arXiv AI
Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Sep 14
FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
FINESSE is an agent‑based simulation framework that generates synthetic, structured datasets of multiple interdependent financial event streams, such as transactions, payments, account status changes, and policy interventions. Each stream has its own action space, schema, and variable types, and the streams are coupled through agents’ evolving latent states, allowing temporally rich interactions. The accompanying FINESSE‑Bench dataset supports four tasks—balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction—and baseline results are provided using various time‑series and event‑sequence methods.
arXiv AI
Sep 1
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
arXiv:2506.11635v2 Announce Type: replace-cross Abstract: Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to...
arXiv AI
Jul 28
Traceable LLM Reasoning for Fake-Order Fraud Detection
arXiv:2607. 23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability.
arXiv Machine Learning
Jul 13
TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
arXiv:2607. 09528v1 Announce Type: new Abstract: The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior.
arXiv Computation and Language
Sep 1
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
TxSum introduces a user-centered approach to understanding Ethereum transactions by providing structured, risk-aware explanations grounded at the token‑flow level. The authors built a dataset of 187 complex transactions with 2,375 token‑flow annotations and transaction‑level summaries, and developed MATEX, a multi‑agent framework that retrieves external knowledge and audits explanations for factual consistency. MATEX outperforms existing baselines, improving user comprehension from 52.9% to 76.5% and increasing malicious‑transaction rejection from 36.0% to 88.0% while keeping false‑rejection rates low.
arXiv AI
Jun 9
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
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.