The capital markets industry has invested billions of dollars to automate back-office processes from trade confirmation all the way through clearing and settlement. The automation business case has typically been based on headcount savings and operational efficiencies, such as scaling operations for higher volumes, effective exception management, and operational risk visibility. More recently though, it has become clear that post-trade automation benefits go far beyond operations and are now being realized by the front office.
Related: Transforming the Data Ecosystem
The original business case for back-office automation did not factor in the rich historical data that digitization creates by capturing the history of post-trade events, collectively providing empirical evidence on the natural forces of supply and demand across all instruments that have been cleared, settled, or reported. Back-office data is now being leveraged by front- and middle-office functions to better understand kinetic and often fragmented markets because the back office is a natural point of data aggregation and standardization.
In the front office, the market structure for each asset class often has a unique set of market participants, intermediaries and various competing auction and trading mechanisms, all striving for best execution. In the US equities market, there are more than a dozen lit markets and upwards of 50 trading venues. However, while trading venues are disparate and could be considered fragmented, the US equities post-trade landscape is centralised, enabling these transactions to be funneled into one central clearinghouse and settlement facility. This results in a single electronic record of all these transactions, regardless of where or how the instrument was originally traded.
Back-office data can be applied across several areas to provide historical and real-time insights into liquidity, valuation risk, momentum, sentiment, correlation, and contagion. Significantly, the provision of back-office data to the front office is particularly important as there is currently a significant industry focus on the need for empirical evidence to support risk, valuations and financial benchmarking. In fragmented and thinly traded asset classes, industry participants have typically relied on skill and judgement with economic models. However, it’s now clear that these models, valuation, and risk need to be supported by more robust empirical data using real observations and actual trade activity.
This need for empirical, transaction-based data has led DTCC, through our daily processing of billions of transactions across equities and fixed income, to deliver enriched back-office data sets to the market.
Simultaneously, international financial reporting standards, independent price verification and market risk capital reforms such as the Fundamental Review of the Trading Book (FRTB) under the Basel III framework are also driving the need for empirical data. All these initiatives demand integrity, supported by independent market evidence of valuation liquidity in every process, as the foundation for risk and capital allocation.
The primary role of the back office has been to process transactions efficiently and provide scalability to the front office, and it still is. However, the industry has accepted new sources of market insight, and now, every processor of transactions flow is looking to provide its clients with new insights that help them more efficiently use services and understand market dynamics.
The automation of post-trade processes has widened this role by providing new insights and propelling the back office into becoming a vital data provider that informs front- and middle-office decision making. This is a trend that will surely continue to grow as post-trade processes become further automated and firms seek new sources of data to remain competitive, grow their business, and manage the complexity of market dynamics.
This article was originally published to A-Team Insight on November 24, 2022.