When the financial leaders of the G-20 convened in Pittsburgh in 2009 to develop a framework for global regulatory reform, the overarching objectives were set, but the details were left to regulators across jurisdictions. Six years (and billions of dollars) later, the new market structure is taking shape.
Chief among the post-financial crisis mandates is the building of a robust and standardized system of global data distribution and storage. Though the task has proven to be difficult, and has required new technology, innovation, and more interconnectedness, the market has risen to meet the challenges. Maryse Gordon of UnaVista, London Stock Exchange Group’s trade repository and approved reporting mechanism, explains how UnaVista is approaching the challenges in data matching, validation, reconciliation and reporting across multiple jurisdictions.
The data issue, according to Gordon, involves adjusting for the differing specifications unique to each jurisdiction. “For example,” says Gordon, “in MiFID reporting, you have to identify a counterparty as a SWIFT code or an [FCA Reference Number] (FRN) code. With EMIR, you have to identify them with an LEI code. So there are disparities between the two when you are trying to harmonize.”
An additional challenge is that not all reporting entities submit using the same standard language.
“You’ve got some regulations reporting in CSV, some in XML, and XBRL is another one that is going to be introduced as well, so there isn’t really a common format that is being reported across jurisdictions,” she says.
As an Approved Reporting Mechanism (ARM) under MiFID II, UnaVista offers two main choices for reporting. Firms can either send information in a structured format, in which case UnaVista simply acts as a validation engine, and then sends the information along to the authorities.
The other model, which has gained in popularity as reporting complexity has grown, is to use UnaVista as a “rules engine,” which can accept data in any structured format. “We then add business and regulatory logic on top of that data, then normalize it, validate it, enrich it, and then distribute it out to the competent authority.”
The important thing when choosing a data engine, according to Gordon, is its flexibility. The regulations are always changing, and with each new rule comes changes to the reporting structure and requirements.
You can say that again.