Mitigating Adversarial Threats: Safeguarding AI-Driven Fraud Detection Systems Against Malicious Exploits

The proliferation of AI-driven fraud detection systems increases the capability of organizations to a large extent in terms of fraud detection and prevention. These systems, however, are fast becoming the targets of adversarial threats that look to exploit their vulnerabilities. This paper focuses on the various machine learning algorithms such as Logistic Regression, Random Forest, and Decision Tree, that are deployed to mitigate such adversarial threats. Comparative analysis may show that, among the various algorithms considered, decision trees are most robust against malicious exploits. Particularly, the hierarchical structure of a decision tree and its split criteria provide resistance through the effective segregation of anomalous patterns of data and treating them differently. Our results indicate that, compared with others, the Decision Tree algorithm does a better job in fraudulent activity detection, performing well in stability and reliability under attacks. These findings suggest the importance of choosing appropriate machine learning models to strengthen AI-driven fraud detection systems against new types of cyber threats so they are both potent and trustworthy in real-world applications.