Decentralized Finance and Traditional Banking: A Machine Learning-Based Risk Assessment Framework
DOI:
https://doi.org/10.30750/ijarst.120105Keywords:
decentralised finance, banking risk, machine learning, risk assessment, smart-contract risk, financial stabilityAbstract
Decentralised finance (DeFi) protocols perform functions long reserved to banks, including lending, exchange and asset management, without the balance sheets, supervision and deposit insurance that structure banking risk. Risk assessment frameworks built for banks do not transfer directly, because DeFi adds risk factors that have no banking analogue, such as smart-contract code risk and oracle dependence, and removes others, such as regulatory capital. This paper develops a unified machine-learning risk assessment framework that scores both DeFi protocols and traditional banks on a common set of seven factors and examines it in a simulation of 1,200 entities. A gradient-boosting model attains an AUC of 0.83 across entity types. Permutation importance differs sharply by type: for banks, leverage (0.15) and governance (0.14) dominate, while code and oracle risk are irrelevant by construction; for DeFi protocols, leverage (0.06), collateral volatility (0.04), liquidity (0.04) and the DeFi-specific code and oracle risks (0.03 each) contribute more evenly, so that risk is distributed across more channels. The paper proposes a two-layer framework in which a shared factor model supports comparison and a type-specific layer captures the mechanisms unique to each, and discusses the implications for supervisors, for banks integrating DeFi services and for the risk governance of protocols themselves.
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