FATE-Compliant ML Architecture with Blockchain-Verifiable Auditing: A Governance Framework for Ethical Compliance in FinTech

Abstract

Concerns about fairness, accountability, transparency, and ethics (FATE) have intensified with the rapid adoption of arti- ficial intelligence (AI) in financial technology (FinTech). Al- gorithmic lending systems risk entrenching socioeconomic and geographic disparities. To address these risks—and to align with emerging regulation such as the EU AI Act—we propose a multi-layer governance framework for credit-risk modelling that integrates: (i) blockchain-verifiable auditing via smart contracts (Hyperledger Fabric); (ii) causal mod- elling with counterfactual, pathway-aware fairness quanti- fied by the Regional Inclusion Score; (iii) a hybrid data pipeline that fuses local micro-data with controlled geospa- tial skew; and (iv) a fairness-aware gradient-boosted learner (FAIRXGBOOST) embedded in a continuous monitoring loop. The smart-contract layer provides cryptographic au- ditability of data lineage, model updates, and alerts, oper- ationalizing fairness as a regulatory constraint that triggers on-chain notifications when RIS declines. Across benchmark credit datasets with induced regional skew, the system main- tained RIS at or above the regulatory threshold of $0.85$ while preserving competitive predictive performance, with automatic alerts when fairness degraded—demonstrating how causal analysis and ledger-backed governance can jointly yield reliable, regulator-verifiable AI for FinTech.