Regulatory reports assembled manually from data extracted from five different systems, reconciled on a spreadsheet, and then reformatted into the submission template -- every single quarter?
A regulatory formula change arriving two weeks before submission deadline, requiring the team to manually re-run every affected calculation across the full reporting period?
Regulatory Reporting Automation
Regulatory reports are not documents you write. They are calculations applied to operational data -- pulled from multiple source systems, transformed according to regulatory formulas, and assembled into a submission-ready format on a schedule your regulator sets. Every quarter, every month, or every year, the same data gathering exercise runs again with slightly different numbers and the same manual effort.
We build automated regulatory reporting systems for financial services, healthcare, and other regulated industries -- pulling data from operational systems, applying regulatory calculations, validating the output, and producing submission-ready reports on schedule without manual assembly.
Regulatory data extraction and aggregation from multiple operational systems into a single governed data layer for reporting
Calculation engine that applies regulatory formulas to source data and produces auditable, traceable calculation outputs
Submission-ready report generation in the exact format your regulator accepts -- no manual reformatting before submission
Regulatory change management workflow so formula and format changes are implemented in the system before the next reporting cycle, not discovered after submission
RaftLabs builds automated regulatory reporting systems for financial services, healthcare, and regulated industries -- data extraction and aggregation from operational systems, calculation engines for regulatory formulas, submission-ready report generation, scheduled delivery, regulatory change management workflows, and multi-regulator reporting. Regulatory reporting automation projects typically cost $30,000 to $100,000 depending on report complexity, data source count, and whether calculation engine development is required.
Regulatory reporting is one of those operational costs that grows with the business but is never proportional to the business value it creates. More transactions mean more reporting lines. More jurisdictions mean more regulators. More regulatory changes mean more manual updates to the process that was already barely manageable. The team doing the reporting gets larger because the reporting volume grows, not because the business decided to invest there.
Automation does not eliminate the regulatory reporting obligation. It eliminates the manual data gathering, formula application, and report assembly that currently consumes most of the time the reporting cycle requires. The compliance team reviews and approves the output. They do not produce it.
What we build
Regulatory data extraction and aggregation
Automated data extraction from the operational systems that hold the source data for regulatory reports -- core banking systems, trading platforms, claims systems, EHR systems, and any other operational source the report draws from. Data aggregation layer that consolidates source data into a governed reporting data store with lineage tracking: every data point in the report traceable to its source record in the operational system. Data quality checks at extraction -- missing records, out-of-range values, and referential integrity failures flagged before they reach the calculation engine. Reconciliation between source system totals and the aggregated reporting data to catch extraction issues before report generation. The data foundation that gives auditors confidence the report reflects the operational reality.
Calculation engine for regulatory formulas
Configurable calculation engine that applies regulatory formulas to the source data and produces auditable calculation outputs. Formula definitions stored in a configuration layer -- separate from the core application code -- so regulatory formula changes are implemented and deployed without code changes to the underlying system. Calculation audit trail: every formula applied, every input value, every intermediate result, and the final output stored per reporting period. Formula version history so reports from previous periods can be reproduced using the formula set that was in effect at the time. Tolerance and validation rules built into the engine to flag outputs that fall outside expected ranges before the report is generated. The calculation engine that produces outputs auditors can trace and regulators can challenge.
Submission-ready report generation
Report generation in the exact format required by each regulator -- XBRL taxonomies for financial regulatory submissions, flat file formats for healthcare regulators, XML schemas for transaction reporting, and PDF reports for audit and board distribution. Format definitions maintained as configurable templates so when a regulator updates the reporting format, the template is updated without rebuilding the generation logic. Multi-format output for the same underlying data where a single report must be submitted to multiple parties in different formats. Validation of the generated report against the regulator's published validation rules before submission -- catching format errors before they reach the regulator rather than in a rejection notification.
Scheduled report delivery and distribution
Automated report generation triggered on the reporting schedule -- daily, monthly, quarterly, or annually -- without manual initiation. Delivery to regulator submission portals via API or secure file transfer where the regulator provides a programmatic submission mechanism. Internal distribution to compliance, finance, and board recipients in the appropriate format for each audience: submission format for the regulator, management summary for internal stakeholders, and detailed working papers for the compliance team. Delivery confirmation and receipt tracking -- a record that the report was submitted, when, and the acknowledgment received from the regulator. Submission deadline monitoring with alerts when a scheduled report generation fails before the deadline, leaving time to resolve the issue before the submission is late.
Regulatory change management workflows
Structured workflow for implementing regulatory changes -- formula updates, format changes, new reporting requirements, and threshold adjustments -- before they take effect. Regulatory change intake: when a regulatory update is published, a change record is created documenting the effective date, the specific changes required, and the systems affected. Impact assessment workflow identifying which formula definitions, data extraction queries, and report templates need to be updated. Change implementation and testing against the new requirements in a staging environment before deployment. Parallel running validation confirming the updated output matches the regulatory expectation. The change management process that implements regulatory updates on schedule rather than discovering after submission that the format changed.
Multi-regulator reporting for global operations
Regulatory reporting infrastructure for organisations operating under multiple regulatory frameworks across different jurisdictions -- managing the fact that similar underlying data must be reported differently to different regulators, on different schedules, in different formats. Regulator registry managing each regulatory relationship: submission deadlines, contact points, format requirements, and submission history. Shared data layer with jurisdiction-specific calculation and formatting layers on top -- so the same underlying operational data populates reports for multiple regulators without duplicating the data extraction infrastructure. Cross-regulator reconciliation checking that data reported to one regulator is consistent with data reported to another where the same underlying positions or transactions appear in both reports.
How many person-days does your regulatory reporting cycle consume, and what does a late or incorrect submission cost?
Tell us which reports you produce, the source systems involved, and the manual effort in the current process. We will scope the automation.
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Related services
Data Engineering -- data pipelines and governed data infrastructure for regulatory reporting
Business Intelligence -- BI dashboards and analytics alongside regulatory reporting systems
Frequently asked questions
Regulatory reports that automate well share a common structure: they are derived from operational data using defined formulas or aggregation rules, they follow a prescribed format, and they run on a fixed schedule. In financial services: capital adequacy reports (COREP, DFAST stress testing), liquidity reports (LCR, NSFR), transaction reporting (MiFID II, EMIR), and anti-money laundering suspicious activity reports. In healthcare: CMS quality measure reporting, Meaningful Use attestation, and payer-specific encounter data submissions. In other regulated industries: environmental emissions reports, workplace safety incident reports, and sector-specific regulatory filings. Reports that require significant narrative judgment from a subject matter expert -- qualitative risk assessments, board attestations -- automate at the data gathering and formatting layer while preserving the human sign-off step. Reports that are purely computational automate end-to-end.
Regulatory formulas change. Capital requirement calculations are updated by regulators periodically. Reporting thresholds shift. New line items are added to existing report formats. Building formula changes into the code as hardcoded logic means every change requires a developer and a deployment. We build the calculation engine with configurable formula definitions: the regulatory formula is defined in a rules configuration layer that non-developer compliance staff can update -- or that a developer can update quickly without touching the core system. When a formula change is published by the regulator, the change is implemented in the configuration layer, validated against the new expected output, and deployed before the next reporting cycle. The system also maintains the historical formula definitions so reports from previous periods can be reproduced using the formula that was in effect at the time, not the current formula.
Parallel running is the standard approach for validating a new automated reporting system before it replaces the manual process. We run both the automated system and the manual process for the same reporting period simultaneously, then compare the outputs line by line. Any discrepancy is a signal to investigate: it is either a data extraction issue (the automated system pulled different data than the manual process used), a formula implementation issue (the calculation in the system does not match the regulatory formula), or a data quality issue in the source systems (the automated system exposes data problems that the manual process was inadvertently masking). We run parallel cycles until the automated output matches the manual output with zero unexplained discrepancies. Only then is the automated system used for submission. The parallel running period is typically 2-3 reporting cycles, though it depends on reporting frequency and the complexity of the reconciliation.
Regulatory reporting automation cost depends on three factors: the number of distinct reports being automated, the number of source systems the data must be extracted from, and whether the reports require a bespoke calculation engine or follow a simpler data aggregation and formatting pattern. Simpler automation -- one or two reports, data from one or two source systems, straightforward aggregation rules -- typically runs $30,000 to $50,000. Complex implementations covering multiple reports across different regulatory frameworks, data from five or more source systems, bespoke calculation engines for complex formulas, and multi-regulator submission management typically run $70,000 to $100,000. We scope every project based on a discovery engagement that documents the reports required, the source data systems, the calculation methodology, and the submission format requirements.