Risk Management for Distributed Arbitrage Systems: Integrating Artificial Intelligence
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910890084794368 |
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| author | Hazarika, Akaash Vishal Shah, Mahak Patil, Swapnil Shukla, Pradyumna |
| author_facet | Hazarika, Akaash Vishal Shah, Mahak Patil, Swapnil Shukla, Pradyumna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk Management for Distributed Arbitrage Systems: Integrating Artificial Intelligence Hazarika, Akaash Vishal Shah, Mahak Patil, Swapnil Shukla, Pradyumna Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning I.2.11; G.3 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. |
| title | Risk Management for Distributed Arbitrage Systems: Integrating Artificial Intelligence |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning I.2.11; G.3 |
| url | https://arxiv.org/abs/2503.18265 |