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In order for Risk teams to continue to meet market and
regulatory pressures while also adding value to the front office,
they need to become more sophisticated from a technology and data
standpoint.
In the past, risk systems almost acted like risk managers. The
managers themselves spent time supporting their systems. This model
has been upended, with the modern risk manager requiring a system
that supports them—rather than the other way around.
Fig 1: A modern approach to risk technology
Off-the-shelf limitations
Many risk systems rely on clients to upload portfolio and
benchmark data. When uploading complicated-format benchmarks with
more than twenty thousand positions, files can become cumbersome
and impossible to query when issues arise. Furthermore, there is
reliance on clients to provide extensive reference data to ensure
their positions are modelled correctly. Firms also find themselves
uploading a single security multiple times, due to holdings in more
than one portfolio or benchmark.
Additionally, some risk systems don't have specific upload
templates for more complicated instrument types, such as structured
products, contingent convertibles, term loans, and securitized
products. As these product types become ever more popular with
asset managers, it is imperative that their risk is properly
modelled.
Connecting the tools for success
One way to supercharge your risk team's output is to take
advantage of the latest desktop technologies. For example, Python
is an open source programming language that makes for rapid
application development. Python is free, well-documented and has
become popular in finance.
Many risk managers use tools such as Python to run their own
computations, but are unable to leverage their risk systems'
engines or data in their desktop calculations.
Why? Because, in order to run the risk computations, they need to
be able to create positions in specific formats and obtain
reference data to upload them into their systems. Building these
connections often requires valuable development resources to
manage, maintain, monitor, and update and cannot be run on-the-fly
without IT help. Even with the required support, running such
computations is so time-consuming it becomes impractical to do
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 run ad-hoc computations?
By using flexible APIs (Application Programming Interface)
instead of relying on integrations with vendor input formats, a
risk manager can get their portfolio into the risk system quickly.
APIs also allow them to dig into the data and submit queries into
the risk system directly from programming languages such as Python
or even Microsoft Excel. This insight has broader benefits for
model validation and data quality assurance.
This direct access to the risk engine liberates risk managers
from vendor-set batch calculation times, allowing them to spend
more time on core risk management and ad-hoc analysis. Flexible and
accessible APIs enable users to access results directly, avoiding
large and complex downloads. Risk managers can use these APIs to
directly access their results as well as the underlying data and
engines.
Buy-side risk has evolved—but risk systems are still
catching up
It is exciting to launch new products, allowing end clients to
take advantage of new investment opportunities. And being the first
to market has great reputational benefits to a firm. Yet, as
investment strategies, products, and regulatory requirements
continuously change, new product launches require the ability to
calculate risk on various 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 at the center.
Risk management techniques and attitudes are also evolving and
becoming more integrated into the investment process. Most risk
teams are looking to move away from static T-1 risk reports in
order to meet regulatory requirements to become real-time,
market-driven risk responders. It could be argued that by the time
risk numbers are reported, they are less meaningful from a
portfolio management perspective as the market has already moved
on. This is even more important in volatile market conditions, in
which market risk can be very different from one day to the next.
Risk teams need to be able to keep up from technological and output
perspectives to continue adding firm-wide value.
Inflexibility erodes competitive edge
Risk systems lacking real-time calculation - or at least using
the latest market data - become an immovable obstacle for risk
managers in establishing themselves within the investment
process.
Inflexible solutions hindering the implementation of new
techniques and portfolios can impair a business's competitive edge.
Risk managers have to wait for new enhancements to be made
available and then wait for internal tech teams to deploy them.
This can delay product offerings as well as the implementation of
innovative risk-measurement techniques and bespoke client
requirements.
A changing risk landscape needs a modern tech stack
For the portfolio risk management function to adapt and grow to
meet the demands of institutions, they must embrace change and look
to next-generation technology stacks.
Long delays in system updates are increasingly unacceptable.
Tech stacks should be flexible enough to support new products,
computations, and reports without large development costs. This can
be achieved with a modern solution, following agile methods with
continuous improvement.
Instead of infrequent and resource-heavy software updates, a
modern approach uses development methodologies that continuously
roll out improvements to a hosted cloud environment. This agile
approach enables new instruments, reports, and methods to be
deployed to all clients as they become available.
The use of JSON, a standard data format, allows for
straightforward mapping to specific instrument types. In fact, by
using flexible input formats, risk managers can allow for portfolio
input files to be ingested using as little as an ISIN and a
notional.
New interfaces such a JSON or a Python API reduce the need to
conform to a risk system's specific requirements, allowing easier
portfolio updates, more frequent risk calculations, and more
detailed risk analysis.
As buy-side organizations consider their investments in future
risk technology, there are clear trends that should be
considered.
Posted 24 May 2022 by Luke Armstrong, Buy Side Risk Product Manager, S&P Global Market Intelligence
IHS Markit provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.
This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.
