Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

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.

TidsskriftIntelligent Systems in Accounting, Finance and Management
Udgave nummer3
Sider (fra-til)159-172
StatusUdgivet - 2021

Bibliografisk note

Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.

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