Tax payment default prediction using genetic algorithm-based variable selection

Höglund, Henrik

doi:10.1016/j.eswa.2017.07.027

Cited by 31 publications

(28 citation statements)

References 25 publications

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“…Articles where loan default (or a proxy of it) is used as the dependent variable or articles where the failure prediction model has incorporated non-financial variables have been summarized in Appendix A Table A1. A1 have analysed default prediction based on different nonfinancial information, such as firm age (Altman et al 2010(Altman et al , 2017Back 2005;Bhimani et al 2013); firm size (Altman et al 2010(Altman et al , 2017Back 2005;Bhimani et al 2013); industrial sector (Altman et al 2010(Altman et al , 2017Bhimani et al 2013;Höglund 2017;Laitinen 2011); management characteristics (Back 2005;Bhimani et al 2013;Ciampi 2015;Laitinen 2011); previous payment history (Back 2005;Ciampi et al 2020;Laitinen 2011); corporate social responsibility (Ciampi 2018); tax arrears (Lukason and Andresson 2019); country of origin (Altman et al 2017); financial support from partners (Bhimani et al 2013); ownership of assets (Bhimani et al 2013); stock price volatility (Atiya 2001); audit information (Altman et al 2010); late filing of reports (Altman et al 2010); and country court judgement (Altman et al 2010).…”

Section: Financial and Non-financial Variables For Firm Failure Predictionmentioning

confidence: 99%

What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

Kohv

Lukason

2021

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This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset from an Estonian commercial bank with 12,901 observations of defaulted and non-defaulted firms. The analysis is performed using statistical (logistic regression) and machine learning (neural networks) methods. Out of the three domains used, tax arrears show high prediction capabilities for bank loan defaults, while financial ratios and reporting delays are individually not useful for that purpose. The best default prediction accuracies were 83.5% with tax arrears only and 89.1% with all variables combined. The study contributes to the extant literature by enhancing the bank loan default prediction accuracy with the introduction of novel variables based on tax arrears, and also by indicating the pecking order of satisfying creditors’ claims in the firm failure process.

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Section: Financial and Non-financial Variables For Firm Failure Predictionmentioning

confidence: 99%

What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

Kohv

Lukason

2021

Risks

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“…Kim et al [39] develop multi-class financial misstatement detection models for discovering fraudulent activities. Höglund [40] proposes a genetic algorithm-based decision support tool for predicting tax payment defaults.…”

Section: B Data Mining and Machine Learning Approachesmentioning

confidence: 99%

Combining Network Visualization and Data Mining for Tax Risk Assessment

et al. 2020

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This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency. INDEX TERMS Tax risk assessment, tax evasion discovery, network visualization, data mining, human-computer interaction.

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“…Measuring the tax default status of firms is considered to be an even more challenging prediction task because it is characterized by the presence of sample selection bias due to the small number of labelled data (known as tax default cases) [4]. The indicators of credit default and corporate bankruptcy have been extensively studied in earlier research [5], [6], but the indicators of tax default are still poorly understood despite the fact that corporate tax documents contain much multifaceted information for assessing tax default risk prediction [7]. Predicting a tax default differs from predicting a tax evasion (tax fraud) [4], [8] or a tax avoidance [9].…”

Section: Introductionmentioning

confidence: 99%

“…This can be attributed to model underspecification (only four financial indicators were used) and the presence of nonlinear relationships between the financial indicators and the firms' tax status, which could not be detected using the logistic regression (LR) model. A substantially improved classification accuracy was achieved when a large number of financial indicators were employed [7]. It must be noted that the aim of this earlier study was to investigate the importance of early-warning financial indicators rather than to develop an accurate tax default prediction system.…”

Section: Introductionmentioning

confidence: 99%

Tax Default Prediction Using Feature Transformation-Based Machine Learning

et al. 2021

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This study proposes to address the economic significance of unpaid taxes by using an automatic system for predicting a tax default. Too little attention has been paid to tax default prediction in the past. Moreover, existing approaches tend to apply conventional statistical methods rather than advanced data analytic approaches, including state-of-the-art machine learning methods. Therefore, existing studies cannot effectively detect tax default information in real-world financial data because they fail to take into account the appropriate data transformations and nonlinear relationships between early-warning financial indicators and tax default behavior. To overcome these problems, this study applies diverse feature transformation techniques and state-of-the-art machine learning approaches. The proposed prediction system is validated by using a dataset showing tax defaults and non-defaults at Finnish limited liability firms. Our findings provide evidence for a major role of feature transformation, such as logarithmic and square-root transformation, in improving the performance of tax default prediction. We also show that extreme gradient boosting and the systematically developed forest of multiple decision trees outperform other machine learning methods in terms of accuracy and other classification performance measures. We show that the equity ratio, liquidity ratio, and debt-to-sales ratio are the most important indicators of tax defaults for 1-year-ahead predictions. Therefore, this study highlights the essential role of well-designed tax default prediction systems, which require a combination of feature transformation and machine learning methods. The effective implementation of an automatic tax default prediction system has important implications for tax administration and can assist administrators in achieving feasible government expenditure allocations and revenue expansions.INDEX TERMS Default prediction, corporate tax, machine learning, feature transformation.

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Tax payment default prediction using genetic algorithm-based variable selection

Cited by 31 publications

References 25 publications

What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains

Combining Network Visualization and Data Mining for Tax Risk Assessment

Tax Default Prediction Using Feature Transformation-Based Machine Learning

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