Abstract
Significant advancements in fraud detection systems have been made due to the increasing integration of artificial intelligence (AI). However, a significant issue with these AI models is that they are inherently difficult to explain, which makes it challenging to understand the rationale behind their decisions. This research investigates techniques to improve AI-based fraud detection systems' transparency, focusing on converting complex numerical patterns into narratives. By doing this, these systems will be able to provide clear and transparent explanations for the decisions they make, giving researchers and investigators crucial new information about transactions that have been identified
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