AI-Powered financial forensic systems: A conceptual framework for fraud detection and prevention
1 Independent Researcher, NJ, Accra, Ghana.
2 Independent Researcher, Canada.
3 Independent Researcher, Toronto, Ontario, Canada.
Review Article
Magna Scientia Advanced Research and Reviews, 2021, 02(02), 119-136
Article DOI: 10.30574/msarr.2021.2.2.0055
Publication history:
Received on 10 June 2021; revised on 05 August 2021; accepted on 08 August 2021
Abstract:
Fraud detection and prevention have become critical priorities in the financial industry, driven by the increasing sophistication of fraudulent schemes. This paper presents a conceptual framework for AI-powered financial forensic systems, focusing on their transformative potential in fraud detection and prevention. The framework integrates artificial intelligence (AI) techniques such as machine learning (ML), natural language processing (NLP), and neural networks to enhance the accuracy, speed, and scalability of forensic investigations. The study emphasizes the role of predictive analytics in identifying anomalous patterns and assessing risk in real time, significantly reducing financial losses and reputational damage. Key components of the framework include data aggregation, where structured and unstructured financial data are collated from diverse sources, and data preprocessing, ensuring accuracy and relevance for analysis. Advanced machine learning algorithms are applied to identify hidden patterns and correlations, enabling the early detection of fraudulent activities. Additionally, the framework incorporates explainable AI (XAI) to ensure transparency and accountability, addressing concerns about black-box decision-making. The research highlights the integration of blockchain technology to enhance data integrity and traceability, providing a tamper-proof audit trail for financial transactions. Moreover, it explores the application of NLP in analyzing textual data from financial reports and communication logs to uncover deceptive behaviors. The framework also emphasizes the importance of adaptive learning, allowing AI systems to evolve with emerging fraud techniques and regulatory changes. Challenges such as data privacy, ethical considerations, and implementation costs are critically examined, alongside strategies for overcoming these barriers. The study concludes that AI-powered financial forensic systems represent a paradigm shift in combating financial fraud, offering proactive and efficient solutions for safeguarding the global financial ecosystem.
Keywords:
AI-Powered Systems; Financial Forensics; Fraud Detection; Machine Learning; Natural Language Processing; Blockchain; Predictive Analytics; Explainable AI; Adaptive Learning; Financial Fraud Prevention
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