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White Paper
COVID-19: Sustaining Business in All
Scenarios: A New Lens on Bank Credit Risk
Management
Matteo Coppola, Lorenzo Fantini, Filippo Fioravanti
March 2020

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hile the COVID-19 outbreak appears to be plateauing in Greater China, it has
reached an inflection point elsewhere, characterized by the emergence of
multiple epicentres. More than 140 countries have been affected, compared
with around 20 in mid-February, and infection rates in many are rising fast.
As COVID-19’s spread has accelerated, markets have started to price in epidemic-related
risks. Equity markets have posted some of the biggest daily declines since the financial
crisis. Valuations of less risky assets, meanwhile, have reached record levels, amid
significant uncertainty.
For the time being, COVID-19 seems set to continue proliferating. One relatively positive
aspect of the virus is that fatality rates are significantly lower than that in previous
epidemics — SARS in 2003-04 or the Spanish flu in 1918-19. Still, contagion levels are
higher. Moreover, based on the experience of previous outbreaks (e.g. MERS in 2014, 2015,
and 2016), the virus is likely to strike in waves, suggesting that containment measures will
be only partially effective until the release of a vaccine. In this context, the risk of recession
is real. Indeed, many central banks have already taken action to offset an economic
slowdown.
Based on historical evidence, a V-shaped scenario – in which a GDP hit is followed by a
rebound, with no long-term loss of output – is most likely. The majority of past epidemics
(SARS in 2002, Asian flu in 1968, Hong-Kong flu in 1958, and Spanish flu in 1918) saw a
temporary shock to the economy followed by a rebound. However, more pessimistic
scenarios remain possible – especially in today’s interconnected world – and even a
relatively speedy rebound may have long-lasting impacts on consumer behaviours, value
chains, politics, and organizations.
On a sectoral basis, we expect widespread and heterogeneous impacts. There is a growing
crisis in travel and tourism, and in many other industries. At the other end of the scale,
there is a chance of a modest boost for pharma. Banks will likely see margin and volume
compression (due to lower interest rates and dampening of client activity/investment) and
disruption to physical operations. A particular threat to banks, however, emanates from
credit risk. Deteriorations in the credit worthiness of counterparties have the potential to
spur increases in rating downgrades, rises in default rates, and consequent pressure on
profitability and regulatory capital.
W

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Risk drivers specifically related to the COVID19 outbreak are not currently directly
captured by credit ratings systems. It is therefore critical for banks to ensure they
understand their positions and prepare to take mitigating action.
A good starting point is to assess primary vulnerabilities, leveraging a comprehensive set
of indicators and triggers. The potential impacts of the virus should then be quantified
against a range of scenarios, leading to impact assessments and identification of mitigation
strategies. (See Exhibit 1).
Exhibit 1: Banks should follow a scenario-based approach in four key steps
Step 1: Vulnerability Analysis
The first step aims at identifying the key vulnerabilities associated with the organization’s
credit portfolio; namely clients that are suffering or are more likely to suffer in the short
term.
The vulnerabilities should be assessed by ranking each client along two dimensions:
1) Cash level/internal vulnerabilities
2) Supply chain-level/external vulnerabilities

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Cash level/internal vulnerabilities
Companies can be affected in multiple ways, including reductions in business volumes,
short-term liquidity shortages, and operational disruption. From the bank’s credit risk
perspective, it is important to assess the client’s business liquidity and debt sustainability,
which can be measured using a number of industrial KPIs (e.g. EBITDA margin, free cash
flow), and financial KPIs (e.g. DSCR1
, short term debt/EBITDA, net financial position).
Banks can also leverage behavioural indicators (e.g. percentage of drawn credit lines). For
each of the KPIs, credit analysts should define thresholds separating healthy from higher-
risk companies.
A more advanced approach is based on transaction data. Banks can define client cash
positions by analysing inflows and outflows from bank accounts. This rich data source can
highlight recent trends (e.g. sudden cash reductions) and potentially inform real-time
estimates of liquidity risk.2
Supply chain-level/external vulnerability
Supply chain risk may arise from supplier business interruption or operational issues.
Vulnerable industries include the automotive and electronic sectors, both of which rely on
complex cross-border supply chains.
Analysis of supply chain risk is non-trivial and requires:
 The engagement of business experts to comprehensively capture the multiple
impacts that the virus could have on each industry’s supply chain.
 An assessment of supply chain risk at sub-industry level, because companies in the
same industry can have different supply footprints. The strength of the supply chain
can be estimated through analysis of payments outflows, mapping of dispersion
levels (e.g. number of suppliers per sub-industry), understanding of footprint (e.g.
1
Debt Service Coverage Ratio
2
Important to take into account the bank’s own share of transactions for a given client. This can be done
by comparing observed cash inflows and outflows with revenues and costs on client’s balance sheet.

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country of origin for payments), and riskiness of large suppliers, where
identification is possible.
The assessment should be cross-functional – involving credit, risk, and business experts
and a combination of quantitative and qualitative considerations.
Step 2. Scenario Design
Banks should start with macroeconomic scenarios available from public health and macro
institutions. Macroeconomic shocks to the real economy can take several forms – V-shaped,
U-shaped or L-shaped. The first step, therefore, should be to determine the underlying
characteristics of each scenario across dimensions including:
 Duration
 Severity of impact on the economy (e.g. GDP hit, unemployment increase)
 Severity of impact on financial markets (e.g. equity market collapse, credit spread
widening)
 Recovery speed and shape
Each scenario should translate into industrial KPIs (e.g. percentage drop in revenues,
EBITDA margin or cash flows) to be applied at both micro sector and client levels, as key
inputs for the impact assessment step. This aspect is critical: ultimately the bank is
interested in specific impacts on its portfolio rather than simple macro projections.
It is also important to assign a qualitative likelihood to each scenario (e.g. high, medium,
or low) and, more importantly, a set of KPIs – and thresholds - that can highlight potential
changes in the likelihood of each scenario. These KPIs can act as “triggers” (e.g evolution
of swap curves, downward revision of macroeconomic forecasts, equity market
performance, government bond spreads) and should play a critical role in the COVID 19
credit risk framework. They will provide an early warning signal of which scenarios are
most likely and will guide remediation strategies (see below). They should be closely and
regularly monitored and reported to senior management, the board, and risk committees,
which should ensure adequate oversight of the process.

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Step 3. Impact Assessment
The above vulnerability analysis should result in a list of vulnerable clients. For each
scenario, possible client evolutions can be defined as:
 No change in credit riskiness (or marginal change)
 Migration to IFRS9 Stage 2
 Migration to default
 Migration from UTP to bad loans3
Banks are already well equipped to estimate changes in probability of default (PD), stage
2 migrations and danger rates due to changing macroeconomic scenarios (thanks to their
stress testing and IFRS9 models). However, these models are based on historical
relationships between structural macro variables (e.g. unemployment rate) and changes in
PDs, and are therefore inadequate to capture short-term shocks such as the outbreak of a
virus.
For that reason, we believe that an expert-led approach is more appropriate, at least as a
preliminary step to structure model revisions. To estimate the potential impact of the
scenarios on highly-vulnerable clients, we recommend a sensitivity /stress testing analysis
approach based on the following two steps:
1) Define client-level indices of business profitability (e.g. EBITDA percentage), debt
sustainability (e.g. debt-service coverage ratio), short-term indebtedness and
liquidity (e.g. short-term debt/EBITDA, net financial position) that are more aligned
with potential short-term vulnerabilities.
2) Define a set of thresholds for these KPIs, leveraging internal experience, to trigger
actions client’s migration to Stage 2, migration to default, and migration from UTP
to bad loans.
Since in the scenario analysis, the bank has defined impacts on key financials (e.g.
revenues, EBITDA) at industry and client level, then for each scenario it can simulate
impact on the KPIs above and measure client migrations – thus estimating the
corresponding impact on provisions, expected loss, risk-weighted assets and CET1 ratio.
The simulations can be run at industry or client level, leveraging appropriate sampling and
extrapolations where necessary.
3
UTP is an acronym for Unlikely to Pay

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Step 4. Trigger-based Mitigating Actions
As in a traditional risk appetite framework, contingency actions should be activated when
there is a breach of early warning levels for identified triggers. Contingency strategies
should be focused on highly-vulnerable clients and segments, and should be more or less
aggressive depending on likelihood and severity of the scenario. They might include:
 Provision of new credit lines to clients likely to face short-term cash shortages,
where the business is sustainable. Measures should be accompanied by close
monitoring of the client position.
 Offers of debt restructuring (e.g. deferment of instalments, conversion of short-term
into long term debt, interest-only payments). The best restructuring option should
be informed by the results of the previous steps.
 Deployment of deleveraging strategies for clients/sectors that are likely to face a
more prolonged and structural crisis and cannot easily adapt supply chains and/or
distribution to a COVID19 environment.
In addition, banks should consider sharing the results of the exercise with their clients. This
can support discussions and improve the client experience.
The more rigorous the approach to scenario planning, the more resilient the organization
will be. Senior management should plan ahead, so that the most effective remediation
steps can be put in place quickly once a scenario (or variant) materializes.
Importantly, we expect a large set of fiscal and public policy measures to help companies
in distress. These will require a more proactive approach to identify COVID19 impacts on
the client base, since they will potentially “hide” client-specific impacts in the short term
that might lead to unpleasant surprises later on.
Finally, banks must be bold; in light of the fast spread of COVID-19, speed in evaluating
credit risk is of the essence.
Matteo Coppola
Lorenzo Fantini
Filippo Fioravanti

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Matteo Coppola is a senior partner and managing director in the Milan office of Boston
Consulting Group. Lorenzo Fantini is a partner and managing director in the firm’s Milan
office. Filippo Fioravanti is a principal in the firm’s Milan office.
You may contact the authors by e-mail at:
coppola.matteo@bcg.com
fantini.lorenzo@bcg.com
fioravanti.filippo@bcg.com
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