AI analytics need auditability
As banks adopt AI in analytics stacks like Power BI and Microsoft Fabric, experts emphasize that governance and traceability are now mandatory—'black box' models won't pass regulatory scrutiny. Institutions must pair predictive models with clear provenance and audit trails for decisions used in credit and compliance. (technoedgels.com)
The CFPB’s September 19, 2023 guidance tells lenders using complex algorithms they must supply “specific and accurate reasons” for adverse credit actions rather than generic boilerplate, creating a legal requirement for decision provenance in underwriting. (consumerfinance.gov) The Federal Reserve’s SR 11-7 model risk framework — the supervisory standard for U.S. banks since 2011 — still governs documentation, validation and governance expectations for new AI models and continues to be cited in 2026 guidance on adapting model risk practices to ML systems. (federalreserve.gov) Microsoft positions Purview as the lineage and governance layer for Microsoft Fabric and Power BI, promising scan-and-catalog lineage “from data source down to the Power BI report,” which auditors can use to trace datasets and transformations. (learn.microsoft.com) Microsoft’s Azure Responsible AI toolset and Responsible AI dashboard provide model interpretability and fairness checks intended to produce explainability artifacts for each prediction, such as SHAP/LIME-style breakdowns and fairness metrics. (learn.microsoft.com) Databricks’ Unity Catalog captures runtime lineage down to the column level and retains audit logs across notebooks, jobs and dashboards, offering an alternative vendor approach to the same provenance requirement banks need for exam-readiness. (learn.microsoft.com) Solifi launched Solifi Document Intelligence on March 6, 2026, claiming up to a 70% reduction in document verification time for auto and equipment lenders, and its December 2025 acquisition of DataScan added wholesale/floorplan risk-audit capabilities to the platform. (solifi.com) Equipment finance lenders must pair predictive residual and remarketing models with auditable inputs because end-of-term decisions hinge on depreciation schedules and asset valuations tracked in loan systems, a capability Solifi’s equipment finance product and originations automation advertise for lifecycle reporting. (solifi.com) Regulatory and industry reports from the BIS FSI and risk bodies warn that opaque “black-box” models create supervisory friction, and examiners increasingly expect traceable model lineage, human-readable explanations, and documented validation evidence before allowing AI-driven credit or compliance decisions in production. (bis.org)
