Automating Regulatory Compliance

Technology problems vs. process problems

Before automating, figure out what's actually broken:

• •Technology problem: Data exists in the right systems but extracting and formatting it is manual. Automation fixes this.
• •Process problem: Data doesn't exist, isn't captured correctly at the source, or requires human judgment calls. Automation can't fix this.

Example: Your Call Report requires loan classification data. If loan officers classify loans correctly at origination, automation can pull that data into the report. If classifications are wrong or missing, automation just gets you bad data faster.

What good compliance automation looks like

When the underlying data is sound, automation transforms compliance from a quarterly scramble to a routine process:

• •Automated extraction: Pull data from core systems, loan platforms, and other sources automatically. No manual exports.
• •Built-in validation: Check for errors before filing. Catch the missing fields and inconsistent values that cause examiner questions.
• •Pre-submission review: See the full report before filing. Compare to prior periods. Understand changes.
• •Audit trail: Every data point traceable to its source. Every change logged. Examiner asks a question, you have the answer in minutes.

The goal isn't "no humans involved." It's humans reviewing and approving clean reports instead of building them from scratch.

The data quality foundation

Regulatory reporting problems are usually data quality problems in disguise. The report is just where they become visible.

• •Source system accuracy: Is data entered correctly at the point of origination? Bad inputs mean bad outputs.
• •System reconciliation: Do different systems agree? If your core and your loan system show different balances, which is right?
• •Historical consistency: Can you compare periods accurately? Chart of accounts changes, system migrations, and definitional shifts break trending.
• •Edge cases: How are unusual situations handled? The exceptions are where errors hide.

Investment in upstream data quality pays dividends across every downstream use - not just compliance.

Audit readiness (the part everyone forgets)

Filing the report is half the job. Defending it during examination is the other half.

Examiners ask questions like:

• •"Where did this number come from?"
• •"Why is this different from last quarter?"
• •"Show me the loans included in this line item."
• •"How do you validate this before filing?"

Good compliance automation includes the audit trail, not just the filing. Every number should trace back to source records. Every change should be logged and explainable.

Common automation patterns

Most compliance automation projects follow similar patterns:

1. Data warehouse approach: Build a compliance data mart that pulls from all source systems. Reports query the warehouse, not operational systems directly.
2. Validation layer: Business rules that run before filing. Balance checks, reasonability tests, period-over-period comparisons.
3. Exception workflow: When validation fails, route to the right person for resolution. Track open exceptions.
4. Version control: Save every draft. Track changes. Know who approved what and when.

The specific technology matters less than the architecture. Whether you use SQL Server or Snowflake, the patterns are the same.

When automation makes sense

Not every institution needs full compliance automation. The investment makes sense when:

• •Staff time on regulatory reporting is significant (days per quarter, not hours).
• •Data quality issues have caused filing problems or examiner findings.
• •Key person risk exists - one or two people know how to file.
• •Growth is creating complexity beyond current manual capacity.
• •Leadership wants faster access to the data that feeds regulatory reports.

It may not be worth it for:

• •Very small institutions with simple filings and low volume.
• •Institutions that already have robust processes and acceptable risk levels.
• •Organizations not willing to address underlying data quality issues.