add Graph Auditing 2024

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[Nilsonmichiles]

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Title: Connecting the Dots: Graph Neural Networks for Auditing Accounting Journal Entries
Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4847792
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Authors: Marco Schreyer, Qing Huang, Nilson Michiles, Miklos Vasarhelyi,
Published at: SRRN 2024
Key Contributions:Based on the double-entry bookkeeping mechanism, each transaction is recorded in at least two ledger accounts, with either debit or another 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 intricate patterns and connections inherent in journal entries at the transaction level. The real-world application results demonstrate that the unsupervised graph neural network framework of journal entry data offers a promising methodology for detecting fraud and error in auditing work.