Automation Workflow for Counterparty Exposure Aggregation and Reporting

Manual reconciliation of exposures across disparate prime brokers, OTC desks, and repo systems is a high-risk, low-value operational drag. A custom workflow automates the ingestion, normalization, and netting of positions from systems like Bloomberg, MarkitWire, and internal booking platforms, applying legal netting agreements automatically. This removes the primary source of reporting errors that can lead to regulatory fines or misstated risk.

Regulatory reports like Form PF, CRR, and Dodd-Frank exposure require complex aggregation across legal entities and product types. An automated workflow orchestrates data collection, applies the correct jurisdictional logic, and generates audit-ready submissions on schedule. This transforms a quarterly fire drill into a controlled, repeatable process, ensuring compliance without diverting quantitative or risk talent.

Without a centralized, automated view, firms fail to capture full netting benefits across swaps, repos, and securities financing, leading to excess collateral posting. This workflow continuously calculates net exposure by counterparty and legal netting set, triggering alerts when collateral can be recalled or when exposures breach optimal thresholds. The result is direct working capital and balance sheet efficiency.

Moving from batch to real-time exposure monitoring allows risk teams to see concentration buildup as it happens. The workflow integrates credit default swap (CDS) spreads and external ratings feeds, scoring counterparty health and triggering review workflows for deteriorating names before limits are breached. This shifts risk management from reactive reporting to proactive defense.

Implementation centers on a LangGraph or Temporal-based orchestrator that manages a pipeline of specialized agents: an Ingestion Agent normalizes feeds from APIs and files; a Netting Agent applies CSA and GMRA rules; a Calculation Agent runs exposure and CVA metrics; and a Reporting Agent formats outputs for internal dashboards and regulatory templates. Human-in-the-loop approval gates are embedded for limit breaches and report sign-off.

Trust in automation requires robust controls. The workflow includes an immutable audit trail of all data transformations and calculations, configurable exception queues for unmapped trades or data quality issues routed to operations, and simulation modes to vet logic changes against historical data before deployment. This ensures the system is both agile and defensible to internal audit and regulators.

This agent orchestrates the secure, scheduled pull of raw exposure data from disparate sources: prime broker portals (via headless browser automation), swap data repositories (SDRs) via APIs, and internal repo trading systems. It normalizes legal entity identifiers (LEIs), currency codes, and instrument types into a unified schema, applying data quality checks and alerting on missing or anomalous feeds before downstream calculation.

The core calculation component that applies legal netting agreements and collateral arrangements (CSA) to gross exposures. It aggregates positions across asset classes (rates, FX, equity derivatives) per counterparty, calculates net present value (NPV) and potential future exposure (PFE). This engine must integrate with margin systems to account for posted collateral, producing a true net economic exposure figure that drives risk decisions.

An LLM-powered agent that maps the aggregated, netted exposure data to specific regulatory frameworks (e.g., Form PF, SEC 13F, Dodd-Frank). It drafts the required narrative and quantitative disclosures, retrieves pre-approved language from a knowledge base, and formats the output for submission systems. The agent routes drafts through a human-in-the-loop approval gate within the compliance team's workflow (e.g., ServiceNow) before final filing.

Continuously monitors calculated net exposures against pre-configured credit limits, concentration thresholds, and regulatory ceilings. When a breach is detected or a threshold is approached, this orchestrator triggers a multi-step response: it pages the responsible credit officer, generates a breach report with context, and can initiate a circuit-breaker to block new trades with that counterparty via the Order Management System (OMS) API until the breach is resolved.

A managed workflow for discrepancies. This component compares the workflow's calculated exposures with prime broker statements and internal general ledger figures. Any breaks (DKs) are automatically classified (e.g., timing difference, booking error) and routed to the appropriate operations team's queue in Jira or ServiceNow. The system tracks resolution SLAs and can escalate stale exceptions, ensuring the golden source of exposure data remains accurate and trusted.

The build leverages LangGraph or Temporal for core stateful orchestration, ensuring fault-tolerant execution across long-running data pulls and calculations. Agents are containerized (Docker) and deployed on Kubernetes for scaling during reporting cycles. Critical integrations include: Snowflake for the exposure data warehouse, Salesforce for counterparty CRM data, and Mendix or Power Apps for the front-end dashboard used by credit officers. The entire workflow is instrumented with OpenTelemetry for observability.

The workflow's primary data source. Agents must connect to multiple Order Management Systems (OMS) and Execution Management Systems (EMS) via APIs or file drops to ingest live trade data for swaps, repos, and securities financing. This includes pulling notional values, counterparty IDs, and trade economics. The orchestration layer must handle schema normalization, deduplication, and real-time validation to ensure the exposure engine operates on a clean, unified trade blotter.

To calculate net exposure accurately, the workflow must integrate with systems like Murex, Calypso, or Bloomberg AIM to pull netting agreement details, collateral balances, and margin call data. This integration allows the aggregation logic to apply legally enforceable netting benefits, moving from gross to net counterparty exposure. Without this, reported figures are inflated and unusable for risk decisions or regulatory reporting.

For funds operating with multiple primes, exposure is fragmented. The workflow automates the collection of position and financing statements from Goldman Sachs, Morgan Stanley, JP Morgan, etc., typically via SFTP or dedicated APIs. An agent normalizes this data, reconciles it against internal records to catch breaks, and incorporates it into the aggregate view. This eliminates the manual, error-prone process of consolidating Excel reports from each broker.

Aggregated exposure data must feed into regulatory reporting frameworks (e.g., for Form PF, SFTR, Dodd-Frank). The workflow pushes validated, timestamped exposure snapshots to a centralized data lake or directly to reporting engines like AxiomSL or Broadridge. This creates an audit trail and ensures the firm can generate accurate, on-demand reports for regulators, turning a monthly fire drill into a controlled, automated process.

The core logic is built in an orchestration framework like LangGraph or Temporal, which sequences agents for data pull, validation, netting calculation, and report generation. A critical dependency is a robust exception handling queue (e.g., in Redis or RabbitMQ) to manage breaks—unmatched trades, missing netting agreements, data quality failures. These exceptions are routed to human operators in the middle office via Slack or ServiceNow tickets with full context for resolution.

The business value is realized through consumption. The workflow must integrate with BI tools (Tableau, Power BI) or a custom React dashboard to visualize real-time exposure by counterparty, region, and product. Furthermore, it triggers alerts via PagerDuty or Teams when exposures breach pre-set concentration limits or when data pipelines fail. This closes the loop from automated aggregation to proactive risk management.