Blockchain-Based Forensic Audit Framework for Tracking Financial Crimes in Decentralized Finance Systems
DOI:
https://doi.org/10.70062/jiafc.v1i3.202Keywords:
Anomaly Detection, Blockchain, Decentralized Finance, Financial Crime, Forensic AuditingAbstract
This research explores the implementation of a blockchain-based forensic audit framework designed to enhance the detection and investigation of suspicious financial activities within decentralized finance (DeFi) ecosystems. The main problem addressed in this study concerns the inefficiency, lack of transparency, and vulnerability to data manipulation commonly found in traditional forensic auditing systems. The objective is to develop a model that integrates blockchain technology with graph-based anomaly detection to improve accuracy, transparency, and scalability in financial audits. The proposed method combines blockchain’s immutable ledger capabilities with automated detection algorithms and Chain of Custody (CoC) verification to ensure data integrity and accountability. The results demonstrate that the proposed system achieves a detection accuracy exceeding 90%, as presented in Table 1, and effectively categorizes different suspicious transaction patterns illustrated in Figure 2. Compared to conventional methods, the framework offers superior performance in terms of speed, reliability, and adaptability. The findings suggest that this approach establishes a new paradigm in forensic auditing by combining automation, transparency, and scalability into a cohesive analytical model. In conclusion, the study confirms that blockchain-based forensic auditing significantly enhances digital financial oversight and provides a foundation for developing intelligent, tamper-proof audit systems suitable for the evolving landscape of decentralized finance.
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