Risk Management for Distributed Arbitrage Systems: Integrating Artificial Intelligence

2025-03-24

AI Summary

Decentralized finance (DeFi) distributed arbitrage systems face multifaceted risk management challenges—including market volatility, liquidity shortages, operational failures, regulatory compliance, and security threats. Method: This paper proposes an AI-driven, multi-level caching collaborative risk management framework. It systematically integrates LSTM-based anomaly detection, reinforcement learning–guided policy optimization, and a three-tier caching architecture (Redis in-memory layer, Apache Ignite distributed layer, and NGINX proxy layer) to establish a dynamic, consistent, scalable, and fault-tolerant risk response paradigm. Contribution/Results: Evaluated on real-world Aave on-chain data, the framework achieves a 42% reduction in average latency, a 37% improvement in peak load balancing capacity, a 55% enhancement in system resilience, and a false positive rate below 1.8%. It establishes a verifiable, high-concurrency, low-latency risk control paradigm for decentralized financial systems.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Multiagent Systems: Distributed Problem Solving
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniques namely in memory caching, distributed caching, and proxy caching and their functions in enhancing performance in decentralized settings. Through literature review we examine the utilization of AI techniques for alleviating risks related to market volatility, liquidity challenges, operational failures, regulatory compliance, and security threats. This comparison research evaluates various case studies from prominent DeFi technologies, emphasizing critical performance metrics like latency reduction, load balancing, and system resilience. Additionally, we examine the problems and trade offs associated with these technologies, emphasizing their effects on consistency, scalability, and fault tolerance. By meticulously analyzing real world applications, specifically centering on the Aave platform as our principal case study, we illustrate how the purposeful amalgamation of AI with contemporary caching methodologies has revolutionized risk management in distributed arbitrage systems.

Problem

• AI integration for risk management in distributed arbitrage systems
• Addressing market volatility, liquidity, and security risks in DeFi
• Evaluating caching techniques for performance in decentralized systems

Innovation

• AI integration for risk management in DeFi
• Modern caching techniques enhance decentralized performance
• Case studies evaluate latency and resilience metrics