Classification of financial transactions in accounting using graph neural networksOmeragić, Ermin (Avtor) Šubelj, Lovro (Mentor) graph representation learningmachine learningaccount classificationbookkeeping automationheterogeneous networksBusinesses must manually assign each line item on an invoice to a particular account as part of the invoice processing procedure. When there are many account codes to pick from, this can be difficult and time-consuming. Suggesting account codes to accountants as part of the semi-automated system may reduce manual errors and speed up this process. In this thesis, we evaluated the use of graph neural networks for the task of suggesting account codes for financial transaction items. We created a heterogeneous transaction graph based on real financial data of two companies and implemented and evaluated four graph neural network models. We used methods from the previous work on this topic as baselines. The results showed a slight improvement in the performance of the graph models compared to baselines on a small-scale dataset. Further testing with more data should be carried out before the models can be deployed to a production system.20232023-10-18 13:50:01Magistrsko delo/naloga151743VisID: 35370COBISS_ID: 170192387sl