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Our consulting and services teams are experts in financial data, with practical experience in over 65 banking and buy-side implementations around the world.

From post-trade processing to independent price verification and stress testing, Asset Control provides insight-driven financial data management solutions for any application. A solid data sourcing and mastering process is combined with easy ways to distribute, integrate, discover and explore the data.

Asset Control’s provides a standard data model that tracks regulatory developments and keeps integration with content providers up to date. Its business rules capabilities can be used for risk factor classification, taxonomies, proxy management, risk factor preparation and eligibility tests

Asset Control’s provides a standard data model that tracks regulatory developments and keeps integration with content providers up to date. Its business rules capabilities can be used for risk factor classification, taxonomies, proxy management, risk factor preparation and eligibility tests

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Our range of products help financial organisations deliver high-quality reference, market and risk data to the people and applications that need it – on time, all the time

Meet the specific data requirements of risk management and new regulation

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The industries largest managed data lakes for seamless data acquisitions

The growth in data volumes and in data diversity coupled with increasingly data intensive jobs and reporting requirements mean firms need to improve their market data access and analytics capabilities.

Asset Control offers flexible integration options with a firm’s existing infrastructure for greater speed, scalability and performance

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Asset Control helps financial organisations deliver high-quality reference, market and risk data to the people and applications that need it – on time, all the time

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Optimize operational costs and cost-effectively process change through using our AC PaSS managed data services.

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Lighter regulatory reform is an opportunity for data management best practice

Martijn Groot, VP Product Management, Asset Control, makes the case, in an era of escalating data costs, for a new deployment of data management policies, processes and technologies.

The implications of the potential roll back on regulation, from Dodd-Frank to a new level of commitment to EMIR and MiFID as a result of the Brexit negotiations, are hard to predict. While regulatory compliance (and the associated cost) is seen by some financial institutions as a significant constraint, others have come to recognise the operational benefits associated with better data management.

Stress testing and scenario management, for example, while mandated in some jurisdictions, have actually been adopted by organisations outside the regulated areas due to the risk mitigation benefits provided. Similarly, while the retention of data for up to seven years is mandated by regulators, there are also benefits associated with retaining long histories of quote streams or the use of data mining to improve trading strategies.

The difference, of course, in a market not dominated by regulatory demands is the way in which organisations can approach the timing and evolution of EDM projects, as well as the way in which vendors will have to respond to make a new business case for investment that reflects cost and business value.

Cost versus Control

The different approaches to EDM deployment that have evolved over the past decade on either side of the Atlantic reflect the new potential for data management best practice. In the heavily regulated EU, banks have invested heavily in EDM in a bid to attain, retain and report on the diverse and complex information sources required by the regulators. And the costs are indicative: for every £1 spent obtaining data, organisations spend upwards of £10 in managing that data due to the combination of escalating data volumes and the complexity of regulatory classification demands. Legacy systems cannot manage the new data demands, leading some organisations to opt for the additional cost of fast track, external reporting solutions.

In the US, in contrast, a lighter regulatory touch has enabled firms to take a different approach towards EDM, where the technology has largely been harnessed to drive down the cost of data ownership through the adoption of scalable, flexible, subscription based, and often cloud solutions. With the right model, that data management cost can be cut in half.

However, the situation is not, in fact, as clear cut as a ‘regulatory versus cost-based’  EDM deployment. The additional data rigour created in response to regulatory expectations has today actually placed many firms on the cusp of significant financial benefit.

Data Rigour

The focus on data modelling and data scope, combined with the prescribed adoption of data standards – such as Legal Entity Identifier (LEI) for counterparties, CFI classification for financial products and the wider adoption of standard product identifiers outside bonds and equities – provide long term benefits. Once the initial compliance requirement has been met, this robust, standards-based model allows organisations to move away from managing multiple data sources / databases towards operationalising reporting processes and looking for internal efficiencies.

There are undoubtedly data collection and retention strategies in place today that are 100 per cent focused on regulatory compliance and may not be required within a less regulated market. However, the ability to leverage this standards-based approach to data that has been mandated by regulators will provide organisations with an opportunity to address the data management cost by eradicating much of the expensive data duplication currently in place.

With this foundation, organisations can embrace the cost driven approach already in place within the US. With an emphasis on buying data efficiently and achieving the lowest possible cost of ownership, firms can explore cloud deployment, scalable infrastructure, cost models that flex with usage and a scalable data model that supports any new data structures required and created for new products.

Conclusion

Predicting the regulatory landscape is  a difficult game; in ten years will the US and UK have turned away from Basel, from MiFID II and Dodd-Frank, or will another crisis have sparked a further tightening of regulation? The fact is that the regulatory environment of the past decade has led organisations to embrace new data standards and invest in technologies and data management models designed to support the new data retention and reporting requirements.

Data management professionals should not face a battle between an investment in EDM to support regulatory objectives versus one focused on operational improvements but a combination of the two. By leveraging the benefits of a standardised data model within a cost first mind-set, organisations can attain both data flexibility and data rigour. They can slowly evolve from interim, regulatory focused solutions towards fully operationalised systems that deliver essential data insight and, critically, they can do so using more cost effective technology, including flexible, scalable and cloud based solutions. 

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