# Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection

arXiv: https://arxiv.org/abs/2506.21443 · HTML: https://mlsanity.com/paper/2506.21443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Adaptive Online Incremental Learning for Evolving Data Streams](https://mlsanity.com/paper/2201.01633/) (2022)
- [Detecting Scams Using Large Language Models](https://mlsanity.com/paper/2402.03147/) (2024)
- [Advanced Real-Time Fraud Detection Using RAG-Based LLMs](https://mlsanity.com/paper/2501.15290/) (2025)
- [Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment](https://mlsanity.com/paper/2505.07852/) (2025)

## Built on by (most cited first, 2 of 2)

- [Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows](https://mlsanity.com/paper/2607.13078/) (2026)
- [When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control](https://mlsanity.com/paper/2609.17516/) (2026)

## Abstract (arXiv)

Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)-Enhanced LLM framework that integrates pretrained LLMs with structured, task-specific insights to perform fraud and concept drift detection. The proposed architecture consists of three main components: (1) a DK-LLM module to detect fake or deceptive conversations; (2) a drift detection unit (OCDD) to determine whether a semantic shift has occurred; and (3) a second DK-LLM module to classify the drift as either benign or fraudulent. We first validate the value of domain knowledge using a fake review dataset and then apply our full framework to SEConvo, a multiturn dialogue dataset that includes various types of fraud and spam attacks. Results show that our system detects fake conversations with high accuracy and effectively classifies the nature of drift. Guided by structured prompts, the LLaMA-based implementation achieves 98% classification accuracy. Comparative studies against zero-shot baselines demonstrate that incorporating domain knowledge and drift awareness significantly improves performance, interpretability, and robustness in high-stakes NLP applications.