Advancing Digital Forensic through Machine Learning - An Integrated Framework for Fraud Investigation

Aug 2, 2026

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The article addresses the growing need for efficient digital forensic investigations amid cybercrime and cyber-related fraud. It contrasts traditional approaches that are often time-consuming, costly, and resource-intensive with machine learning’s potential to automate analysis and reduce complexity. Using document analysis, it reviews existing digital forensic practices and demonstrates machine learning implementation through Autopsy 4.15.0 features. Results emphasize a comprehensive framework that prioritizes interpretation, while noting current machine learning mainly supports the most time-intensive investigation phase and is still limited for fraud detection and prevention.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Article Information

Abstract and Keywords

How to Cite

Introduction

Background: cybercrime and fraud

Role of digital forensics in investigations

Case examples and legal relevance

Big Data and unstructured evidence

Asia Pacific Fraud Journal E-ISSN: 2502-695X, ISSN: 2502-8731 Volume 9, Issue 1 (January-June) 2024 Asia Pacific Fraud Journal now has been accredited “SINTA 3” by Ministry of Research and Technology of The Republic of Indonesia (No SK. 225/E/KPT/2022) . Available online at: [http://apfjournal.or.id/index.php/apf](http://apfjournal.or.id/index.php/apf) | Advancing Digital Forensic through Machine Learning: An Integrated Framework for Fraud Investigation
􀀍1,2Wishnu Agung Baroto
1Doctoral Student, Department of Social and Human Science, Tokyo Institute of Technology Tokyo, Japan 2Directorate General of Taxes, Ministry of Finance of the Republic of Indonesia
Jakarta, Indonesia | | | --- | --- | | ARTICLE INFORMATION Article History:
Received July 28, 2023 Revised January 23, 2024 Accepted June 1, 2024
DOI:
10.21532/apfjournal.v9i1.346

This is an open access article under the CC-BY-SA License | ABTRACT
The rise of cybercrime and cyber-related crime encourages efficient digital forensic investigations more crucial than ever before. Traditional investigation methods can be timeconsuming, costly, and resource-intensive, while machine learning algorithms have the potential to reduce the complexity by promoting automation and investigation capabilities. This study begins with an analysis of digital forensics framework using a document analysis methodology. Moreover, exploring current practice and potential implementation of machine learning in digital forensics for fraud investigation is demonstrated through the features of Autopsy 4.15.0, a widely known digital forensics tool. The findings suggest the implementation of a comprehensive digital forensic framework that prioritizes the interpretation phase, with the support of machine learning capabilities. At present, machine learning mainly supports the analysis phase, which happens to be the most time-intensive process of digital forensic investigations. Furthermore, as fraud investigation has a role offraud detection and prevention, current digital forensics procedures do not support the fraud detection and prevention process, despite the potential for machine learning to support this through pattern recognition.These discoveries are particularly significant in the fight against fraudulent activities, such as tax fraud, data fraud, financial fraud, and asset misappropriation, in the digital age.
Keyword: Digital Forensic, Machine Learning, Fraud Investigation | | How to Cite:
Baroto, W. A., (2023) . Advancing Digital Forensic Through Machine Learning: An Integrated Framework for Fraud Investigation. Asia Pacific Fraud Journal, 9(1), 1-16. [http://doi.org/10.21532/](http://doi.org/10.21532/)[ ](http://doi.org/10.21532/)[apfjournal.v9i1.346.](apfjournal.v9i1.346.) | | 􀀍 Corresponding author : [Email:](Email: wishnu.ab@gmail.com)[ wishnu.ab@gmail.com](Email: wishnu.ab@gmail.com) Association of Certified Fraud Examiners (ACFE) Indonesia Chapter Page. 1-16 The 1st Winner of National Call for Paper ACFE Indonesia Chapter 2023 2| Wishnu Agung Baroto, Advancing Digital Forensic through Machine Learning 1. INTRODUCTION The massive expansion of the internet and increased use of information and communication technology brought momentous changes in human life. Digital technology has provided us with numerous economic opportunities, such as e-commerce, cryptocurrency, and other digital economy sectors. Unfortunately, these advancements are also inline with the rise of cybercrime and cyber-related crime, as well as other fraudulent activities, such as financial fraud, asset misappropriation, tax fraud, and data fraud. In order to combat fraud, fraud examiners and investigators must thoroughly collect, examine, and analyze all evidence, including digital and electronic data, in addition to traditional physical evidence. This is where digital forensics becomes more critical to combat crime and fraud asa process of investigating and collectin

FAQs

Why are digital forensics considered critical for fraud investigation in the digital age?

Fraud examiners need to collect, examine, and analyze digital and electronic evidence alongside physical evidence. Digital forensics helps investigate and prepare data for use in court while improving the authenticity of evidence.

What limitation of traditional investigation methods does the study highlight?

Traditional investigation methods can be time-consuming, costly, and resource-intensive, increasing the burden on investigators.

How does the article position machine learning within digital forensic investigations for fraud?

Machine learning is described as supporting investigation automation and analysis, mainly benefiting the analysis phase today. The article also stresses that current procedures do not yet fully support fraud detection and prevention despite machine learning’s potential for pattern recognition.

Aug 2, 2026

Upload by Riley