AI-Driven Credit Scoring In Hybrid Cloud Banking: An Investigative Assessment Of Explainability And Regulatory Transparency Gaps

Authors: Amit Butail, Anamika

Abstract: The introduction of Artificial Intelligence (AI) into the credit scoring process has transformed banks' capacity to evaluate risk, however, the deployment of AI models in a hybrid cloud environment brings significant challenges of transparency and explainability in terms of regulations. This study examines the current state of AI credit scoring systems in hybrid cloud banking systems and systematically pinpoints the regulatory transparency challenges that hinder compliance with the banking regulations in the United States, including the Federal Reserve's Model Risk Management guidance (SR 11-7), the Federal Housing Administration's Fair Housing Act (FHA), the Equal Credit Opportunity Act (ECOA), and the National Institute of Standards and Technology's AI Risk Management Framework. To investigate the operational architecture, explainability challenges, and compliance issues of hybrid cloud AI credit scoring systems, a systematic literature review was performed based on the PRISMA framework, which included peer-reviewed publications, regulatory documents, and industry reports from 2020 to 2025. Here are three areas where we found insufficient transparency: (i) the “black box” issue with tracing back opaque decision-making processes due to Federal Reserve Board SR 11-7 remains unaddressed, (ii) lack of explainability mechanisms originating from consumer protection frameworks under the FHA and ECOA, and (iii) lack of transparency and accountability frameworks with AI models in distributed hybrid cloud systems. Therefore, the research contributes to a comprehensive classification of transparency and explainability gaps in hybrid cloud credit scoring, particularly in relation to US regulatory frameworks, and provides basic building blocks for explainable AI (XAI) systems that comply with regulatory requirements.