Financial services lessons learned in 2020: Part 2
The continued data services journey
Though there were many challenges in 2020, financial institutions also made a lot of progress in developing their approaches to data management and data services. Three major financial services firms shared their experiences over the last 12 months during InterSystems virtual annual summit. Speakers from Credit Suisse, Broadridge Financial Solutions and Banco do Brasil Tecnologia e Serviços (BBTS) explained how their firms have built out their enterprise data foundations to deliver new capabilities and services.
Broadridge has adopted a data-as-a-service strategy that it has applied to all of its existing capabilities and services to provide its clients with unique analytical insights. The focus of the firm’s efforts has been on improving data quality and accessibility via a multi-tier approach that encompasses data cleansing and data governance aspects on top of its enterprise data fabric. The firm has worked with InterSystems to ensure that data can remain in situ and data lineage can be tracked for every item via a consistent metadata layer that sits on top of its various data stores. The firm has a consistent data integration layer that normalizes the underlying data, machine learning capabilities to enable dynamic queries and data analytics, and application programming interface (API) management capabilities to deploy data and services for a variety of purposes.
Wall Street giant Credit Suisse has also built out its own smart enterprise data fabric with a view to transforming the business capabilities of its front office. The firm has been able to move from static algorithms to those that are distributed, adaptive and better able to cope with market volatility, such as that experienced throughout 2020. It has used normalized data sets and machine learning models to analyze the performance of its algorithms and therefore increase its ability to capitalize on alpha signals in the market.
The bank has also put its data to work within the prime brokerage space to enable it to optimize the use of its inventory. Prime brokers have long faced a rising cost of capital and it is increasingly important for firms to eke out cost efficiencies where possible. With this in mind, Credit Suisse has used machine learning techniques to analyze opportunities related to capital efficiency.
Brazil’s BBTS has built out its own data capabilities in response to the introduction of open banking in the local market, which is being driven by the central bank and is rolling out in phases over the next 12 months. The central bank is keen to foster more competition in the local market and encourage the inclusion of the more than 40 million people who are currently without a bank account in the country. The Banco do Brasil subsidiary is also responding to the new domestic data protection regulation that is due to come into force in May 2021 that will introduce sanctions and financial penalties for non-compliance.
The focus on data integration and permissioned accessibility within these two Brazilian market developments has compelled BBTS to develop a hub for integration with the more than 700 fintechs in the local market. Rather than developing brittle point to point connections with each of these market players, the firm is instead providing API access to allow providers and financial market participants to act as consumers or suppliers of data.
BBTS has also focused on investing in areas of data analytics including the development of a data fabric across the bank’s 90,000 branch surveillance cameras and its 500,000 different alarms and sensors to enable the monitoring and surveillance of suspicious events and activities. The firm is developing machine learning models to forecast new points of security risk across all of its locations in Brazil. The data from all the different devices is being aggregated and analyzed to increase the physical security of the bank’s premises.
The smarter use of data has been an industry theme throughout 2020 and will continue to dominate industry minds for some time to come. Financial services firms have plenty of opportunities to benefit from robust data infrastructure investments that will enable them to harvest and harmonize data from across the organization, and leverage analytics, machine learning, and APIs to put actionable information to use for the business and clients.
About the Author
Virginie O’Shea is a capital markets fintech research specialist, with two decades of experience in tracking financial technology developments in the sector, with a particular focus on regulatory developments, data and standards. She is the founder of Firebrand Research, a new research and advisory firm focused on providing capital markets technology and operations insights for the digital age.