With the deadline for the Securities Finance Transaction Regulation (SFTR) advancing rapidly (end of 2018), firms need to ensure they have a sustainable and low cost plan for how they will tackle the requirements laid out by the regulation. Beyond, simply pulling data from disparate sources together into a single report, astute organisations will be those that take the opportunity to use insight driven data management to achieve actionable intelligence during and after the implementation. Looking beyond the aggregation of data required, regulatory agility should allow both lenders and borrowers to enjoy a low technical and operational debt environment after the go live.

Shining a light on poor data and process practices

Recently, the main focus has been on other regulations, such as MIFID II and EMIR, and despite the fragmented trade scenarios and lifecycle events Securities Finance has been less of a priority for regulators and for the firms themselves. Consequently there has been significant underinvestment in the area of post trade Securities Finance compared to other product lines.

Cost pressures have produced excellent automation in places (e.g. Triparty, CCP’s, Messaging, Third Party Reconciliation), however, regulatory reporting is now bringing poor data quality and practices firmly back onto the management agenda. Realistically, some required new fields under SFTR could have been added years ago, such as the Legal Entity Identifier (LEI), but historically there has typically been poor data governance and no legal requirement to do so. A second effect of the cost reduction mandate is that many technology enhancements struggle to compete with the low cost of manual post trade management, despite offering improved transparency, speed and control. Finally, the basic reconciliation of trade facts is under cost pressure. Firms should keep a very close eye on post trade reconciliation KPI’s now and use an external solution for all products that require reporting.

What could be the best approach to achieve SFTR compliance?

The likely best approach is to exercise regulatory agility, covering both data quality and data convergence, expose potential problems early on in the project and keeping overall costs to a minimum. This approach can be combined with an efficient software solution to provide an optimal method of ingesting all the required data fields, publishing progress and ultimately track SFTR compliance in a truly transparent way.

This approach could enable a more balanced learning curve throughout the SFTR compliance programme. This is key, as some detailed expert knowledge will be challenge to resource in time to meet the deadline.

In the early stages, creating an ethos of ‘regulatory agility’ could be vital as the solution evolves; creating the initial solution quickly, by starting early and starting live. Securing support for an ‘early start’ is pivotal, with rapid iterative improvements during the development phase, thereby increasing consistency and confidence in internal and external data during the project. The initial development solution should be ‘live’, providing constant updates and enabling the data to be reconciled on a daily basis. Clear driving principles of data ownership is important, in order to address data quality, timeliness, and accuracy.

Knowledge management capability for competitive advantage

The regulator wishes to ensure the transparency of all aspects of securities financing, checking the data quality for millions of transactions being undertaken every day in order to monitor the overall risk in the market. This is an attempt to monitor and thereby prevent financial institutions from undertaking risky transactions that could endanger the market. The Financial Stability Board policy framework provides many clues as to how the regulator intends to analyse the submitted data. This will be achieved by using macro prudential tools and monitoring key metrics using overarching dashboards, in order to highlight areas of concern.

Given that firms will need to submit their part of these metrics to the regulator with a common data format, there is no reason why they should not query their own regulatory reporting suite within the firm. This will very likely reveal new insights into the management and efficiency of the securities financing function and present valuable trends at a glance.

Collaboration based on a single source of the truth

The minimum requirement of the regulation is to achieve SFTR compliance. Once firms have built an efficient new solution, they could just stop there and report to the regulator on an ongoing basis. However, the more visionary firms will rapidly realise that they can build additional value, by utilising the new data sets required by SFTR that have previously not been available to them. By taking this approach and maximising the re-use of data in a smart way, astute firms may be able to recoup some of the costs they have been forced to spend to achieve compliance.

Valuable new data combinations can be derived from the new fields that have been added such as LEIs, UTIs, collateral, settlement, agreement, instrument, business type etc. The combination and re-use of these data points will enable reporting dashboards to be created highlighting valuable correlation and concentration risks that have not been available before. New insights could then be identified across the firm to inform risk intelligence.

New data combinations have the potential to enable visualisation of netting arrangements and provide more transparent views of liquid asset holdings. The statistics could also give firms an idea of their total agreement version exposure or drive out CCP attribution by SFT type, from an initial and variation margin perspective.

Prime brokers, meanwhile, may also be able to visualise conflicts of interest when it comes to short selling and cover. Daily collateral composition updates by LEI, haircut and price control, or trend analysis for collateral / funding types are other potential benefits.

Visualisation of data via new dashboards will allow the complete picture of SFT collateral to be seen. Firms will be able to monitor and visualise the complete client relationship, adding value and creating multiple pivots to ensure better decision making in future.

Conclusion

As regulatory reporting evolves there are a number of opportunities for financial institutions to address data quality and efficiency:

• Consolidate their data management approach
• Review Big Data analytics and federated data lineage capabilities
• Practice regulatory agility
• Use business intelligence technology

Combining these opportunities will lead to faster risk, collateral or legal insights that will improve controls, thereby reducing regulatory reporting transaction costs.

For SFTR compliance, clearly data capture is key. Successful firms will be those which achieve zero business transition impacts, then they’ll be able to move beyond simple data reporting and utilise or even enrich the new data environment.