The tokenization of real-world assets (RWAs) is considered to be the next potential “killer use case” for the crypto industry, with a projected market size of $4 trillion to $5 trillion by 2030 according to a report from Citi. While the potential for tokenization through blockchain is acknowledged as transformative, mass adoption is yet to be achieved. However, industry experts believe that the integration of artificial intelligence (AI) solutions could play a significant role in advancing the use cases for tokenized RWAs.
RWA tokenization has been a concept for several years, dating back to the popularity of security token offerings (STOs) in 2018. Unlike previous STOs, today’s tokenized RWAs have a degree of tangibility. Assets like art, diamonds, and real estate can now be fractionalized through tokenization, allowing investors to own a portion of the asset and receive income from its use. Vertalo, a digital transfer agent and enterprise software platform, has been involved in tokenizing RWAs since 2018, contributing to the growth of the decentralized finance (DeFi) ecosystem.
Tokenizing RWAs offers numerous benefits, such as increased tradability, transparency, and enhanced mid/back-office applications within traditional asset management functions. With the emerging interest from large financial institutions in utilizing distributed ledger technology to improve asset management functions, the potential for tokenized RWAs is becoming increasingly recognized. Leading companies like Blackrock and Mastercard have already shown interest in exploring the possibilities of tokenized asset settlement through blockchain technology.
While the tokenization of RWAs shows promising growth, the role of AI in advancing use cases is also being highlighted by industry experts. AI can enable asset value prediction and assist in better understanding the future valuation of RWA tokens. Tokenization platform RealCap is utilizing AI to determine the price of RWAs, particularly in cases where pricing information is limited, such as rare artwork. By leveraging AI solutions like virtual fungibility, tokenized RWAs can be made more tradable with reduced price discrepancies, ultimately benefiting investors.
In addition to predictive pricing, AI is also being applied to automate RWA workflow analysis, streamline smart contract creation, and optimize customer risk analysis. Platforms like Propy are utilizing AI to automatically streamline transaction timelines, creating a smoother, more secure, and transparent property investment process. Despite the potential benefits of AI in advancing tokenized RWA use cases, challenges related to data accessibility, privacy concerns, and regulatory issues need to be addressed to ensure successful adoption.
Primary challenges associated with tokenized RWAs include regulatory scrutiny, asset verification, and ensuring the authenticity of assets backing RWA tokens. Regulatory issues may arise when tokenizing assets that could potentially be classified as securities, impacting the incentives for RWA tokens. Verifying the authenticity of assets and addressing investor skepticism about token backing also pose significant challenges. Careful structuring and adherence to regulatory requirements are essential for the success of tokenized RWAs, with considerations for factors like collateral, interest rates, accreditation, KYC, and AML procedures.
