On February 23, 2012, the U.S. Commodity Futures Trading Commission (CFTC) adopted final rules regarding the internal business conduct of swap entities. The approved rules, detailed in CFTC 17 CFR Subpart F, impose sweeping record retention and production requirements for swap dealers (SD) and major swap participants (MSPs). Under these new rules, one of the most challenging aspects for both risk and compliance professionals is the work set surrounding the CFTC’s trade reconstruction requirements. Trade reconstruction imposes a new standard on swap entities, requiring affected firms to produce a time-sequenced completed reconstruction of a swap trade within 72 hours as requested by the CFTC. Firms must be able to retain and produce the following categories of information and records:
- Pre-trade data: Communication records on oral communications, email, instant messages, social media platforms, and other pertinent business documents.
- Trade data
- Post-trade data: Payments, amendments, collateral events, novation, valuations, expiry/maturity and terminations.
Fundamentals of Post-Trade Reporting
|Swap dealers are required to create and keep daily records of all swaps executed, including documents where transaction information is originally recorded. In order to reconstruct a swap trade, these records must be maintained on “write once, read many” (WORM) media and be searchable by transaction and counterparty. Trade execution and post-trade execution records include:
Key Regulatory Challenges and Solutions: Post-Trade Reporting
The section below provides examples of specific challenges that arise from post-trade reporting along with how native visual analytics address those challenges. Native visual analytics describes the process of running business analytics engines directly where the data resides and in a self-service manner. These capabilities are critical to enabling accurate and timely reporting that can connect the dots of trade lifecycle events.
|Regulatory Challenge||Enablement via Visual Analytics Capabilities|
|Portfolio Compression and Reconciliation Trading Books: Swap dealers are required to create and keep daily records of all swaps executed, including documentation and transaction information. The process of data compression is dependent upon the accuracy of when the trade was initiated, terms, fees, and other components. Depending on the complexity of the firm and whether that firm has multiple trading platforms, it could present dilemmas in overseeing trading repositories.||The data makeup of swap deals are multifaceted: unstructured (legal parameters), structured (quantitative values), and often maintained in disparate systems. The data is also continuously amended as risk is reduced through whole or partial contract terminations and amendments (portfolio compression). The real-time and historic analysis capabilities of native visual analytics is required to fully articulate what is happening across middle office, back office, risk management, collateral, and financial reporting functions.|
|Trade Reconstruction: Firms would need to quickly access large volumes of information from multiple sources of structured and unstructured datasets.||Record keeping, as it relates to trade reconstruction, is a massive connect-the-dots exercise. Native visual analytics provides the capabilities that enable the unstructured, structured, historical, and real-time pieces of complicated derivative trades to be pieced together. Self-service analytics with zero data movement is a holistic business intelligence solution that offers the multifunctional accessibility and transparency needed to address portfolio compression, reconciliation, and trade reconstruction challenges.|
Native visual analytics unifies data discovery, business intelligence, and real-time visualization in a single, integrated platform. It provides users with direct access to big data (it runs natively on Apache Hadoop clusters to leverage its capabilities) through an intuitive and self-service interface. Referring to the examples above, a team can explore billions of rows of highly varied data, join views into interactive packages, and share that package with relevant colleagues to solve problems in a timely, secure, and collaborative way.