The core issue commonly found in the current DeFi fixed income sector is the 'break in the value chain': the implicit values of assets, such as credit value and ecological rights, cannot flow across scenarios and are limited to single pledge or income scenarios; data barriers between scenarios lead to repeated risk control verification for the same asset, resulting in over 40% loss in value transmission efficiency; user behavior is disconnected from ecological rules, making it difficult to promote asset or scenario optimization even with high-frequency participation, ultimately forming a vicious cycle of 'asset idling, inefficient scenarios, and user loss'. Most protocols focus on optimizing single-point functions but ignore the connectivity of the 'asset-scenario-user' value chain, which limits the overall value creation capacity of the ecology.
The 'Fixed Income Value Chain Connectivity System (FVLC)' built by TreehouseFi breaks through the key nodes of value transmission through three core mechanisms: 'full-dimensional asset value transmission, cross-scenario data collaboration, and user behavior-driven iteration', reconstructing the value creation logic of DeFi fixed income while deeply aligning with the industry trend of RWA scaling and institutionalization.
1. Full-dimensional asset value transmission: breaking the limitations of 'single function'
The DeFi fixed income industry has long confined asset value to basic income, with its credit attributes (such as holding duration and performance records) and ecological rights (such as governance rights and empty investment qualifications) largely idled, failing to create cross-scenario reuse. The FVLC system achieves full-link transmission of asset value through a 'multi-dimensional value mapping architecture'.
On the technical level, relying on the ERC-4626 expansion standard and custom on-chain credit contracts, asset value is broken down into three transferable dimensions: first, the basic income dimension, anchored to a decentralized interest rate benchmark (DOR), combined with multi-chain market supply and demand dynamics to ensure that income synchronizes with fair market levels; second, the credit value dimension, generating credit certificates based on on-chain data such as asset holding duration, redemption timeliness, and cross-chain compliance, allowing for RWA scenarios without third-party endorsement—users holding assets for more than 90 days and with no default records can reduce collateral rates by 10%-15% when participating in RWA investments, addressing the pain point of credit value being unusable across scenarios; third, the ecological rights dimension, allowing users to participate in governance voting of the underlying public chain corresponding to the linked assets and obtaining early ecological participation opportunities, breaking the limitation of assets 'only containing financial attributes'.
This full-dimensional transmission model enhances the comprehensive value of assets by over 40% compared to the industry's single income model, while providing credible credit endorsement for RWA scenarios, aligning with the core demand for 'credit enhancement' in the current RWA scaling on-chain.
2. Cross-scenario data collaboration: breaking through the barriers of 'efficiency loss'
The independent operation of scenarios such as pledging, lending, and RWA has led to prominent data island issues—when institutions configure across scenarios, the same asset needs to undergo repeated risk assessments, with operation cycles lasting 3-5 days; retail users face more than 8 steps in cross-scenario operations, resulting in serious efficiency loss. The FVLC system constructs a cross-scenario data platform through a 'distributed scenario collaboration protocol', achieving a leap in value transmission efficiency.
The data platform achieves real-time sharing of three types of key data through standardized interfaces: first, risk control data, where KYC verification and risk ratings completed by users in any scenario can be synchronized to all related scenarios, allowing institutional users to avoid repetitive submission of qualification proofs and improving review efficiency by 70%; second, asset performance data, such as non-default records in pledge scenarios and full payment records of RWA, can serve as the basis for interest rate discounts in lending scenarios and collateral reduction certificates in derivatives scenarios, achieving 'one performance, multiple scenario rights'; third, market dynamic data, with multi-chain interest rate fluctuations and RWA industry prosperity data synchronized in real time to various scenarios, allowing scenario parameters (such as collateral rates and profit-sharing ratios) to automatically adjust with market changes, avoiding value loss caused by data lag.
In addition, the inter-scenario risk parameter linkage has also been realized—when the credit rating of a certain asset in an RWA scenario is downgraded, the pledge and lending scenarios will simultaneously adjust the collateral rate and lending amount of that asset, forming a risk prevention and control closed loop. This collaborative model increases the efficiency of institutional cross-scenario configuration by 3 times, reducing retail user operation steps to within 3, meeting the demand for 'efficient cross-scenario management' under the institutionalization trend.
3. User behavior-driven iteration: building a 'demand adaptation' closed loop
Traditional protocol asset rules (such as lock-up periods and redemption fee rates) are mostly statically preset, making it difficult for user behavior to reverse influence ecological adjustments, leading to long-term mismatches between demand and supply. The FVLC system creates a feedback closed loop of 'user behavior-ecological rules', making user behavior the core basis for the iteration of assets and scenarios.
Specifically, the system collects three types of key behavioral data through on-chain event listening and user-authorized data: operational habits (such as asset redemption frequency and cross-chain operation cycle), risk preference (such as RWA asset selection tendency and leverage usage frequency), and ecological participation (such as governance voting enthusiasm). After being verified by Chainlink Oracle, the data is analyzed by AI models to generate optimization suggestions, which are automatically implemented after lightweight community voting (support rate ≥ 51%)—if over 60% of retail users redeem assets more than twice a month, the system will increase the emergency redemption limit from 10% to 20%, and the redemption fee rate from 0.5% to 0.2%; if institutional users' demand for 'customized pledge ratio' exceeds 50%, a dedicated institutional pledge parameter configuration function will be added.
This demand-driven closed loop increases the user matching degree between assets and scenario rules from the industry average of 65% to 92%, enhancing user stickiness and making the ecology more aligned with real market needs, laying a foundation for long-term development.
Future trend prediction
Combining the development pace of RWA scaling and institutionalization in the DeFi fixed income sector, the value of the FVLC system will be further released within the next 12 months: TVL is expected to grow from the current scale to $2.5 billion, entering the top 15 of DeFi fixed income protocol TVL rankings; RWA cooperation scenarios will expand from the current 12 to 30, covering six core areas such as new energy, consumer loans, and mortgages; its constructed 'value chain connectivity' logic will become the industry standard paradigm, promoting the upgrade of DeFi fixed income from 'single functional tools' to 'ecological value carriers', providing a reusable technical path for global fixed income digitization.
