UC4: Compliance Exception Report Generation

The Problem

Compliance teams generate daily/weekly exception reports gathering data from 5-8 systems (core banking, trading, payments, AML, market data, counterparty exposure). Each report requires manual data consolidation (4-6 hours), policy threshold checking, breach identification, and narrative write-up. Reports must be complete and accurate for regulatory examination readiness. Current error rate: 8-12% of reports contain missing data or miscalculated breaches. Rework and investigation burn 2-3 hours/report. 20+ reports/month across compliance function.

What the Agent Does

Receives exception report specification (policy scope, system sources, reporting period, distribution list)
Queries source systems via API (core banking, trading, payments, market data) to extract relevant transaction/exposure data
Applies configured policy thresholds (e.g., large transaction amounts, counterparty exposure limits, concentration limits)
Identifies exceptions (transactions/exposures exceeding thresholds) and enriches with context (customer, product, geography, counterparty)
Generates structured exception list with sorting/filtering options
Compiles narrative summary (# exceptions, top trends, any emerging risks) following template
Delivers report in required format (Excel, PDF, email) to distribution list
Maintains audit trail of data sources, extraction timestamps, and threshold logic applied

Data Requirements

Data Sources:

Core Banking System (Temenos T24, Finastra) , Customer accounts, transaction history, balances
Trading System (Bloomberg, Reuters, internal trading platform) , Trade details, positions, exposures, market data
Payments System (SWIFT, FedWire, ACH clearing) , Payment transactions, status, beneficiaries
Market Data Feed (Bloomberg, Reuters) , Current market prices, FX rates, credit spreads for valuation
Internal Policy Rules Database , Policy thresholds (large transaction amounts, concentration limits, counterparty exposure caps)
Data warehouse/Snowflake , Pre-aggregated exposure data, historical exception baselines

Data Classification:

Public: Market data feeds, general policy thresholds
Internal Structured: Customer account data, transaction data, policy exception rules, pre-aggregated exposure reports
Internal Unstructured: None
External Third-Party: Bloomberg feeds, Reuters data
Sensitive/Regulated: Customer transaction data, exposure data (confidential)

Data Quality Requirements:

Data freshness: intraday or real-time for transaction data (T+0 for end-of-day reports). Completeness: 99%+ of transactions captured from each source system. Accuracy: zero tolerance for missing transactions; policy threshold accuracy ±1%. Historical baseline data required for trend comparison (12 months minimum).

Integration Complexity: Medium , Requires API integration with 4-5 core banking/market systems (core banking, trading, payments clearing). Data transformation needed to map system-specific fields to common report schema. Policy rule engine may require custom development or Policy Technology platform (PolicyTech, Actimize). Cross-system reconciliation required.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	5	0.75
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%	5	0.50
Audience (Int/Ext)	10%	5	0.50
Composite	100%		4.35

Why It Scores Well

Data sources are well-defined and mostly accessible via API. Process is templated,exception reports follow consistent structure. High volume (20+ reports/month) and clear time savings (4-6 hours per report). Fallback is straightforward: switch to manual extraction. Internal audience. Clear compliance value: fewer errors, faster detection, audit-ready documentation.

Regulatory Alignment

Basel III (Pillar 3 to Public Disclosure)**: Agent supports timely, accurate regulatory metrics
CCAR/DFAST: Agent assists in exception tracking and governance reporting
MAS Notice 626 (Risk Management): Agent supports exception-based risk monitoring
AML/CFT: Agent detects transaction exceptions that trigger investigation

Sprint Factory Fit

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

Sprint 0: System integration architecture, policy threshold definition, reporting template design, data source access configuration

Build Sprints 1-3: API integration to each source system, data validation logic, threshold application, narrative generation, format/distribution automation, validation testing

Comparable Implementations

Citibank: Uses automated data gathering for compliance exception reports; 70% reduction in manual data entry
UBS: Deployed multi-source exception consolidation; 5-day cycle reduced to 1 day

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.