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

Rasmus Kær Jørgensen and Christian Igel

Intelligent Systems in Accounting, Finance and Management, 2021, vol. 28, issue 3, 159-172

Abstract: 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.

Date: 2021
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