Audit Opinion Prediction Using the Decision Tree Algorithm

Amra Gadžo, Mirza Suljić and Erna Herić

EconStor Conference Papers from ZBW - Leibniz Information Centre for Economics

Abstract: This paper presents a data mining approach for Audit opinion pre¬diction in Government-owned enterprises within the Federation of Bosnia and Herzegovina using the Decision tree algorithm. A database was constructed from financial statements covering 2004-2019, incorporating indicators from balance sheets, income statements, and cash flow statements, alongside cor¬responding Audit opinions from the state audit body. The study evaluates three Decision tree algorithms (J48, RandomTree, REPTree) on data from 2020-2023, with REPTree achieving 73% classification accuracy through seven predictive rules. The findings demonstrate the potential of data mining techniques for pattern recognition in audit reports, contributing to transparency in financial reporting and supporting regulatory authorities in detecting irregularities within Government-owned enterprises.

Keywords: Audit opinion; Decision tree; Prediction (search for similar items in EconPapers)
JEL-codes: M40 M41 (search for similar items in EconPapers)
Date: 2025
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