards were the most frequently used consumer payment method in the 2019 survey. Consumers are also increasingly taking advantage of the ability to make payments using a range of innovative new payment services that have emerged in recent years, often facilitated by mobile technology and the use of digital payment credentials. Despite the trend towards electronic payments, cash still accounted for a significant share of lower-value payments and a material proportion of the population continue to make many of their payments in cash.

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Background. From information theory, surprisal is a measurement of how unexpected an event is. Statistical language models provide a probabilistic approximation of natural languages, and because surprisal is constructed with the probability of an event occuring, it is therefore possible to determine the surprisal associated with English sentences. The issues and pull requests of software repository issue trackers give insight into the development process and likely contain the surprising events of this process.Objective. Prior works have identified that unusual events in software repositories are of interest to developers, and use simple code metrics-based methods for detecting them. In this study we will propose a new method for unusual event detection in software repositories using surprisal. With the ability to find surprising issues and pull requests, we intend to further analyse them to determine if they actually hold importance in a repository, or if they pose a significant challenge to address. If it is possible to find bad surprises early, or before they cause additional troubles, it is plausible that effort, cost and time will be saved as a result.Method. After extracting the issues and pull requests from 5000 of the most popular software repositories on GitHub, we will train a language model to represent these issues. We will measure their perceived importance in the repository, measure their resolution difficulty using several analogues, measure the surprisal of each, and finally generate inferential statistics to describe any correlations.