Abstract
This project aims to explore to what extent external semantic resources on companies can be used to improve the accuracy of a real bank transaction classification system. The goal is to identify which implementations are best suited to exploit the additional company data retrieved from the Brønnøysund Registry and the Google Places API, and accurately measure the effects they have. The classification system builds on a Bag-of-Words representation and uses Logistic Regression as classification algorithm. This study suggests that enriching bank transactions with external company data substantially improves the accuracy of the classification system. If we compare the results obtained from our research to the baseline, which has an accuracy of 89.22%, the Brønnøysund Registry and Google Places API yield increases of 2.79pp and 2.01pp respectively. In combination, they generate an increase of 3.75pp.
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Vollset, E., Folkestad, E., Gallala, M.R., Gulla, J.A. (2017). Making Use of External Company Data to Improve the Classification of Bank Transactions. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_54
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DOI: https://doi.org/10.1007/978-3-319-69179-4_54
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