# AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

**Authors:** Bingqiao Luo, Zhen Zhang, Qian Wang, Anli Ke, Shengliang Lu, Bingsheng He  
**arXiv:** [2308.15992](https://arxiv.org/abs/2308.15992)  
**Date:** 2023-08-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.15992v3/

## Key points

- Blockchain technology has transformed the financial industry through DeFi, offering transparency and efficiency.
- DeFi's popularity has led to an increase in fraudulent activities, resulting in significant financial losses.
- Researchers are exploring the use of AI to detect and prevent fraud within DeFi ecosystems.
- The survey categorizes various types of DeFi frauds such as Ponzi schemes, money laundering, and pump and dump schemes based on project life cycles.
- It compares CeFi with DeFi in terms of characteristics, corresponding frauds, and detection methods to improve understanding of the financial landscape.
- The survey provides an analysis of on-chain patterns and existing detection methods for DeFi frauds along with state-of-the-art AI approaches.
- It aims to guide researchers and regulators towards establishing a secure and trustworthy DeFi ecosystem by addressing research directions in fraud detection and regulatory challenges.

## Summary

In recent years, blockchain technology has transformed the financial industry through decentralized finance (DeFi), offering a transparent and efficient alternative to traditional systems. However, DeFi's growing popularity has also attracted fraudulent activities, resulting in significant financial losses. To combat this issue, researchers have explored the potential of artificial intelligence (AI) in detecting and preventing fraud within DeFi ecosystems. This comprehensive survey delves into various types of DeFi frauds such as Ponzi schemes, money laundering, and pump and dump schemes. It provides a taxonomy of DeFi frauds based on project life cycles to enhance understanding and classification of fraud distribution within the ecosystem. Additionally, it compares centralized finance (CeFi) with DeFi by highlighting their characteristics, corresponding frauds, and detection methods to improve our understanding of the financial landscape. Key contributions include an in-depth analysis of on-chain patterns and existing detection methods for DeFi frauds along with state-of-the-art AI approaches. By shedding light on research directions in DeFi fraud detection and regulatory challenges, this survey aims to guide researchers and regulators towards establishing a secure and trustworthy DeFi ecosystem. Overall, is crucial in combating fraudulent activities in decentralized finance while providing valuable insights for stakeholders to ensure integrity and security within the evolving financial landscape.