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The Utility of Neural Model in Predicting Tax Avoidance Behavior

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Intelligent Decision Technologies

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 238))

Abstract

The phenomenon of tax evasion and phenomenon of tax avoidance are two facets of similar behavior. Both affect societies across globe. It is a well-known fact that tax revenues account for the majority of public revenues and therefore the phenomenon is of great interest for public authorities as it is for other stakeholders. Designing effective tax policies aiming at maximizing public revenues can only be done through a deep and complete understanding of taxpayer behavior and its internal motivations. The present study aims at developing a model for detecting risk of tax fraud in taxpayer behavior by trying to predict propensity of individual showing intention for evading taxes. The purpose of this study is to model fiscal behavior through artificial intelligence, using MLP network model that was trained and tested on a real data set comprising behavioral or biometric data. Results were compared to that of a binary logistic regression. The empirical model tested was based on qualitative attributes, which relate to behavioral elements such as the attitudes and perceptions of taxpayers and also internal motivations, morality or personal values. The prediction efficiency of the MLP model relies on 70% which shows similar performance to comparable research.

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Abbreviations

MLP:

Multilevel perceptron neural network

AI:

Artificial intelligence

SVM:

Support vector machine

PSO:

Particle swarm optimization

ROC:

Receiver operating characteristic curve

AUROC:

Area under receiver operating characteristic curve

P :

Is the probability for an individual to trust the fiscal system

X j :

Are the factors considered to influence this probability

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Acknowledgement

This paper is part of the project COST CA19130 FinAI - Fintech and Artificial Intelligence in Finance - Towards a Transparent Financial Industry.

Author information

Authors and Affiliations

  1. Department of Finance and Accounting, Faculty of Economics, University of Oradea, Oradea, Romania

    Coita Ioana-Florina

  2. Department of Statistics-Forecasting-Mathematics, Faculty of Economics and Business Administration, University of Babeş-Bolyai, Cluj-Napoca, Romania

    Codruţa Mare

Authors
  1. Coita Ioana-Florina
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  2. Codruţa Mare
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Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University, Poole, UK

    Robert J. Howlett

  3. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Ioana-Florina, C., Mare, C. (2021). The Utility of Neural Model in Predicting Tax Avoidance Behavior. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_6

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