Data mining for assessing the credit risk of local government units in Croatia

Abstract

Abstract.Over the past few decades, data mining techniques, especially artificial neural networks, have been used for modelling many real-world problems. This paper aims to test the performance of three methods: (1) an artificial neural network (ANN), (2) a hybrid artificial neural network and genetic algorithm approach (ANN-GA), and (2) the Tobit regression approach in determining the credit risk of local government units in Croatia. The evaluation of credit risk and prediction of debtor bankruptcy have long been regarded as an important topic in accounting and finance literature. In this research, credit risk is modelled under a regression approach unlike typical credit risk analysis, which is generally viewed as a classification problem. Namely, a standard evaluation of credit risk is not possible due to a lack of bankruptcy data. Thus, the credit risk of a local unit is approximated using the ratio of outstanding liabilities maturing in a given year to total expenditure of the local unit in the same period. The results indicate that the ANN-GA hybrid approach performs significantly better than the Tobit model by providing a significantly smaller average mean squared error. This work is beneficial to researchers and the government in evaluating a local government unit's credit score.

Understanding the Extent of Automation and Process Transparency Appropriate for Public Services

“…The first approach is to use AI to identify situations such as fraud (Srivastava et al, 2014), sustainable areas (Gigović et al, 2016), and pollution (Adams & Kanaroglou, 2016;Cabaneros et al, 2017). The second method uses AI to assess measures like work effectiveness (Omoteso, 2012), credit risk (Vlah Jerić & Primorac, 2017), and fertilizer use (Nabavi-Pelesaraei et al, 2016). The third type of task is to measure when AI is implemented to gauge and optimize energy consumption (Grant et al, 2014;Ruiz et al, 2016), water quality (Khataee & Kasiri, 2010;Burchard-Levine et al, 2014), and public transportation (Kouziokas, 2017. Finally, the fourth use is prediction; here, AI is utilized to forecast behavior and needs (Saeedi, 2018), crime patterns (Alves et al, 2018), or the spread of disease (Zhang et al, 2015).…”

A hybrid model combining neural networks and decision tree for comprehension detection

“…There are many other fields than education in which ANNs may make high-stakes decisions and some progress has been made in extracting rules from ANNs, although the degree to which solutions to reasonably complex problems could be understood by a non-AI specialist remains debatable. These include classifying incipient faults in a power transformer [5], hydrological modelling [6], Credit-Risk Evaluation [7] and software cost estimation [8]. Some progress has been made in extracting rules from recurrent neural networks by transforming them to finite state machines [9], and [10] has attempted to unify various neuro-fuzzy rule approaches for ruled generation from recurrent and feedforward neural networks in a single soft computing framework.…”