Leveraging Generative AI for Asset Tokenization
Iris Software Perspective Paper
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By Ashwani Kumar, Principal Architect at Iris Software
Context
Asset tokenization, in its simplest terms, is the process of converting ownership rights of an asset that has traditionally resided within legacy or traditional systems into a digital token on a Distributed Ledger Technology (DLT) platform. This transformation enables numerous benefits, such as fractional ownership, 24/7 availability, easier transferability, and enhanced liquidity.
Real-world Asset (RWA) tokens are gaining significant traction across a wide range of financial institutions. This indicates a major shift in adoption and emphasizes the growing importance of asset tokenization in the financial industry. This surge in interest, particularly from wealth managers and global financial markets, highlights the need for technology teams developing blockchain solutions, and business users exploring use cases, to create stable and scalable systems. Ideally, these systems will work seamlessly with traditional financial systems, harnessing the benefits of different blockchain networks. The convergence of Traditional Finance (TradFi) and Decentralized Finance (DeFi) not only promises to address current inefficiencies, but also unlocks new opportunities for innovation. Further, it democratizes investment opportunities, and expands global market access.
Asset tokenization, which primarily uses DLT as its backbone, faces challenges. These arise from the evolving nature of the technology, and include non-standardization, fragmented efforts, isolated blockchain networks, low interoperability, limited skilled resources, and issues of integration with legacy systems. These obstacles hinder adoption and create barriers for organizations seeking to implement solutions.
Globally, teams are addressing these challenges to accelerate adoption of asset tokenization, aided by the rise of Generative AI (Gen AI) tools that enhance the efficiency of key use cases. Deploying such platforms involves navigating a comprehensive Software Development Life Cycle (SDLC).
Developing and deploying a comprehensive asset tokenization system on DLT is a full-scale software development endeavor that encompasses all phases of the SDLC. Every phase presents challenges including technology complexities, evolving business use cases, non-standardization, scarcity of resources, and reluctance to adoption.
Figure 1 summarizes prominent issues at every SDLC phase in the development of an asset tokenization system.
Given these complexities, leveraging Gen AI tools throughout each phase of the SDLC can significantly enhance efficiency. Gen AI can assist in refining requirements, optimizing system design, and automating code generation. It can also identify potential issues during testing, and providing valuable insights during maintenance. Development teams can navigate the complex landscape of asset tokenization more effectively, overcoming challenges, and accelerating the delivery of robust and scalable solutions. We shall focus mainly on the Requirement Analysis & Planning and Deployment phases for the purpose of leveraging Gen AI tools, including prompts that can help greatly.
Requirement Analysis & Planning is a crucial phase in the SDLC, during which all necessary requirements for identified business processes are collected and organized. Since asset tokenization remains a niche area for much of the industry, using Gen AI tools can empower teams to explore solutions for both known challenges and unforeseen issues. The table that follows is a guide on helpful prompts and tools.
AI tools such as Notion, Mind, and Elicit, when used appropriately, can significantly streamline the planning process and support teams in generating innovative ideas and solutions.
Designing for asset tokenization is currently an uphill task due to the lack of standardization and the complexities arising from the convergence of on-chain and off-chain processes. The design phase necessitates thoughtful consideration of smart contracts, oracles (and their providers), regular web applications, and, most importantly, integration with legacy systems. Leveraging Gen AI tools can significantly assist architects and designers in expediting the entire process, thereby enhancing efficiency.
First and foremost, tools like ChatGPT can be invaluable. By crafting effective prompts, designers can obtain responses that help solve design challenges.
Writing smart contracts is an essential part of setting up the asset tokenization ecosystem. This is where all the logic lies, and it controls the life cycle of an asset. Smart contracts that depend on the asset and its real functionality can be of medium to high complexity. AI tools can help provide the crucial snippet of code that you can build upon.
Likewise, verification of decentralized applications (DApps) that are integrated with a legacy system using integration oracles is considerably more challenging and complex than testing regular applications. Furthermore, when the system involves assets of monetary value, the stakes are higher, making it imperative to give careful consideration during the testing phase. Tools that could be used are OpenAI Codex, Testim (AI-Powered Testing), and GitHub Copilot.
Deploying an asset tokenization solution involves numerous moving parts. These complexities can make the deployment process fragmented, challenging, and time-consuming. Most deployment-phase tools, such as Ansible, Docker, and Kubernetes, have started providing Gen AI support. In the case of DLT-based applications, such as asset tokenization, crafting specific prompts that address tools like Hardhat and Truffle could be of great help.
Incorporating Gen AI during the maintenance phase can significantly augment these efforts. It can assist in predictive maintenance by analyzing system logs and performance data to forecast potential failures before they occur. Additionally, AI-driven tools can automate routine maintenance tasks, identify optimization opportunities in smart contracts, and provide advanced threat detection to bolster security
Some of the tools that can be used are OpenAI ChatGPT, Blocknative, Chainalysis, etc. The table that follows lists prompts that could help.
As asset tokenization emerges as an essential solution for financial institutions, the integration of Gen AI amplifies customer value. By leveraging Gen AI's capabilities throughout the asset tokenization process, institutions can achieve unprecedented levels of efficiency, accuracy, and innovation.
The fusion of these technologies fosters the development of robust platforms and new financial products that are better equipped to handle market complexities, regulatory requirements, and customer needs. That, in turn, enhances market liquidity and promotes financial inclusion by lowering barriers to entry for investors. Convergence not only enhances operational performance but also paves the way for a more resilient, transparent, and dynamic financial ecosystem, delivering exceptional results in all phases of the SDLC.
Asset tokenization is proving to be a boon for asset-holders and market-makers, and its adoption is on the rise. The potential benefits of blockchain technology, such as instantaneous settlement, 24/7 availability, the opening of new avenues, and the embedding of programmability within the token itself, are clearly visible. While institutions are realizing these operational and cost benefits, challenges persist, primarily due to technological limitations that are still being addressed, high initial costs, market reluctance to embrace change, and regulatory compliance issues.
Gen AI is set to play a pivotal role in improving asset tokenization by contributing to the different phases of its implementation. Gen AI can assist not only in the implementation phase but also beforehand, as it can help produce synthetic financial data that closely resembles real market conditions, conduct stress tests and other simulations, helping to strengthen the platform. By simulating market scenarios, it may help automate compliance tasks and ensure adherence to ever-evolving financial regulations within asset tokenization. It can also empower asset managers to develop strategies that can withstand extreme events and unforeseen challenges.
Leveraging Generative AI in DLT-based asset tokenization can enhance the efficiency, security, and user experience of the platform. From smart contract development and market analysis to compliance and customer support, Gen AI can play a crucial role in transforming how assets are tokenized and managed on blockchain networks.
Ashwani Kumar, Principal Architect, Iris Software, is the author of the book, Hyperledger Fabric In Depth. Ashwani has deep knowledge and experience with digital ledger technology (DLT) and blockchain platforms such as Ethereum, Hyperledger Fabric & R3 Corda. A technology enthusiast, he has successfully developed and delivered numerous large-scale enterprise solutions.
Iris Software has been a trusted software engineering partner to Fortune 500 companies for over three decades. We help clients realize the full potential of technology-enabled transformation by bringing together a unique blend of domain knowledge, best-of-breed technologies, and experience executing essential and critical application development engagements. With over 4,300 skilled professionals, Iris builds mission-critical technology solutions across banking, financial services, insurance, life sciences, logistics, and manufacturing. Iris Automation services and solutions include Intelligent Automation,Quality Engineering, and Low-code No-code development.Click here to read about the milestones of our 30-year journey.
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