Regulatory reporting across dozens of jurisdictions and regulators is complex and manual. Between FFIEC call reports, OCC filings, APRA submissions, and board governance packages, teams spend weeks compiling data and formatting reports. Uno automates report generation, submission tracking, and provides audit-ready documentation across 50+ regulatory bodies.
Managing reporting obligations across multiple jurisdictions is a mounting challenge. FFIEC call reports, OCC supervisory filings, APRA prudential returns, PRA regulatory submissions, and DORA incident reports each demand different formats, timelines, and data sources. Teams manually compile data from disparate systems, format reports to exact specifications, track overlapping deadlines, and maintain audit trails proving chain of custody. A single late or inaccurate submission can trigger enforcement actions, increased scrutiny, and reputational harm.
From data aggregation to submission confirmation, Uno handles the entire regulatory reporting lifecycle. AI agents compile data, draft reports in required formats, track deadlines, and maintain complete audit trails so your team can focus on analysis and strategy rather than manual report assembly.
Pulls data from GRC modules, controls monitoring, and risk registers automatically. Connects to your existing systems to consolidate reporting data without manual extraction, transformation, or reconciliation across source systems.
Pre-built templates for major regulatory bodies updated with each regulatory change. Covers FFIEC, OCC, APRA, PRA, DORA, and dozens more - each template maintained to reflect the latest filing specifications and format requirements.
Built-in approval workflows with role-based access and digital signatures. Route reports through the correct review chain, capture sign-offs at each stage, and ensure only authorized personnel can approve final submissions to regulators.
Trend analysis across reporting periods with variance detection and commentary. Compare current filings against historical submissions, flag unexpected changes in reported values, and generate variance explanations for reviewer confidence.