  Machine learning models underpin many modern financial systems for use cases
such as fraud detection and churn prediction. Most are based on supervised
learning with hand-engineered features, which relies heavily on the
availability of labelled data. Large self-supervised generative models have
shown tremendous success in natural language processing and computer vision,
yet so far they haven't been adapted to multivariate time series of financial
transactions. In this paper, we present a generative pretraining method that
can be used to obtain contextualised embeddings of financial transactions.
Benchmarks on public datasets demonstrate that it outperforms state-of-the-art
self-supervised methods on a range of downstream tasks. We additionally perform
large-scale pretraining of an embedding model using a corpus of data from 180
issuing banks containing 5.1 billion transactions and apply it to the card
fraud detection problem on hold-out datasets. The embedding model significantly
improves value detection rate at high precision thresholds and transfers well
to out-of-domain distributions.
