UC13: Regulatory Reporting Data Assembly

The Problem

Regulatory reporting (MAS 610, COREP, FR Y-9C, Stress Test returns) requires banks to extract and validate data from core banking, risk, and finance systems, then populate regulatory submission templates. Current process: finance team extracts data manually from multiple systems, validates against regulatory schema, identifies discrepancies, pre-populates submission templates, reviews for completeness. Median time: 40-60 hours per regulatory return. 15-30 returns/year across all regulatory bodies. Rework and reconciliation: 10-20 hours per return (data issues, definition mismatches, late data arrivals). Annual effort: 1000-2000 hours. Submission delays create regulatory friction and potential penalties.

What the Agent Does

Receives regulatory return specification (reporting template, scope, data requirements, deadline)
Queries source systems (core banking, finance, risk) via APIs to extract required data elements
Validates extracted data against regulatory schema: format, range checks, completeness, consistency with prior periods
Identifies discrepancies and gaps: missing data, inconsistent definitions, amounts that don't reconcile
Auto-populates regulatory submission template with extracted and validated data
Flags cells requiring manual entry or business judgment with "attention needed" indicators
Generates reconciliation report: reconciles submitted amounts to general ledger, identifies variances
Routes pre-populated submission to finance team for review, business judgment entry, and final submission
Tracks submission timeline and generates deadline alerts

Data Requirements

Data Sources:

Core Banking System (Temenos T24, Finastra, Oracle FLEXCUBE) , Customer accounts, balances, facilities, product codes, customer segmentation
Risk System (SAS Risk Management, Moody's RiskCalc, internal) , Credit risk exposures, counterparty exposures, risk-weighted assets, capital requirements
Finance System (SAP, Oracle, Intacct) , General ledger, P&L, balance sheet, accruals, fair value adjustments
Regulatory Return Specification (MAS 610, COREP, FR Y-9C, CCAR/DFAST templates) , Required data fields, definitions, validations, submission format
Data Warehouse/Snowflake , Pre-calculated regulatory metrics, historical submissions for comparison
Regulatory Validation Rules Database (internal) , Data type definitions, format requirements, range checks, consistency rules
Mapping Database , Core system field-to-regulatory-definition mappings, calculation rules, submission templates

Data Classification:

Public: Regulatory return specifications (publicly available)
Internal Structured: Core banking data, risk exposures, GL data, regulatory metrics, validation rules, mapping definitions
Internal Unstructured: Regulatory correspondence, submission guidance memos
External Third-Party: Risk/capital provider feeds (Moody's, SAS)
Sensitive/Regulated: Customer account data, risk exposures, proprietary risk metrics, regulatory submissions

Data Quality Requirements:

Data completeness: 99%+ of required data elements available at submission cutoff. Data timeliness: T+0 to T+5 depending on system (core banking T+0, GL T+1, risk metrics T+1-5). Accuracy: 100% reconciliation with source system GL and regulatory control totals (zero tolerance for discrepancies). Historical data availability: 3+ years for trend comparison.

Integration Complexity: High , Requires integration with 4-5 core systems (core banking, risk, finance). Regulatory return specifications change quarterly (per regulator updates). Field-to-definition mapping requires business rule codification and often needs manual review/adjustment. Validation rule engine may require specialized tool (e.g., PolicyTech, Alteryx, custom Python). Cross-system reconciliation adds complexity. Regulatory data governance required for audit trail.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	3	0.30
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	5	0.50
Composite	100%		3.85

Why It Scores Well

Data sources are well-defined (regulatory return specifies exactly which fields from which systems). Validation logic is explicit (regulatory schema is published). High frequency (15-30 returns/year) justifies investment. Clear time savings (reduce manual extraction/validation from 40-60 hours to 5-10 hours). Data quality improvement: reduces discrepancies and rework. Fallback is straightforward: finance team manually extracts data if agent fails. Internal audience. Clear compliance value: on-time submissions, audit-ready documentation, reduced regulatory friction.

Regulatory Alignment

MAS Notice 610 (Regulatory Data): Agent supports data extraction and validation for MAS submissions
COREP/FINREP (EU Regulatory Reporting): Agent aggregates data per ECB specifications
FR Y-9C (US Bank Holding Company Report): Agent supports data assembly for Federal Reserve
Dodd-Frank (Stress Testing): Agent assists in data preparation for CCAR/DFAST

Sprint Factory Fit

Sprint 0 (2 weeks) + 3 build sprints (6 weeks)

Sprint 0: Regulatory return specification analysis, source system mapping, validation rule codification, template schema definition

Build Sprints 1-3: Data extraction API integration, validation logic implementation, discrepancy identification, auto-population, reconciliation reporting

Comparable Implementations

UBS: Automated regulatory reporting data assembly; 60% faster submissions; zero discrepancies
Credit Suisse: Deployed multi-template regulatory reporting; reduced rework by 80%

Deploy This Use Case with the Sprint Factory

From zero to a governed, production agent in 6 weeks.

Sprint Factory Schedule a Briefing

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.