New rkAMM Model Uses Kelly Criterion for Risk-Based DeFi Lending

A new paper proposes the Reverse Kelly Automated Market Maker (rkAMM) to solve DeFi lending's core problem: severe over-collateralization that excludes under-collateralized real-world assets like corporate invoices. Current protocols like Aave and Compound rely on heuristic utilization curves that fail to price individual loan risk. The rkAMM inverts the Kelly Criterion—traditionally used for optimal bet sizing—to dynamically calculate interest rates based on real-time Probability of Default (PD) streams from an off-chain Explainable AI oracle. The mathematically derived pricing function (r = (y + PD) / (1 - PD)) is proven to be strictly convex, delivering superior capital efficiency. The architecture is implemented in Solidity smart contracts with gas-efficient 1e18 (WAD) arithmetic, and the simulation pipeline uses MLflow for yield tracking, DVC/DagsHub for versioning real-world asset data, and edge-inference via Ollama (Llama-3) and Hugging Face (FinBERT) for zero-cost predictive modeling.

Monte Carlo simulations across 10,000 macroeconomic stress scenarios confirm the rkAMM maintains protocol solvency and stabilizes liquidity provider yields at 12-15% net of expected credit losses. This is a significant improvement over current DeFi lending models, which often suffer from unsustainable yield volatility or demand extreme collateral ratios (150-200%). The work directly addresses the $2 trillion global supply chain finance gap by enabling permissionless, risk-adjusted lending for under-collateralized assets. While still a research paper, the combination of mathematical rigor, practical simulation, and tested smart contract implementation suggests a plausible path toward production deployment, potentially reshaping how DeFi interfaces with real-world business credit.