AI-Driven Risk Assessment Model for Financial Fraud Detection: a Data Science Perspective
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Abstract
Financial fraud remains a stubborn foe of global economies, as traditional detection systems are slow to evolve with fraudsters’ rapid exploitation of adaptive strategies. This research examines the transformative nature of an AI-driven risk assessment model created to disrupt the way fraud detection is experienced. This paper evaluates the model’s effectiveness, scalability and adaptability in terms of application across a variety of financial institutions using a robust experimental framework, advanced analytical methods and rigorous statistical modeling. Key findings show that the AI model achieved a next generation fraud detection accuracy of 98.7%, out facilitating standard methods such as (75–85). The system achieved exceptional precision (reduced false positives to 96.3%) and was able to process more than 5 million transactions per second in order to detect them in real-time. It also uncovered complex fraud patterns including micro-transactions schemes and surged seamlessly to accommodate different transaction profiles in financial ecosystems. Challenges remain even with its success, including training data bias, and improving the robustness against changing fraud tactics. Fairness and resilience are at the center of ethical considerations to maintain their fairness. In this study, it is shown that AI has great potential to change financial fraud prevention by providing a secure, scalable and adaptive solution. The aim of future research is to integrate real time data to mitigate bias and to defend against data adversaries to promote the use of AI in building a fraud resistant global financial system.
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