# Are You for Real? Detecting Identity Fraud via Dialogue Interactions

arXiv: https://arxiv.org/abs/1908.06820 · HTML: https://mlsanity.com/paper/1908.06820/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [Zero-Shot Question Answering over Financial Documents using Large Language Models](https://mlsanity.com/paper/2311.14722/) (2023)
- [Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities](https://mlsanity.com/paper/2504.07274/) (2025)
- [FinTech for Social Good: A Research Agenda from NLP Perspective](https://mlsanity.com/paper/2211.06431/) (2022)
- [Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment](https://mlsanity.com/paper/2505.07852/) (2025)
- [Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation](https://mlsanity.com/paper/2408.01342/) (2024)

## Abstract (arXiv)

Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules. One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant. The other is structured dialogue management that can dynamically generate a series of questions based on the personal KG to ask the applicants and determine their identity states. We also present a heuristic user simulator based on problem analysis to evaluate our method. Experiments have shown that the trainable dialogue system can effectively detect fraudsters, and achieve higher recognition accuracy compared with rule-based systems. Furthermore, our learned dialogue strategies are interpretable and flexible, which can help promote real-world applications.