arXiv:2606.08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers. Existing methods fall at least one of these requirements: automated machine learning systems search a fixed numerical space without semantic awareness of the dataset; graph neural network-based methods require pre-defined relational graphs and remain opaque at the individual-decision level; and the design of general-purpose large language model (LLM)
The rapid advancement of large language models (LLMs) is enabling new, more sophisticated approaches to complex problems like fraud detection, moving beyond previous computational limitations.
This development suggests a significant leap in using AI for high-stakes operational security, impacting financial systems and online commerce through more accurate and understandable fraud prevention.
Fraud detection can become more semantically aware and transparent at the individual decision level, potentially reducing false positives and improving risk manager comprehension compared to current opaque systems.
- · Financial institutions
- · E-commerce platforms
- · Telecommunications companies
- · AI/LLM developers
- · Fraudsters
- · Legacy fraud detection system providers
- · Opaque black-box AI systems
Improved fraud detection accuracy and reduced financial losses for businesses.
Increased trust in digital transactions and potentially lower operational costs for security.
The methodology could generalize to other complex-decision, high-imbalance problems requiring explainability, accelerating broader AI agent adoption.
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Read at arXiv cs.AI