Abstract
AbstractBookkeeping is crucial in both accounting and auditing. However, a substantial quantity of accounting information initially recorded using unstructured natural language, which restricts the efficiency and accuracy of bookkeeping. In this study, we exploit proprietary transaction data from three firms to demonstrate the capacity of a word embedding approach based on a neural network model (i.e., Word2vec) for processing transaction‐related natural language and automating bookkeeping practice. Our study contributes to accounting practice and literature by demonstrating a practical application of Word2vec to the construction of an automated bookkeeping system.
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