Machine learning for financial transaction classification across companies using character-level word embeddings of text fields
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.
Originalsprog | Engelsk |
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Tidsskrift | Intelligent Systems in Accounting, Finance and Management |
Vol/bind | 28 |
Udgave nummer | 3 |
Sider (fra-til) | 159-172 |
ISSN | 1550-1949 |
DOI | |
Status | Udgivet - 2021 |
Bibliografisk note
Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.
ID: 280029661