Knowledge Graph Enrichments for Credit Account Prediction

Michael Schulze, Michael Schulze, Andreas Dengel, Andreas Dengel

2025

Abstract

For the problem of credit account prediction on the basis of received invoices, this paper presents a pipeline consisting of 1) construction of an accounting knowledge graph, 2) enrichment algorithms, and 3), prediction of credit accounts with methods of a) rule-based link prediction, b) case-based reasoning, and c) a combination of both. Explainability and traceability have been key requirements. While preserving the order of invoices in cross-fold validation, key findings in our scenario are: 1) using all enrichments from the pipeline increases prediction performance up to 12.45 percent points, 2) single enrichments are useful on their own, 3) case-based reasoning benefits most from having enrichments available, and 4), the combination of link prediction and case-based reasoning yields best prediction results in our scenario. Paper page: https://git.opendfki.de/michael.schulze/account-prediction.

Paper Citation

in Harvard Style

Schulze M. and Dengel A. (2025). Knowledge Graph Enrichments for Credit Account Prediction. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 441-452. DOI: 10.5220/0013181000003890

in Bibtex Style

@conference{icaart25,
author={Michael Schulze and Andreas Dengel},
title={Knowledge Graph Enrichments for Credit Account Prediction},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={441-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013181000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Knowledge Graph Enrichments for Credit Account Prediction
SN - 978-989-758-737-5
AU - Schulze M.
AU - Dengel A.
PY - 2025
SP - 441
EP - 452
DO - 10.5220/0013181000003890
PB - SciTePress