Investment Compliance in Hedge Funds using Zero Knowledge Proofs

Autor: Komal Kalra, Sandeep Kumar Shukla, Shubham Sahai
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: The Journal of The British Blockchain Association (2021)
Druh dokumentu: article
ISSN: 2516-3957
2516-3949
DOI: 10.31585/jbba-4-1-(9)2021
Popis: Financial Regulation is a form of compliance system that subjects financial institutions to certain requirements and restrictions. Investment Compliance is an example that involves investment restrictions and monitoring on behalf of investors. Hedge Funds differ from other traditional funds such as mutual funds because of their ability to employ complex investment and hedging techniques. These are private entities with few public disclosure requirements. This is useful in a way as the strategies used are confidential which allows financial agents to participate in the financial markets without any fear of information leakage, hence promoting liquidity. However, this is often implied as a lack of transparency. Hedge Funds are expected to produce higher returns, but sometimes investors seek a risk guarantee in addition to higher returns. However, too much transparency rules out the incentives financial entities have by participating in the first place. On the other hand, too much secrecy may give rise to malicious entities that can break the rules due to a lack of compliance. We aim to solve this problem of protecting investors while ensuring the privacy of financial bodies using zero knowledge proofs. Proofs can be visualized as a way of providing enough information to investors while the zero-knowledge property of proofs maintains the privacy of the fund manager’s strategies. We propose a protocol to address this scenario using Zokrates, a framework for verifiable computation using Zk-SNARKs on Ethereum, to encode the constraints and export the verifier. Based on our implementation and analysis, it can be concluded that zero knowledge proofs provide us with a variety of ways to develop compliance systems.
Databáze: Directory of Open Access Journals