The capital markets industry is very complex, and the industry is going through very interesting transformation phases with exploding opportunities through FinTech inventions, blockchain, Bitcoin, advanced data capabilities, and innovation. Also, there are significant efforts in progress to invest in internal innovation teams, building R&D labs, and mobilizing teams to understand the feasibility of industry trends.
A quick background: The capital market engages in trading financial securities that involve three layers of organizational functions, including front office, middle office and back office. They support trading multiple asset classes with home grown smart trading and efficient processing applications, vendor products, and building proprietary technologies, custom databases, and even custom computer languages.
On the other side, there are significant data challenges for the capital markets in raised financial regulatory scrutiny, strict mandated financial stress testing for capital sustainability, sensitive financial and regulatory reporting, and data retention requirements, and there are occurrences where regulators and audit teams may require full access to historical data etc. The industry is also facing enormous data challenges from increased data redundancy, making critical data available, too many data reconciliations processes between data sources, and making necessary adjustments where needed without reflecting at the source. In addition, there are heavy data sourcing costs for every time there new regulations are introduced, and fragmented trade and client views. In a such a dynamic, and complex business environment, a clear, pragmatic, and result driven data strategy can make a great difference, and could position the industry to stay much more competitive and profitable.
Let’s take a closer look at a data strategy that could improve the capital markets financial risk management function. Risk management is one of the most data driven function with wide range of computations, and delivers comprehensive risk information with forecast, evaluation of financial risks to senior management, board members, regulators, and traders to understand the potential financial risks that may breakdown the economical stability of the institution. It also provides transparency element to regulators.
In general, the risk management function is challenged to deliver information in almost real-time due to a variety of data challenges, legacy applications, computing power and data storage. Traditionally, data has always been sourced and positioned to support specific risk processing model, and there are no centralized, consolidated data sets available for all risk models to operate. In addition, there is very complex review process involved to finalize results and prepare dashboards for management, and regulatory reports for submissions. It is vital for risk management function to operate on near real-time with establishment of financial risk command centers to intercept critical risks or/and monitor the existing financial risk thresholds. The concept is similar to infrastructure command centers that monitors infrastructure, servers, networks, databases and communicate implications to entire organization in case of unexpected breakdowns.
The foundation steps towards near real-time risk management capabilities to build a centralized data infrastructure to capture every business event that occurs across asset classes, and the risk models should be designed to perform just-in-time computation. There are number of architecture patterns evolved with commodity big data platforms supporting unlimited storage with stream computing to support near real-time information/decisions. The next step is to have strategy focused on pillars of data integration, data governance, and data architecture to establish enterprise wide single view of trades, and establishment of business domain driven golden data sources with high data quality.
it is very important for senior management and board members to understand the financial risk, and its implication to make timely decisions. It would be highly productive for them to have near real-time access to risk information through self-service mode on their smart phones or tablets instead of waiting for dashboards or reports to come through in a traditional way.