Connecting the Dots: Graph Neural Networks for Auditing Accounting Journal Entries

SUMMARY Based on the double-entry bookkeeping mechanism, each transaction is recorded in at least two ledger accounts, with one debit and the other one credit. Journal entry data, in the context of accounting, contains a rich network of information that can be effectively translated into a graph. This study explores how to use graph neural networks to learn graph representations from journal entry data and to systematically understand the complex patterns and connections that exist in journal entries at the transaction level. The real-world application results demonstrate that the unsupervised graph neural network framework offers a promising methodology for detecting error and fraud in financial audits.1 Data Availability: Dataset A comes from the Ernst & Young Academic Resource Center (EYARC), https://www.eyarc.site/blog/peach-state-university-hotel-understanding-audit-analytics-case