The brokerage business has undergone significant change since the New York Stock Exchange traded its first stock for Bank of New York in 1817. Today, trading is far more complex, quantitatively-driven and powered by technology. It’s also been significantly impacted by national regulatory regimes in Europe and North America as countries tightened their rules and supervision after the financial crisis. The effect was that brokerage firms would have to report additional transaction details that require the collection, profiling and management of large amounts of data across asset classes and financial products. Firms were no longer just traders, they would become big data managers.
Stay current on your favourite topics
Every firm has its own path to deciding how to optimize its data which until recently was used primarily for reporting and regulatory purposes. The more forward looking, and insightful firms are using this trove of data to better price trades, evaluate market manipulation and dig more deeply into the rich mine they hold within their trade data. Firms are using their data and creating a tiered approach to mining this data by creating data lakes. These environments allow data to remain in many formats – raw, refined, or curated form – for use by diverse groups. Forward-thinking securities firms see value in analyzing trade data for customer and pricing insights that could influence future product development and sales strategies. Whether it’s by data lakes or other tools, they’re investing in systems and analytics that will also help then grow the business and potentially offset compliance costs.
Is There Gold in Those Trades?
Reporting to regulators on every aspect of its business from AML, to proprietary trading and pricing has been all-consuming for many large financial institutions. To comply with new requirements, they’ve had to add new departments, technology and processes to describe reporting — wielding great power to the technology firms who implement those systems. Firms have used many outside providers in the data arena in order to report out their regulatory data. The firms that collect, house and report that data have a tremendous amount of valuable insights about the trader, counterparty and all of the dimensions of those trades. Some firms are looking to keep this data in-house in order to decompose it for use in surveillance for anti-money laundering, know your customer and to obtain pricing volatility to correlate with the market movement to determine any major market shifts due to collusion. The model for self-sustaining internal data warehouses are costly and unsustainable.
Some firms are finding ways to leverage their investment in data management to propel growth as next generation data architecture makes it possible to increase their return on investment. McKinsey proposed that managing data will be “a foundation for value in capital markets,” providing better data and application architecture to boost financial performance. These new systems will continue to deliver operational efficiencies, but they’ll also allow firms to better understand how to deliver and serve customers using improved analytics.
What Does the Next Generation of Data Gathering Look Like?
There are several new regulations where firm can quickly benefit from new systems and analytics. The Consolidated Audit Trail, which requires firms to report trade level data to the Securities and Exchange Commission (SEC) from inception through settlement, is an area where firms are amassing a large amount of customer data. CAT will be the largest detailed trade repository ever created; it’s database will hold valuable information on the timing of trades, pricing, volatility, customer preference and a range of other metrics that potentially benefit the firm beyond their reporting requirements. From this data, securities firms can develop and tailor products to their customer needs. Firms were collecting and profiling some of the data for FINRA requirements however; the scope of the requirements for CAT is much comprehensive and unprecedented in the securities industry.
Consumer companies have led the way in customer experience marketing. They’ve successfully harnessed data using location-based marketing, SEO and other tools to track consumer behavior during the buyers’ journey. Markets are being reshaped by technology and the ability of platforms and tools that accurately align products to a customer’s buying needs. Large investment banks, hedge funds, fintech companies and others in financial services are poised to follow a similar path. With vast quantities of data and improved systems and analytics, they, too, will soon offer financial instruments, pricing, liquidity and inventory as defined by customer preference. The change is already taking place in the wealth management arena where firms have created robo-advisers that allow customers to tailor their personal financial journey and with the information that the customers provide, the “adviser” adapts its offering of products and services based on the algorithms as well as the past purchase decisions.
Stay current on your favourite topics
Those that invest quickly to capitalize on new data management systems and analytics tools, will have a competitive advantage to grow and scale new products and markets. These could take the form of new collateral management platforms that allow internal and external views on the inventory. For sales and trading, dashboards can be created based on buying decisions and pricing to better target products for corporate and large institutional customers. The benefits can also be seen in improved processes like customer on-boarding which can be an arduous process, if the data architecture and customer structure is created correctly, a seamless on-boarding process can be launched which would benefit corporate customers and could improve their willingness to engage in more business with that firm. When it comes to the trading optimization itself, predictive trading signals would most effectively leverage the in-house data that was collated. Data quality also has a positive impact on risk weighted assets (RWA) and greater insight would allow the big banks to behave in a less risky manner because they have a more accurate view on their risk profile.
BigTech and Data Interpretation
A recent article in The Financial Times, reported that several large European Banks complained to regulators that the BigTech firms who provide services to open banking systems, were not subject to the same regulations as they were. It is an ironic statement. Many of these banks colluded on pricing, sent funds to foreign countries on global restricted lists and have acted in less than transparent ways to their counterparties, customers and the individual investor. Perhaps their real concern is that BigTech companies could capture and analyze data on inventory/product availability, product mix, settlement ease and other areas to benefit the customer. The reality is that banks have that same ability. The question is how will they take advantage of it?
As securities firms evaluate their investment in new systems and tools, they should consider:
- Committing to a deeper understanding of customer data and testing it with new products and customer segments
- Developing a proof of concept for trade analytics defined by product and goal
- Decomposing the data to be used in surveillance and pricing analyses
 Mckinsey December 2017
 Financial Times October 2017