Cloudcase AI
AI that actuallyunderstands lending
Lending-native automation embedded in the Cloudcase platform: classify documents, extract lending-relevant data, and flag anomalies, with audit trails and human oversight.
Lending-native
Built for regulated origination, not generic document processing.
Explainable
Flags show what and why, with supporting evidence attached.
Workflow-native
Findings create tasks, route queues, and escalate by policy.
How it works
Agents chain into one reviewable insight
Document agents work per file. A case agent reasons across the whole application. Outputs land in workflow, never straight to a decision.
Document agents
Case agent
Reasons across every document, data source, and bureau feed on the application
Reviewable outputs
Human oversight stays central: agents draft, your team decides
Agentic AI
Document agents + case agents
Chain agents to produce reviewable insights across the whole application.
Document agents
Work on individual documents
Classified: Payslip
payslip-march.pdf · 99.2% confidence
Extracted income, deductions, ratios
14 fields captured to case data
Authenticity check passed
No tamper signals detected
PII identified in 3 fields
Routed to data-protection workflow
- Automatic classification (payslip, bank statement, ID, valuation)
- Data extraction into case fields
- Authenticity checks + tamper signals
Case agents
Work across the full application
Income verified from 3 sources
Payslips · bank statements · bureau data
Employment assessed as stable
4.2 years tenure, consistent pay cycle
Exception: net pay mismatch
Requires human judgment. Task created
Recommendation drafted (reviewable)
Approve with 2 conditions
- Verify income using all available sources
- Surface exceptions requiring human judgment
- Generate recommendations + conditions (reviewable)
Configurable agents
Agents your team defines: Role, Goal, Prompt
Cloudcase AI is a configurable agent framework, not a vendor black box. Encode your own banking knowledge into each agent, test it, and deploy it under governance.
Agent configuration
Income verification agent · v3
Role
Senior credit assessor specialising in income verification
Goal
Verify declared income against payslips, bank statements, and bureau data
Prompt
Compare net pay across all sources. Flag variances above the policy threshold and draft a task for the assessor with the evidence attached…
Test run · case SME-118402
Net pay variance of 4.1% found between payslip and bank statement, below the 5% policy threshold. No task required. 2 sources verified.
Powered by your expertise, not a generic model
Every agent is assembled from Role, Goal, and Prompt components your team controls, the same configuration-first philosophy as the rest of the platform.
- Define agents with Role, Goal, and Prompt (low-code)
- Encode your credit policy and banking knowledge
- Test and review behaviour before deployment
- Tune automation levels to your risk appetite
- Versioned, with a full audit of configuration changes
Governance
Fully auditable and explainable
Every execution produces a record that your team and auditors can review: AI actions and human actions, side by side.
| Time | Actor | Action | Confidence | Status |
|---|---|---|---|---|
| 09:41:02 | Document agent · Classifier | Classified payslip-march.pdf → Payslip | 99.2% | Logged |
| 09:41:04 | Document agent · Extractor | Extracted 14 fields (income, employer, cycle…) | 97.8% | Logged |
| 09:41:05 | Document agent · Integrity | Tamper scan clean · PII identified (3 fields) | — | Logged |
| 09:41:06 | Case agent · Income | Net pay mismatch vs. stated income flagged | 91.4% | Task created |
| 09:42:18 | J. Okafor (Assessor) | Reviewed flag · Requested updated payslip | — | Human action |
Full audit trail
Inputs, timestamps, versions, insights, and confidence captured with the case.
Transparent configuration
Review and test agent role, goal, and prompt before deployment.
Human oversight
Enforce approvals and four-eyes on AI-flagged exceptions.
See Cloudcase AI in your process
See how Cloudcase AI plugs into your workflow and governance model, and what your team can own in configuration.