In an industry with so much value on the line, errors can still happen easily and often. Manual mistakes like transposition errors and misspellings aren’t the only culprits. Even supposedly “clean” data may be incorrectly parsed, incomplete or simply outdated. Inaccuracies aren’t always easy to detect, and, with today’s massive computing power, they can multiply at an exponential rate.
The result: even a small error can result in trading delays, less-than-optimal client experiences or regulatory penalties. Clients, trading partners and regulators rightfully demand accuracy and accountability in every transaction, communication and regulatory report. But capital market firms often come up short.
The cost of settling for less
Data management executives at Capital Markets firms report that data quality is their biggest challenge1 , and stricter post-crisis regulations increasingly require greater data transparency. Yet, firms tend to settle for “good enough”, regulation-specific fixes. This approach is cumbersome, costly and can become unworkable over time. As the pace of regulatory change accelerates, unnecessary complexity, delays and expense accelerate with it.
A new perspective on data management
Implementing an enterprise-wide data management strategy doesn’t need to take a back seat to trade execution. Regulatory change is unavoidable, but the way firms choose to tackle their data management challenges can make all the difference. A more holistic approach looks for broader opportunities in every change. More than simply addressing the latest regulatory requirement, it considers the downstream impacts of improved data quality—including operational flexibility, greater clarity for decision making and an enhanced customer experience. It helps transcend Individual dedicated systems and information silos established by individual departments and lines of business. This makes it easier to share and update data across the enterprise.
Detailed data on demand
Why is this approach so important now? Regulators are asking for more types of information about more asset classes and more transaction categories. They also require visibility into the data in context, in some cases, in near real time.
For example, Consolidated Audit Trail (CAT) requires firms to capture data about the complete trade lifecycle, including origination, modification, cancellation, routing, execution, allocation and receipt of a routed order. The Securities Finance Trade Regulation (SFTR) requirements include 153 reportable fields. Ninety-six of these will eventually be required to match with those reported by the counterparty to the transaction. For an efficient matching process, data transparency and accuracy are essential.
Similarly, MiFID II reporting covers key aspects of pre- and post-trade activities in addition to the transaction itself. Firms need to prove that trades are carried out in accordance with their own published best execution policies. This requires an ability to quickly assess data on price, cost, speed, size and type of orders, and more. And it all must be reported within a matter of minutes.
Making data fit for purpose
As regulators ratchet up their expectations, firms must be able to quickly demonstrate compliance across multiple functions—from trading and portfolio management to risk management, transaction processing and more. This demands a broader definition of accuracy in customer and transaction data:
- Correct
- Complete
- Timely
- Contextual
When firms can see how quality data is sourced and linked to clients, accounts and transactions, they stand to reap rewards on many levels.
The new core competency
Easy access to accurate, complete and timely data does much more than simplify compliance reporting. Data transparency empowers firms to assess risk more accurately, make better-informed decisions and provide fast answers to customers’ questions.
Transforming raw inputs from multiple sources, in disparate formats, into robust, reliable reference data provides a more holistic and detailed view of business operations, enabling greater cost efficiencies via straight-through processing and automated reporting. A more strategic data management strategy also sets the foundation for advanced technologies such as artificial intelligence and machine learning. These have the power to uncover valuable insights, improve the customer experience and deliver a significant competitive advantage.
“Creating a consistent, universal reporting methodology which does not compromise on accuracy, completeness and timeliness will be a core competence in the new era.”
Is your firm ready for a smarter, more strategic data management strategy? Broadridge can help you stay ahead of regulatory complexity and capitalize on the insights hidden in your trading and customer data. We see what’s coming because the industry runs through us. Explore the leading technologies, operations and expertise that add transparency and control at every step.
