  International audit standards require the direct assessment of a financial
statement's underlying accounting transactions, referred to as journal entries.
Recently, driven by the advances in artificial intelligence, deep learning
inspired audit techniques have emerged in the field of auditing vast quantities
of journal entry data. Nowadays, the majority of such methods rely on a set of
specialized models, each trained for a particular audit task. At the same time,
when conducting a financial statement audit, audit teams are confronted with
(i) challenging time-budget constraints, (ii) extensive documentation
obligations, and (iii) strict model interpretability requirements. As a result,
auditors prefer to harness only a single preferably `multi-purpose' model
throughout an audit engagement. We propose a contrastive self-supervised
learning framework designed to learn audit task invariant accounting data
representations to meet this requirement. The framework encompasses deliberate
interacting data augmentation policies that utilize the attribute
characteristics of journal entry data. We evaluate the framework on two
real-world datasets of city payments and transfer the learned representations
to three downstream audit tasks: anomaly detection, audit sampling, and audit
documentation. Our experimental results provide empirical evidence that the
proposed framework offers the ability to increase the efficiency of audits by
learning rich and interpretable `multi-task' representations.
