Regulatory reporting automation with traceable source data.
Regulatory reporting automation turns source transactions and reference data into validated returns with controlled exceptions, approvals and evidence lineage. KJ Capital builds bespoke reporting workflows that connect data mapping, calculations, validation, reviewer sign-off, submission status and remediation without hiding responsibility inside a black box.
Regulatory reporting owners, finance and operations leaders, compliance teams, data leaders and CTOs.
Regulatory reporting automation turns source transactions and reference data into validated returns with controlled exceptions, approvals and evidence lineage. KJ Capital builds bespoke reporting workflows that connect data mapping, calculations, validation, reviewer sign-off, submission status and remediation without hiding responsibility inside a black box.
The reporting control chain
Automation is credible only when every submitted value can be traced and exceptions cannot disappear silently.
Source and mapping
Authoritative data sources, field mappings, transformations and reference data are versioned.
Validation
Format, completeness, reconciliation and business rules run before review or submission.
Exceptions and approval
Breaks route to named owners; fixes, overrides and sign-off retain reasons and evidence.
Submission and remediation
Receipts, rejects, corrections, resubmissions and management reporting remain connected to the return.
Where AI adds value
AI can reduce manual analysis around the controlled reporting process, but deterministic rules should govern the return itself wherever possible.
Mapping assistance
Draft mappings and explain source-field candidates for human review.
Exception diagnosis
Group recurring breaks and retrieve relevant data, lineage and prior remediation.
Narrative support
Draft reviewer notes and management explanations from verified evidence.
Change impact
Compare source guidance and identify candidate data, control and workflow changes for accountable assessment.
Choose the boundary before the technology.
Buy established submission formats and regulatory utilities where available. Build the source mapping, reconciliation, exception and evidence workflows when the firm’s data estate and ownership model are the real cause of reporting risk.
- Automating file creation while exceptions remain in email and spreadsheets.
- Keeping transformations in undocumented scripts without ownership or version control.
- Using generated explanations that are not tied to verified reporting evidence.
- Losing submission receipts, corrections or approval history across systems.
Questions a serious buyer should ask
- 01Which source owns each reported field?
- 02Can every number be reconciled back to source?
- 03What blocks submission and what can be overridden?
- 04Who approves mappings, rules and corrections?
- 05How are rejects and resubmissions linked to the original return?
Frequently asked questions
Should AI calculate regulatory returns?
Deterministic, tested rules are preferable for reportable calculations. AI is better used to assist mapping, exception diagnosis, change analysis and evidence-grounded narrative work.
Can reporting automation work with legacy data?
Yes, but the delivery plan must expose data quality and ownership problems rather than conceal them. Reconciliation and exception workflows are part of the product.
What evidence should be retained?
The source snapshot or references, mapping and rule versions, validation results, exceptions, approvals, submitted artefact, receipt and any correction trail.
Does KJ Capital determine what a firm must report?
No. The firm’s qualified reporting, legal and compliance owners define the requirement; KJ Capital engineers the controlled workflow that implements it.
Regulatory references provide engineering context. KJ Capital does not provide legal advice; each firm’s qualified legal and compliance owners determine its obligations.
Related specialist capabilities
Turn the priority workflow into a buildable, controlled plan.
The £15,000 AI Diagnostic takes two weeks and produces the architecture, data and control map, delivery sequence and cost plan for the first production build.