CompatibL won an award for introducing the industry's first machine learning-based credit risk models in 2021. These models use market generators trained on unsupervised machine learning to extrapolate risk measures from limited pandemic-era data for long time horizons. This solves the problem of outdated pre-pandemic models no longer accurately reflecting current risk levels. CompatibL's founder believes machine learning will become the standard across the front, middle, and back office by the end of the decade due to advantages like combining disparate data sources while preserving individual differences.

2. Best new technology introduced over
the last 12 months (trading and risk)
CompatibL
“Machine learning is a transformative new
technology that will touch every aspect of the front,
middle, and back office. I am confident that before
the end of this decade, it will become the market
standard in financial product valuation and risk management.
By delivering the industry’s first machine learning-based,
market generator driven credit risk models to production in
2021, CompatibL won its place at the forefront of this historic
change while solving a specific industry need of measuring
the pandemic level of risk without relying on outdated pre-
pandemic historical data.”
Alexander Sokol, executive chairman and head of quant
research, CompatibL
THE SOLUTION
• CompatibL’s market generators are able
to generate the data for time horizons
between one and 30 years that risk models
require, making accurate measurement
of credit risk, limits, insurance reserves
(economic scenario generation) and macro
strategy performance possible during the
pandemic.
• CompatibL’s market generator uses
unsupervised machine learning to
aggregate statistics from multiple curren-
cies and credit names to overcome the
short length of pandemic-era time series,
while rigorously preserving the individual
differences between the names.
WHAT’S TO COME
• CompatibL will look to enhance its offering to help firms manage risk during
periods of dramatic change.
• The vendor will look to further expand its risk coverage in light of what was
learned during the pandemic.
WHY THEY WON
The impact of the Covid-19 pandemic is ongoing in financial markets
and CompatibL has answered the call for accurate rick measurement
during uncertain times using artificial intelligence (AI). Specifically,
machine learning-powered capabilities will continue to solve evolving use
cases and CompatibL has demonstrated that.
OVERVIEW
• Pre-Covid-19 historical data no longer
accurately represents current risk levels,
and the ability to measure risk from shorter
time series is invaluable during the ongoing
pandemic.
• Market standard models for credit risk,
limits, insurance reserves, and macro
investing measure risk years ahead. They
require a long time series that extends to
pre-pandemic data.
December 2021 waterstechnology.com

3. Winners’ Circle
Princeton, NJ-based CompatibL won the best new technology introduced over the last 12 months (trading and risk)
category in the 2021 American Financial Technology Awards. Victor Anderson chats to Alexander Sokol about his firm’s
recent work around leveraging machine learning technology to help clients extrapolate more pertinent and accurate risk
measures, especially in the wake of the Covid pandemic where historical data and models did not exist.
CompatibL’s new approach to an old problem
Q CompatibL won this award on the back of
its machine learning-based approach to market
generators, introduced to help capital markets
firms produce more accurate risk measures in
the wake of the Covid pandemic. Can you tell me
a bit about the new approach and how it came
about?
Alexander Sokol, founder and executive chair-
man, CompatibL: Risk models are based on historical
data and risk horizons are the times in the future when
risk is measured and managed. The length of historical
time-series [data] necessary to measure risk has to be
longer than the horizon for which you are measuring.
For example, you cannot use one day of data to predict the next
30 years into the future. The data also has to be relevant to today’s
risk—the last few years of the pandemic era are very different to
anything that came before it. For many risk and investment man-
agement applications—including credit risk, limit risk, insurance
reserves and macro investing—the investment horizon is measured
in years and decades. The problem is that we are limited to the past
few years when it comes to generating accurate pandemic-era data.
The technology underpinning the CompatibL Risk Platform is
based on my research, the research of CompatibL’s quant research
team and that of Alexei Kondratyev while he was at Imperial
College London, on applying machine learning to generate accurate
risk measures from limited data for long time horizons.
Q Specifically what problems does the new approach
solve for its clients and how does it go about solving those
problems?
Sokol: The way machine learning technology addresses this
problem is that it is able to combine data from, for example,
different currencies (in the context of interest rates) and differ-
ent credit names (in the context of credit spreads) in a way that
preserves the individuality of the currency or credit name. In
traditional risk management practices, either you use data from the
individual name or currency or you combine all the data, which
then becomes the average for that name or currency. The problem
is that no one wants risk management for average currencies or
names—they want individual risk. Machine learning makes it pos-
sible to combine data for disparate names (and currencies), while
preserving the individuality of each name. It is able to do this
because it does not interpolate or aggregate the data—it “reasons”
with the data.
Q To what extent do you believe machine
learning technology will be used to calculate
risk measures across the industry going
forward? What are the advantages of using
machine learning models over traditional
models to calculate risk measures?
Sokol: We absolutely believe that machine learning
is a transformative technology that will revolutionize
not only risk but all front-office functions as well. I’m
confident that this transition will happen by the end of
this decade, where machine learning won’t be just a tool,
but the preferred way of addressing front-, middle- and
back-office problems.
Machine learning replaces unsophisticated algorithms that aggregate
and interpolate data and produce risk averages, with reason. For
example, when it identifies two similar currencies, it combines their
data, but it does so in a rigorous, logical way. This unique combina-
tion of rigor and perceptiveness is a huge advantage [compared with
traditional models].
Q To what extent does the technology genuinely learn in
terms of becoming faster, more accurate and more experi-
enced when dealing with problems it has seen before?
Sokol: The great thing about machine learning is that the more data it
has,the more accurate it becomes.It doesn’t necessarily become faster,
but we have the cloud [for performance],and machine learning technol-
ogy is always cloud-based.What’s more important than speed is that it
[machine learning] can be as precise as the amount of data it has,and it
doesn’t over interpret or simplify the data.Unlike with other techniques
where you select in advance how precise you want to be,machine learn-
ing continually evolves where it can provide results from minimal data,
which then become more accurate when more data is provided.
Q What can we expect from CompatibL and the
CompatibL Risk Platform going forward? What’s your
focus for next year?
Sokol: Machine learning is rapidly becoming the standard in trading
and risk applications and we’re happy that our work over the past year
toward this goal has been recognized. Our market generator was the
first vendor-produced limit management model in the industry and
we’re currently working on a number of other initiatives that we’re
excited about. We will be unveiling new machine learning software
models during 2022 and we believe that this year will be amazing for
machine learning.
Alexander Sokol
CompatibL
waterstechnology.com December 2021