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research-article

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

Authors:

Rasmus Kær Jørgensen,

Christian IgelAuthors Info & Claims

Intelligent Systems in Accounting, Finance and Management, Volume 28, Issue 3

Pages 159 - 172

https://doi.org/10.1002/isaf.1500

Published: 28 October 2021 Publication History

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.

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Cited By

Kotepuchai PLimpiyakorn Y(2024)Multilabel Classification of Account Code in Double-Entry BookkeepingProceedings of the 2024 10th International Conference on Computer Technology Applications10.1145/3674558.3674587(209-214)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3674558.3674587
Pinconschi EGopinath DAbreu RPăsăreanu CChristakis MPradel M(2024)Evaluating Deep Neural Networks in Deployment: A Comparative Study (Replicability Study)Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680401(1300-1311)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680401
Zeng XZhang SZhong HZhang HLu MZheng ZChen Y(2024)PECJ: Stream Window Join on Disorder Data Streams with Proactive Error CompensationProceedings of the ACM on Management of Data10.1145/36392682:1(1-24)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639268

Index Terms

Machine learning for financial transaction classification across companies using character‐level word embeddings of text fields
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image International Journal of Intelligent Systems in Accounting and Finance Management

International Journal of Intelligent Systems in Accounting and Finance Management Volume 28, Issue 3

July/September 2021

58 pages

ISSN:1055-615X

EISSN:2160-0074

DOI:10.1002/isaf.v28.3

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© 2021 John Wiley & Sons, Ltd.

Publisher

John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 28 October 2021

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Citations

Cited By

Kotepuchai PLimpiyakorn Y(2024)Multilabel Classification of Account Code in Double-Entry BookkeepingProceedings of the 2024 10th International Conference on Computer Technology Applications10.1145/3674558.3674587(209-214)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3674558.3674587
Pinconschi EGopinath DAbreu RPăsăreanu CChristakis MPradel M(2024)Evaluating Deep Neural Networks in Deployment: A Comparative Study (Replicability Study)Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680401(1300-1311)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680401
Zeng XZhang SZhong HZhang HLu MZheng ZChen Y(2024)PECJ: Stream Window Join on Disorder Data Streams with Proactive Error CompensationProceedings of the ACM on Management of Data10.1145/36392682:1(1-24)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639268
García-Méndez Sde Arriba-Pérez FBarros-Vila AGonzález-Castaño F(2022)Detection of temporality at discourse level on financial news by combining Natural Language Processing and Machine LearningExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116648197:COnline publication date: 18-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116648

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