Harnessing artificial intelligence for financial fraud detection through cybersecurity-integrated models ensuring real-time anomaly tracking and mitigation

Comfort Alorh *

Illinois State University, Normal, IL, USA.
 
Review
International Journal of Science and Research Archive, 2024, 13(02), 1586-1603.
Article DOI: 10.30574/ijsra.2024.13.2.2635
Publication history: 
Received on 21 November 2024; revised on 28 December 2024; accepted on 30 December 2024
 
Abstract: 
The escalating complexity and globalization of financial transactions have amplified vulnerabilities to fraud, necessitating innovative solutions that transcend traditional detection mechanisms. Financial fraud poses systemic risks to institutions, markets, and consumers, particularly as digital platforms expand transaction volumes and cross-border flows. Conventional rule-based systems, while valuable, often fail to capture the evolving sophistication of fraudulent schemes, leading to delayed responses and increased economic losses. Recent advances in artificial intelligence (AI) offer transformative potential in this domain by enabling adaptive learning, predictive modeling, and pattern recognition across vast and heterogeneous datasets. From a broader perspective, AI-driven approaches to fraud detection are reshaping financial cybersecurity, bridging the gap between static safeguards and dynamic, real-time defenses. Integration with cybersecurity protocols enhances resilience by enabling multi-layered anomaly detection that aligns with regulatory compliance and risk governance requirements. Moreover, AI models embedded in cybersecurity frameworks provide transparency and explainability, bolstering institutional trust and investor confidence. Narrowing to operational deployment, cybersecurity-integrated AI models support real-time anomaly tracking by leveraging techniques such as deep learning, natural language processing, and graph-based analysis to identify hidden connections within transaction networks. These models do not merely detect anomalies but also generate actionable intelligence, facilitating immediate mitigation of threats before they escalate. By aligning with national and organizational priorities for financial stability, AI-augmented fraud detection contributes to safeguarding assets, reducing systemic vulnerabilities, and sustaining trust in digital finance. Ultimately, harnessing AI for financial fraud detection within cybersecurity-integrated models underscores a paradigm shift from reactive monitoring to proactive, intelligent defense mechanisms.
 
Keywords: 
Artificial Intelligence; Financial Fraud Detection; Cybersecurity; Real-Time Anomaly Tracking; Predictive Modeling; Risk Mitigation
 
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