Early Warning System for Local Government Financial Distress Using Support Vector Machine with Synthetic Tabular Data

DOI

10.2991/978-94-6463-956-8_6How to use a DOI?

Keywords

Financial Distress; Predict; Synthetic Data

Abstract

Financial distress in local governments occurs when a region is unable to fulfill its debt obligations, creating risks to fiscal stability and regional economic health. This condition is especially concerning for governments that rely heavily on loans to fund infrastructure projects, as repayment failures can lead to penalties, additional costs, and limited access to future credit. Detecting the financial distress early is therefore crucial. However, prediction is often challenged by the imbalance data, out of 508 local governments in 2022, only 93 were classified as financially distressed, while 415 were financially healthy, resulting in a 1:4 ratio. Conventional predictive models tend to perform well for the majority class but fail to recognize high risk cases. To overcome this, the study applies synthetic data generation using the Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Network (CTGAN) to balance the dataset through oversampling. Support Vector Machines (SVM) with different kernels (linear, RBF, sigmoid, and polynomial) were trained on the augmented data. Model performance was evaluated using confusion matrices, F1-Score, G-Mean, and ROC-AUC. The findings indicate that SVM using the linear kernel trained with TVAE achieved superior results, with a G-Mean of 0.74 and ROC-AUC of 0.83, outperforming CTGAN based models (0.73 and 0.77). Feature importance analysis identified Individual Risk, Audit Opinions, and Human Development Index as key predictors of financial distress. Overall, integrating synthetic oversampling with SVM offers a practical and effective framework for developing early warning systems to help policymakers detect and mitigate fiscal risk.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Inggit Fatika
AU  - Laila Fathiyaturrahmi
AU  - Edhy Sutanta
AU  - Catur Iswahyudi
AU  - Retno Widiastuti
PY  - 2025
DA  - 2025/12/26
TI  - Early Warning System for Local Government Financial Distress Using Support Vector Machine with Synthetic Tabular Data
BT  - Proceedings of the 4th International Conference on Economic, Business, and Accounting Studies (ICEBAST 2025)
PB  - Atlantis Press
SP  - 48
EP  - 66
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-956-8_6
DO  - 10.2991/978-94-6463-956-8_6
ID  - Fatika2025
ER  -