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5.3

MODEL RISK
MANAGEMENT: MODEL RISK MANAGEMENT TECHNOLOGY SOLUTIONS
EFFECTIVELY ENHANCE THE KEY STAKEHOLDER’S ABILITY TO
TECH IDENTIFY RISK IN A TIMELY MANNER AND CONTINUOUSLY

INTERVENTIONS MEASURE ITS IMPACT.

IN THE

AFTERMATH OF

COVID-19 PART II

Many models and approaches are widely used in financial which required a systematic framework such as Model Risk 3. Learning from the insurance industry
institutions for pricing, valuation, analytics, and risk Management (MRM) solution. Principles of MRM, aided with Largely, in cases of extreme events such as epidemics, the
management of banking book and trading books, which technology led interventions, can be used to effectively insurance industry, through their Actuarial models – more
include less quantitative models like scoring models, rating contain such model risks. We will look at how this can be specifically through a family of models called as
May 20 2020 models, default prediction models to highly quantitative achieved through a few use cases: ‘Epidemiology Models’ or ‘Compartmental Models’ – tries
models like stochastic volatility models, jump diffusion to predict the financial implication of said events. A graded
Jaya Vaidhyanathan models and so on. In recent times it has been a standard 1. Stress testing & what-if scenarios impact analysis (such as Grade I/II/III severity) is performed
CEO, BCT Digital practice that the materiality of risk drivers and valuation As we discussed, quantitative models used for routine on the cash flows. The standard practice in actuarial science
adjustments aligned to an institution’s risk governance decision-making do not consider the impact of extreme is to add economic considerations (such as a GDP drop) to
framework to deliver desired or optimal results. However, events. Such models have to be complete with testing for models and design insurance policies based on changes in
We previously discussed Early Warning Systems in the tail events and increase in application of new generation stress/what-if scenarios for strategic decision-making cash flows, in case of an unfortunate event.
context of black swan events, like the COVID-19 pandemic. approaches such as Machine Learning algorithms often purposes, such as fixing tolerance limits, defining materiality
With their near real-time capabilities, these systems are brings in new uncertainty and less visibility to human of risk drivers and thresholds etc. These help in ensuring The banking industry can take a cue from this and can
reliable allies in countering the negative fallout on financial oversight, miscalculation or indiscretion, and often find that decisions made on the basis of the models stay within perform graded impact analyses of the cash flows based on
institutions and their credit risk profiles. Incorporating Model themselves at the centre of heated debates and limits, even under extreme circumstances. their portfolios, rather than merely relying on single-point
Risk Management best practices into the Risk management controversies. default models. Recently certain epidemiological models
of an FI is another effective method to manage and mitigate Technology can be used to simulate various scenarios and have been used in applications for Enterprise Risk
model risk and calibration risk. As valuation adjustment and For example, in most of the cases, black swan events such can be used for measuring their impact at various levels. Management (ERM). Organisations can incorporate such
managing the materiality of the risk drivers take precedence as COVID-19 are not factored in the standard models, as 2. Model risk management framework cross-discipline learning as part of the overall Model Risk
under the stress caused by unpredictable, potentially these are tail events, less frequent and very few in number, A comprehensive MRM Framework would help in Management framework to augment the quantitative
catastrophic events, the systematic approach and, from a historical perspective. In other words, standard identifying and containing various risks arising out of models. models.
technology-aided framework should enable the user and the models are based on the principles of central limit theorem, A typical framework would include qualitative and
FIs to effectively manage the risk over this time and balanced with a trade-off between flexibility and bias, by quantitative standards, documentation of assumption behind Model Risk Management technology solutions effectively
ignoring the extreme events. Any attempt to fit the model to enhance the key stakeholder’s ability to identify risk in a
After the tumultuous impact of COVID-19 on the global extreme events within a limited sample size would result in the models, regular audits, validation, issue tracking, timely manner, continuously measure its impact, and
developing challenger models, and so on. Models also have manage model risks. In a time that’s laden with uncertainty,
financial sector, countries are bracing up with exit plans that large residual errors, which cannot be generalized or
will not only help restore financial stability, but also allow practically used day-to-day. to be continuously calibrated and refined by it is in the best interests of banks and financial institutions to
them to be prepared for future black swan occurrences. adding/dropping variables based on parameters such as deploy frameworks that underpin model functionality in
Whether these collective efforts will buckle or thrive under The application of new generation models could be new information criteria. unprecedented times, empowering them to withstand
pressure always remains to be seen. But, in the interim, solution to these tail events and we are seeing increase in A comprehensive technology-led platform would certainly shocks, and dismissing possibilities of untimely breakdown.
there are several technology-led interventions that financial research activities in this space; however these are still go a long way in enabling an organisation in rolling out a
institutions can adopt to identify and mitigate model risk and unproven territory or not established practises that required strong Model Management Framework.
calibration risk in the face of uncertainty. close oversight at user level and at the governance level,

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