Bank of America: Innovation to Expand Decentralized Finance Functionality Over Time
Developer activity and adoption is the key to success, the report said.
The correction in digital asset markets last year led to a shift in focus and capital from speculative trading to projects with real-world functionality. However, decentralized finance’s (DeFi) current functionality “barely scratches the surface,” Bank of America (BAC) said in a research report Wednesday.
The bank says platforms like Gauntlet are driving the evolution of DeFi applications, but cautions that “regulatory barriers to entry, poor user interfaces and limited functionality likely prevent mainstream adoption in the short term.”
“The inability to identify users, access credit scores and perform [know your customer/anti-money laundering] requirements creates regulatory headwinds and limits use cases to trading platforms and overcollateralized lending products, which also creates headwinds for mainstream adoption,” analysts Alkesh Shah and Andrew Moss wrote.
Soulbound tokens, or non-transferable identity and reputation non-fungible tokens (NFT), could be the key to expanding DeFi’s “functionality frontier,” but the majority of existing applications are under three years and old and will require time to mature, the note said.
“DeFi applications require development to produce a differentiated product and positive user experience, which drives adoption and usage,” the report said, adding that “increasing adoption and usage result in increasing revenues and native token appreciation if properly designed, both of which can be reinvested in development.”
Bank of America says the majority of DeFi applications are immature, “but we remain in the early stages of a major change in applications that may take place over the next 30 years.”
In the longer term, the bank expects the development of DeFi applications with real-world functionality to increase the efficiency of traditional financial products and services, and it anticipates that these applications will evolve by “optimizing the trade-offs between user incentives and risks.”
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