DeFi TrustBoost: Blockchain and AI for Trustworthy Decentralized Financial Decisions

Paper: arXiv 2512.00142

Authors: Swati Sachan, Dale S. Fickett

Abstract

This research introduces the Decentralized Finance (DeFi) TrustBoost Framework, which combines blockchain technology and Explainable AI to address challenges faced by lenders underwriting small business loan applications from low-wealth households. The framework is designed with a strong emphasis on fulfilling four crucial requirements of blockchain and AI systems: confidentiality, compliance with data protection laws, resistance to adversarial attacks, and compliance with regulatory audits. It presents a technique for tamper-proof auditing of automated AI decisions and a strategy for on-chain (inside-blockchain) and off-chain data storage to facilitate collaboration within and across financial organizations.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper is conceptually focused on a framework integrating blockchain and AI for financial decisions, with minimal advanced mathematical derivations or heavy statistical modeling. While it outlines a system architecture and mentions technical components like hashing and deep neural networks, it lacks detailed implementation specifics, backtest results, code, or datasets typical of high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Trustworthy Decentralized Lending"] --> B["Data: Financial & Alternative Off-Chain Data"]
  B --> C["Method: Confidential XAI & Blockchain Auditing"]
  C --> D["Process: Tamper-proof AI Decision & On/Off-Chain Storage"]
  D --> E["Outcome: DeFi TrustBoost Framework"]
  E --> F["Results: Secure, Compliant, Transparent Lending Decisions"]