← Back to all news
arXiv AI By Parviz Ghafariasl, Weimin Fu, Xiaolong Guo, Shing I. Chang

Incremental Risk Assessment of Progressive Elder Financial Scams via Instruction-Tuned Small Language Models

Read the original on arXiv AI →

The paper presents a cumulative turn‑based risk assessment framework for detecting financial scams targeting older adults, which aggregates conversational turns and updates risk estimates at each step. A multi‑turn dialogue dataset covering investment, charity, and tech support scams is created, with annotations for risk level, score, rationale, and safety recommendation at every cumulative stage. Four small language models (Phi‑4, LLaMA‑3.2, DeepSeek‑R1, Qwen3) are fine‑tuned; Phi‑4 and LLaMA‑3.2 outperform others in turn‑aware risk estimation, demonstrating that compact models can effectively support incremental scam detection in resource‑constrained, privacy‑aware deployments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Jul 21

Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification

arXiv:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.

By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao
llms
More like this →
arXiv Machine Learning
Aug 26

Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate

The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.

By Ethan Traister, Ankit Raj, Jiaqi Gan, Xingyu Shen, Tyler Wu, Yuchen Zhou, Tommy Duong, Kidus Zewde, Siying Chen, Simiao Ren
llmsagentsfine-tuningbenchmarks
More like this →
arXiv Computation and Language
2d ago

TeleAntiFraud 2.0: A Refreshable, Profile-Grounded, and Audio-Based Benchmark for Telecom Fraud Detection

TeleAntiFraud 2.0 is a monthly‑frozen, audio‑based benchmark for telecom fraud detection that incorporates newly observed scam patterns while preserving earlier test sets. It uses a Mixed‑Tree Anti‑Fraud Generation Pipeline to create profile‑grounded scenarios, expands them into mixed‑tree dialogues, and renders validated speech for 900 Chinese calls (600 fraud, 300 near‑domain non‑fraud) each month. Experiments show that classifiers perform well against unrelated negatives but drop significantly against near‑domain negatives, highlighting the need for near‑domain construction and collapse‑aware reporting in realistic evaluation settings.

By Huiyuan Liu, Zhiming Ma, Yanxing Liu, Shun Zhang, Qifan Wang, Di Liu, Yifan Wang, Yuyang Deng, Haoyang Meng, Yijin Zhou, Yuxi Zhao, Chengxian Hu, Peidong Wang, Peng Chen
llmsmultimodalbenchmarks
More like this →