Use case
Use case3 min readBy The Data Workers Team

Credit risk data, run by agents

Exposure, collateral and limits data come from lending systems that change with every release, and risk runs on whatever loaded. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Exposure, collateral and limits data come from lending systems that change with every release, and risk runs on whatever loaded.
  • •What changes. Agents detect source changes, reconcile exposure to the ledger daily and hold model inputs for approval.
  • •What stays with your people. Credit model inputs: model risk rules require approval.
  • •The result. Credit models run on reconciled exposure every day.

The problem today

Exposure, collateral and limits data come from lending systems that change with every release, and risk runs on whatever loaded.

What changes with Data Workers

Agents detect source changes, reconcile exposure to the ledger daily and hold model inputs for approval.

The workflow: Schema Evolution agent (detect_schema_change) → Quality Monitoring agent (run_quality_check) → Data Context & Catalog agent (blast_radius_analysis) → Autonomous Data-Conductor (request_governance_review).
Fig. 1:The workflow: Schema Evolution agent (detect_schema_change) → Quality Monitoring agent (run_quality_check) → Data Context & Catalog agent (blast_radius_analysis) → Autonomous Data-Conductor (request_governance_review).

The workflow, step by step

  1. 1.The Schema Evolution agent calls detect_schema_change and catches lending-system changes.
  2. 2.The Quality Monitoring agent calls run_quality_check and ties exposure to the ledger.
  3. 3.The Data Context & Catalog agent calls blast_radius_analysis and shows affected models.
  4. 4.The Autonomous Data-Conductor calls request_governance_review and holds model inputs at L2.

The impact metrics we move

  • •Source change mapped: from about a week to under 1 h
  • •Exposure tie-out: from about a week to 8-15 min
  • •Impact on models: from about 2 days to minutes
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •Exposure ties to the ledger daily, so no surprises at quarter end.
  • •Source changes are mapped on load, so models never read unmapped data.

Where your people stay in charge

Credit model inputs always go to a named approver, because model risk rules require approval. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Daily exposure tie-out for one portfolio.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •Source change mapped: from about a week to under 1 h
  • •Exposure tie-out: from about a week to 8-15 min
  • •Impact on models: from about 2 days to minutes
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The financial services hub · The full team playbook

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