Prototype A Decision Support Framework for Fraud Detection in Accounting Information Systems

This research focuses on developing, implementing, and validating a prototype decision support system (DSS) designed specifically for detecting fraud in accounting information systems (AIS). The prototype integrates advanced artificial intelligence (AI), data analytics, and expert-driven rule-based reasoning, structured to enhance internal control effectiveness and early fraud detection. Employing a quantitative research methodology, the prototype underwent rigorous simulation testing and statistical evaluation. Analytical techniques such as regression analysis were utilized to determine system effectiveness. Results indicate the DSS prototype significantly improves detection accuracy and auditor responsiveness, contributing practical insights for auditors, financial institutions, and regulatory bodies seeking to mitigate fraud through innovative technological solutions.

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Publication Details

Journal
Proceeding of International Conference on Business Economics Social Sciences and Humanities
Published
2025-06-20
DOI
https://doi.org/10.34010/icobest.v8i.687
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Prototype A Decision Support Framework for Fraud Detection in Accounting Information Systems

Yeffry Handoko Putra, Muhammad Reza Aulia
Proceeding of International Conference on Business Economics Social Sciences and Humanities
Imbalanced Data Classification Techniques
article

Prototype A Decision Support Framework for Fraud Detection in Accounting Information Systems

Yeffry Handoko Putra, Muhammad Reza Aulia
article en

Abstract

This research focuses on developing, implementing, and validating a prototype decision support system (DSS) designed specifically for detecting fraud in accounting information systems (AIS). The prototype integrates advanced artificial intelligence (AI), data analytics, and expert-driven rule-based reasoning, structured to enhance internal control effectiveness and early fraud detection. Employing a quantitative research methodology, the prototype underwent rigorous simulation testing and statistical evaluation. Analytical techniques such as regression analysis were utilized to determine system effectiveness. Results indicate the DSS prototype significantly improves detection accuracy and auditor responsiveness, contributing practical insights for auditors, financial institutions, and regulatory bodies seeking to mitigate fraud through innovative technological solutions.

Proceeding of International Conference on Business Economics Social Sciences and HumanitiesVol. 8
Universitas Komputer Indonesia (ID)
Openalex Percentile: Top 92%
Imbalanced Data Classification Techniques
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