# Predicting municipalities in financial distress: a machine learning approach enhanced by domain expertise

arXiv: https://arxiv.org/abs/2302.05780 · HTML: https://mlsanity.com/paper/2302.05780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A machine learning approach for forecasting hierarchical time series](https://mlsanity.com/paper/2006.00630/) (2020)
- [SOS-SDP: an Exact Solver for Minimum Sum-of-Squares Clustering](https://mlsanity.com/paper/2104.11542/) (2021)
- [An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering](https://mlsanity.com/paper/2111.15571/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)

## Abstract (arXiv)

Financial distress of municipalities, although comparable to bankruptcy of private companies, has a far more serious impact on the well-being of communities. For this reason, it is essential to detect deficits as soon as possible. Predicting financial distress in municipalities can be a complex task, as it involves understanding a wide range of factors that can affect a municipality's financial health. In this paper, we evaluate machine learning models to predict financial distress in Italian municipalities. Accounting judiciary experts have specialized knowledge and experience in evaluating the financial performance, and they use a range of indicators to make their assessments. By incorporating these indicators in the feature extraction process, we can ensure that the model is taking into account a wide range of information that is relevant to the financial health of municipalities. The results of this study indicate that using machine learning models in combination with the knowledge of accounting judiciary experts can aid in the early detection of financial distress, leading to better outcomes for the communities.