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

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The rise of cybercrime and cyber-related crime encourages efficient digital forensic investigations more crucial than ever before. Traditional investigation methods can be time-consuming, 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 of fraud 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.