The rapid growth of e-commerce has reshaped retail and created new financing opportunities for small and medium-sized enterprises (SMEs), particularly through closer collaboration with traditional banks. New financing models now leverage digital cash flows from e-commerce platforms as collateral, enabled by advanced credit risk technologies known as the AIFT model. Building on Merton’s structural framework and extending beyond the KMV model, which is limited to publicly listed firms, the AIFT model enables default risk assessment for unlisted, digital-native merchants by linking projected revenue distributions to bond and option valuations. This allows credit structures to dynamically adjust to real-time revenue changes, significantly improving risk assessment for SMEs and cross-border e-commerce merchants and opening the door to structured financial products.
The AIFT model underpins the development of structured financing vehicles such as Special Purpose Vehicles (SPVs) and Special Purpose Acquisition Companies (SPACs) for e-commerce lending. SPVs enable asset pooling and securitization to diversify risk and stabilize investor cash flows, while SPACs provide a dynamic mechanism for scaling capital to high-growth merchants. The resulting portfolio optimization problem resembles classical capacity allocation but is complicated by uncertain cash flows, correlated defaults, and interactions between short- and long-term loans. Over the past five years, enhanced KMV methods, the AIFT model, and transformer-based neural networks have been developed to estimate default risk at the single-loan level; scaling this to portfolios requires explicit modeling of correlated risks and dynamic portfolio effects as capital is reallocated over time.
To address these challenges, the project proposes an integrated, AI-driven platform that combines static securitization (SPV and Asset-Backed Securities, ABS) with dynamic portfolio optimization (SPAC). Central to this platform is a state-of-the-art continuous-time reinforcement learning (CTRL) framework that continuously ingests real-time alternative data to update revenue volatility, default probabilities, and risk scenarios using diffusion models. This AI layer is complemented by a blockchain-based system, enabling real-time settlement of ABS/RWA (Real-World Assets) assets. The project roadmap starts with building a unified data infrastructure and AI risk models, deploying static ABS/SPV, and is followed by a dynamic SPAC module that supports real-time capital allocation, overbooking, dynamic pricing, and adaptive risk management tailored to the evolving e-commerce markets.
