Self-Adjusting Contracts: Toward AI-Augmented Predictive Risk Intelligence in Blockchain-Enabled Supply Chain Finance
Main article
Abstract
Supply chain finance (SCF) lowers the cost of working capital for firms that are weakly served by traditional lending, yet the instruments that carry it remain static: terms are fixed at origination and rarely respond to the operational events that actually move credit risk. This review examines an emerging alternative, the self-adjusting contract, in which a blockchain-based smart contract continuously consumes predictions from artificial intelligence (AI) risk models and updates financing parameters such as advance rates, spreads, collateral ratios, and covenants without manual renegotiation. Synthesising 50 studies published between 2015 and 2025 across blockchain-enabled supply chains, smart-contract engineering, machine learning for credit and supply chain risk, and decentralised and trade finance, we organise the field around a four-layer reference architecture that links on-chain and enterprise data, a predictive risk engine, self-adjusting contract logic, and governed financing outcomes. We then provide an illustrative data analysis that contrasts conventional and AI-augmented blockchain SCF across supplier tiers, compares the discriminatory power of competing risk-model families, traces how a predicted risk index drives automatic adjustment of financing terms through a stress episode, and decomposes the indicators that contribute most to small-enterprise credit risk. The synthesis argues that the strategic value of combining blockchain and AI in SCF lies not in disintermediation alone but in closing the loop between sensing risk and pricing it, while the principal obstacles are oracle integrity, model opacity, and governance of automated adjustment. We conclude with a research agenda spanning verifiable oracles, interpretable and auditable models, and human oversight of self-executing financial logic.
