This is our story of co-creating a solution with a leading U.S bank
As we know…
The Dodd-Frank Wall Street Reform and
Consumer Protection Act requires the U.S.
Federal Reserve (FED) to conduct annual stress
tests of large banks. Comprehensive Capital
Analysis and Review (CCAR) and Dodd-Frank
Act Stress Test (DFAST) are outcomes of the
Dodd-Frank Act.
Failure on the part of banks to comply with
CCAR and DFAST leads to heavy fines. It also
has an adverse impact on the brand image.
The FED established the model validation
council in 2012. The council is tasked with the
role of rigorously assessing the models used in
stress tests by banks. These stringent norms
and timelines put pressure on banks and their
model risk management function.
The challenge for the bank was…
It had over 200 diverse models spread across
varied product categories. To comply with CCAR
and DFAST requirements, the bank had to
conduct stress tests leveraging its existing
model validation framework and subject all its
models to periodic reviews and validation.
Faced with a shortage of talent, assessing and
validating the accuracy of existing models at
regular intervals was a daunting task. The bank
partnered with WNS to augment its model risk
management function by leveraging a team of
statistical experts and industry best practices.
Here’s what we co-created as a solution…
WNS leveraged its analytics expertise to build
a sophisticated framework to validate the
accuracy of models that were similar to the
client’s existing models. The framework
entailed:
Multiple validation and scoring methods were
deployed to improve the bank’s modeling
standards. Detailed validation reports were
prepared for each model to highlight the
shortcomings.
WNS performed stress testing on the models
using macroeconomic variables and assessed
the impact of stress scenarios on the bank’s
capital and profitability. The stress scenarios
were in accordance with CCAR and DFAST
norms, and the client’s risk management
policies.
In collaboration with the bank, WNS also built
models for Loss Given Default (LGD) and these
were used as references for the client’s existing
LGD model.
Our learnings and outcomes from the process of co-creation are...
That the models helped improve
decision-making and risk management. The
bank was able to discard and replace models
that were not performing well. The WNS
validation reports enabled the bank to
recalibrate its statistical approaches and
enhance the model validation standards.
The enhanced standards have enabled the
bank to comply with CCAR and DFAST
submissions since 2015 with ease. WNS has also
been working extensively to improve the bank’s
model governance through bespoke projects
including:
-
Grading the models based on various micro
and macroeconomic conditions
-
Checking the relevance of the variables used
in the models
-
Overall model inventory management
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