This article was first published in our free e-book: Buy Side Risktech 2.0: Solving a Risk Manager’s Challenges. Access the full eBook here.
For risk teams to continue to withstand market and regulatory pressures while delivering value to the front office, they need to become more sophisticated from a technology and data perspective.
In the past, risk systems acted almost like risk managers. The managers themselves spent time supporting their systems. This model has been turned on its head because the modern risk manager needs a system to support them, not the other way around.
Fig. 1: A modern approach to risk technology
Many risk systems rely on clients to upload portfolio and benchmark data. When uploading benchmarks in the complicated format with more than twenty thousand positions, files can become unwieldy and no longer queryable in the event of problems. In addition, it relies on customers to provide extensive reference data to ensure their positions are modeled correctly. Companies also find that they upload a single security multiple times because they are in more than one portfolio or benchmark.
Additionally, some risk systems do not have specific upload templates for more complicated instrument types such as structured products, contingent convertibles, term loans and securitized products. As these types of products become more popular with wealth managers, it is imperative that their risk is properly modeled.
Connect the tools for success
One way to increase the performance of your risk team is to take advantage of the latest desktop technologies. For example, Python is an open-source programming language that enables rapid application development. Python is free, well-documented, and popular in finance.
Many risk managers use tools like Python to do their own calculations, but are unable to use their risk systems’ engines or data in their desktop calculations.
Why? Because in order to be able to perform the risk calculations, they need to be able to create positions in specific formats and obtain reference data to upload to their systems. Establishing these connections often requires valuable development resources to manage, maintain, monitor, and update, and cannot be done on the fly without IT help. Even with the necessary support, performing such calculations is so time-consuming that it is impractical to perform them on demand.
The benefits of direct access to risk engines
Why not provide a user with a direct connection to the risk engine and its data to perform ad hoc calculations?
By using flexible APIs (Application Programming Interface) rather than relying on integrations with vendor input formats, a risk manager can quickly integrate their portfolio into the risk system. APIs also allow them to drill into the data and send requests to the risk system directly from programming languages like Python or even Microsoft Excel. This insight has wider benefits for model validation and data quality assurance.
This direct access to the risk engine frees risk managers from vendor-set batch calculation times, allowing them to spend more time on centralized risk management and ad hoc analysis. Flexible and accessible APIs allow users to access results directly, avoiding large and complex downloads. Risk managers can use these APIs to directly access their results and the underlying data and engines.
Buy-side risk has evolved – but risk systems are still catching up
It is exciting to launch new products that allow end customers to take advantage of new investment opportunities. And being first to market has huge reputational benefits for a company. However, as investment strategies, products and regulatory requirements are constantly changing, new product launches require the ability to calculate the risk of different components of financial instruments that are the constituents of that product. A new product requires an entire team, from front to back office, to be ready to support offerings, with risk teams often taking center stage.
Risk management techniques and attitudes are also evolving and becoming more integrated into the investment process. Most risk teams are striving to move away from static T-1 risk reporting to meet regulatory requirements and become real-time, market-driven risk responders. It could be argued that risk figures are less meaningful from a portfolio management perspective at the time they are reported, as the market has already moved on. This is all the more important in volatile market conditions where market risk can vary greatly from one day to the next. Risk teams need to be able to keep pace with technology and performance to continue delivering enterprise-wide value.
Inflexibility undermines competitive advantage
Risk systems without real-time calculation – or at least with current market data – become an insurmountable obstacle for risk managers to establish themselves in the investment process.
Inflexible solutions that hamper the implementation of new technologies and portfolios can hurt a company’s competitive advantage. Risk managers must wait for new enhancements to become available and then wait for internal technology teams to deploy them. This can delay product offerings as well as the implementation of innovative risk measurement techniques and customized customer requirements.
A changing risk landscape needs a modern tech stack
In order for the portfolio risk management function to adapt and grow to meet the needs of institutions, they must embrace change and look for next-generation technology stacks.
Long delays in system updates are increasingly unacceptable. Tech stacks should be flexible enough to support new products, calculations and reports without large development costs. This can be achieved with a modern solution using agile methods with continuous improvement.
Instead of infrequent and resource-intensive software updates, a modern approach uses development methodologies that continually introduce improvements in a hosted cloud environment. This agile approach enables new tools, reports and methods to be made available to all customers as soon as they become available.
Using JSON, a standard data format, allows for easy mapping to specific instrument types. In fact, by using flexible input formats, risk managers can allow portfolio input files to be included with only one ISIN and one denomination.
New interfaces such as JSON or a Python API reduce the need to adapt to the specific requirements of a risk system and allow for easier portfolio updates, more frequent risk calculations and more detailed risk analysis.
As buy-side organizations consider their investments in future risk technologies, there are clear trends that should be considered.
IHS Markit provides industry-leading data, software and technology platforms and managed services to solve some of the toughest challenges in the financial markets. We help our clients better understand complicated markets, reduce risk, work more efficiently and comply with financial regulations.
This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, a separately managed division of S&P Global.