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Financial Services

Compliance Reporting Automation

A regional bank ($5B in assets, 12 regulatory reporting requirements)

3x faster regulatory reporting, 85% reduction in manual data reconciliation

KEY RESULTS

3x
Faster report completion. 5-7 days vs. 15-20 days per reporting cycle.
85%
Reduction in manual reconciliation effort. 72 hours/month vs. 480 hours.
0
Deadline misses in past 18 months, vs. 4 per year previously. Regulator confidence restored.
$1.2M
Annual savings from labor efficiency and eliminated overtime.

Before & After

✗ Before

  • •Report preparation time: 15-20 days per cycle
  • •Manual reconciliation effort: 480 hours/month
  • •Deadline misses: 4 per year
  • •Regulatory findings: 8 per year (data quality)
  • •Compliance team overtime: 200 hours/month
  • •Data sources: 12 systems, manual extraction

✓ After

  • •Report preparation time: 5-7 days per cycle
  • •Manual reconciliation effort: 72 hours/month (85% reduction)
  • •Deadline misses: 0 in past 18 months
  • •Regulatory findings: 1 per year (data quality)
  • •Compliance team overtime: 30 hours/month
  • •Data sources: 12 systems, automated extraction

The Challenge

This regional bank faced mounting regulatory requirements across Federal Reserve, OCC, and state regulators—12 major reports with different data requirements, formats, and deadlines. Their 6-person compliance team spent 80% of time on data gathering and reconciliation, not analysis. Four deadline misses in one year triggered regulator concern.

PAIN POINTS

  • ×Data scattered across 12 legacy systems: core banking, loan origination, wealth management, etc.—each with different data models
  • ×Manual extraction required logging into each system, running reports, exporting to Excel, copying to templates
  • ×Reconciliation was a nightmare: data that should match didn't, requiring hours of investigation per discrepancy
  • ×80% of compliance team time spent on data mechanics, leaving 20% for actual compliance analysis
  • ×Deadline pressure led to shortcuts, which led to regulatory findings, which led to remediation projects

Technology Stack

  • →ETL pipeline connecting 12 source systems with automated data extraction
  • →Data quality engine with 2,000+ validation rules
  • →ML-powered reconciliation with discrepancy classification
  • →Regulatory report generator with template automation
  • →Audit trail system documenting every data transformation

Implementation Approach

1

Phase 1: Data Integration Layer (Weeks 1-4)

Built ETL pipelines to extract data from all 12 source systems automatically. Scheduled daily/weekly/monthly pulls aligned with report requirements. Created data warehouse with unified data model. Eliminated manual extraction entirely—data flows without human intervention.

2

Phase 2: Automated Reconciliation (Weeks 5-8)

Implemented reconciliation engine with 2,000+ validation rules codifying regulatory requirements and internal consistency checks. ML model classifies discrepancies by root cause: timing difference, data entry error, system calculation error, etc. Surfaces true issues; auto-resolves predictable discrepancies.

3

Phase 3: Report Generation (Weeks 9-11)

Automated report generation: validated data populates regulatory templates. AI drafts narrative sections based on data patterns and period-over-period changes. Compliance officers review and edit rather than write from scratch. Review time: 4 hours vs. 40 hours previously.

4

Phase 4: Audit Trail & Explainability (Weeks 12-13)

Built complete audit trail: every data point traceable to source system, every transformation documented, every validation logged. Regulatory examiners can see exactly where each number comes from. Reduced exam preparation time by 70%.

Results Breakdown

3x

Faster report completion. 5-7 days vs. 15-20 days per reporting cycle.

85%

Reduction in manual reconciliation effort. 72 hours/month vs. 480 hours.

0

Deadline misses in past 18 months, vs. 4 per year previously. Regulator confidence restored.

$1.2M

Annual savings from labor efficiency and eliminated overtime.

Key Learnings

  • 1.Compliance automation isn't just cost savings—it's risk reduction. Missed deadlines and data errors have real regulatory consequences.
  • 2.Start with the most painful report. Quick win builds internal champions and proves value for larger initiative.
  • 3.Audit trail is as important as automation. Regulators want to see data lineage. Build it from day one.
  • 4.Data quality rules encode institutional knowledge. When senior compliance officer retires, their expertise should live in the system.
  • 5.The real value is shifting from data gathering to analysis. Compliance team now actually has time to think about risk.

Industry Context

Financial services see 30% cost reduction potential with AI automation. Top performers achieve $10.30 return for every $1 invested in compliance automation. The regulatory burden is only increasing—institutions that can't automate will drown in manual processes. The competitive advantage isn't just efficiency; it's the ability to grow without proportional compliance cost increases.

TIMELINE

90 days for core system, ongoing optimization

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