AI that can explain itself to a regulator.
Banks have invested heavily in metadata governance for BCBS 239, FINREP, COREP, and credit risk. xflow converts that investment into executable context for AI, delivering cell-level lineage from model output back to source metadata.
Regulatory pressure on AI is already live
BCBS 239, FINREP, and COREP created the governance foundation. The EU AI Act and SR 11-7 now extend the evidence burden into AI and model use. The gap between governed metadata and usable AI context is where projects stall.
Banks that cannot demonstrate how AI reached a conclusion face the same regulatory exposure as banks that cannot trace how a risk number was calculated. The standard has been set. The tooling hasn't caught up.
Three places where xflow changes what is possible in banking AI
Each use case starts from the metadata your governance team has already built. xflow converts it into executable context for AI, with auditable lineage from day one.
Credit risk AI with traceable lineage
Credit risk aggregation under BCBS 239 requires that every risk number be traceable to its source. When AI assists with or automates any part of this aggregation, that lineage requirement follows the output.
xflow converts your risk metadata — counterparty hierarchies, exposure definitions, aggregation rules — into executable context that AI uses at inference time. Every AI-assisted risk calculation carries a lineage record back to the source metadata that produced it.
Regulatory reporting with AI-assisted validation
FINREP and COREP reporting chains are complex: source data flows through multiple transformation steps before reaching the final submission template. AI can validate, flag anomalies, and accelerate that chain — but only if it understands the transformation logic.
xflow converts your reporting metadata — template mappings, transformation rules, validation checks, data quality thresholds — into executable context for AI. Every AI-assisted validation can be traced back to the rule it applied and the metadata definition it used.
AI model governance with auditable context evidence
Model risk management frameworks require documentation of training data, assumptions, validation methodology, and known limitations. For AI models, this requirement extends to every context source the model uses at inference time.
xflow's context layer produces this evidence as a structural byproduct: every piece of metadata delivered to an AI model is logged, versioned, and traceable. Model risk teams get audit-ready context documentation without a separate evidence collection exercise.
Three owners. One shared problem.
The context layer for AI spans organisational boundaries in banks. Each accountable function has a different entry point to the same conversation.
Chief Data Officer
For data leaders who have built the governance foundation, xflow makes that investment usable by AI and regulated workflows. Definitions, lineage, ownership, and policy can travel with the use case instead of remaining inside the catalogue.
Head of Model Governance
For model governance teams managing SR 11-7 and AI model evidence, xflow provides context documentation as part of the deployment pattern: sources, lineage, rules, context versions, and policy checks are captured as the model is used.
Head of Risk Data / CRO
For risk data and CRO teams accountable for BCBS 239 and aggregation quality, xflow carries governed lineage into AI-assisted risk outputs. The result is a clearer route from reported number to source metadata, rule, and control evidence.
See xflow for your banking use case
Tell us your regulatory context and target use case. We will walk you through what executable context looks like for your specific governance stack.