WEBINAR: COVID-19 AND IMPACT ON THE US ......WEBINAR: COVID-19 AND IMPACT ON THE US FINANCIAL SYSTEM Epidemiology modeling and consumer confidence June 24 th, 2020 CONFIDENTIALITY

Please note that this session was held at a particular point in time (Wednesday June 24, 2020, 4pm-5pm EDT), and in light of the rapidly evolving Covid-19 situation, it is possible these discussions are no longer accurate after that date.

WEBINAR: COVID-19 AND IMPACT ON THE US FINANCIAL SYSTEM Epidemiology modeling and consumer confidence

June 24th, 2020

CONFIDENTIALITYOur clients’ industries are extremely competitive, and the maintenance of confidentiality with respect to our clients’ plans and data is critical. Oliver Wyman rigorously applies internal confidentiality practices to protect the confidentiality of all client information.

Similarly, our industry is very competitive. We view our approaches and insights as proprietary and therefore look to our clients to protect our interests in our proposals, presentations, methodologies, and analytical techniques. Under no circumstances should this material be shared with any third party without the prior written consent of Oliver Wyman.

© Oliver Wyman

3© Oliver Wyman

WEBINAR AGENDA

1 Epidemiological update

2 Macroeconomic outlook

3 Shopping Outlook survey update

4 Pandemic Navigator update and use cases

5 Q&A

4© Oliver Wyman

OUR PANELISTS

Til SchuermannPartner & Co-Head – Risk & Public PolicyNew York

Helen LeisPartner, Health & Life SciencesNew York

Ugur KoyluogluPartner and Vice Chairman, Financial Services AmericasNew York

Beth CostaPartner, Americas Payments Platform LeadPittsburgh

1EPIDEMIOLOGICAL UPDATEPanelist: Helen Leis

6© Oliver Wyman

0

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Cumulative Confirmed Cases of COVID-19

New Cases Per Day of COVID-19Past 3 Mo, 7 Day Moving Average

Updates toMeasurement

Definitions1

COVID-19 TRENDS AND SPREAD OF THE DISEASECumulative confirmed cases continue to rise across the world, but the epicenter is beginning to shift away from Europe and towards South Asia, the Middle East, and South America

Information as of 6/22/20

0

200,000

400,000

600,000

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Active cases per day of COVID-19

Updates toMeasurement

Definitions1

China US Rest of WorldEurope2 South America Asia/Middle East Africa

Source: John Hopkins University & Medicine Coronavirus Resource Centre1. Until February 17, the WHO situation reports included only laboratory confirmed cases causing a spike in total cases. Some sources include this update as of February 13. The jump due to inclusion of non lab confirmed cases is not included in the new cases data in WHO situation reports.; 2. Includes countries categorized under “European region” based off of latest WHO Situation Reports

Epicenter shifts to South America and Asia/Middle East

United States begins to rise again

As of June 22nd, 2020• >9 MM cases

reported in 200 countries and territories

• ~471 K reported deaths

7© Oliver Wyman

Epidemiologic perspectives

• Infection rates in children: A new study suggests that children are half as likely to become infected with COVID-19 as adults over the age of 20. Studies such as this one are critical inputs into the debate on the value of school closures as a suppression mechanism and the overall level of risk associated with school re-opening

• Hospitalization rates in the US: Newly released data from the CDC cites a 14% hospitalization rate for those infected in the US, which is lower than previously cited 19% from the China CDC. While encouraging, the data are not perfectly comparable, as the CDC did not have hospitalization data on the entire population; 14% should be treated as an estimate until more data is available

Suppression and road to reopening

• US testing: FDA has issued EUAs to multiple new testing methodologies in the past month. While manufacturers have announced plans to increase production via various partnerships, only PCR is currently at scale

• UK testing: UK is beginning a pilot for 14,000 healthcare workers and their households to do weekly home-based testing using saliva kits; if effective, this pilot could paint the future of testing

• Contact tracing: Contact tracing is running into roadblocks in the US driven by inconsistent public compliance and lack of uptake of technology (e.g., only 3 states have thus far agreed to use the Apple-Google technology)

Re-opening approach

• Europe: European countries are continuing their path to recovery with steady declines in case growth and reopening of several borders this week to European travelers; reopening to global travel is under discussion and likely to prohibit travelers from current hotspots (e.g., Brazil, Russia)

• New global outbreaks: China, S. Korea, Israel and even New Zealand (all countries that had claimed victory) have seen new clusters of outbreaks driving swift action (e.g., mass testing and lockdowns in Beijing, ban on gatherings in public spaces in Seoul, lockdowns in parts of Israel)

• US growth: Significant growth in cases and hospitalizations across states in the South and West of the US is threatening additional disruption; no state-issued reclosures have been announced yet, but some individual businesses in hard hit areas (e.g., AZ) are reclosing

• Tulsa Rally: President Trump’s rally held in Tulsa, OK on 6/20 may pose a risk for new case growth with several members of the advance team testing positive (six before the rally and two so far after)

Vaccines and therapeutics

• Chloroquine: After much hype and public debate, the FDA has retracted its EUA for Chloroquine and Hydroxychloroquine use in the treatment of COVID-19

• Dexamethosone: NHS approved the use of Dexamethosone (generic steroid) in the UK based on early data that demonstrates reduction of mortality in the most severe patients

RECENT DEVELOPMENTS

8© Oliver Wyman

DESPITE STRONG RECOVERY IN THE NORTHEAST, STATES IN THE SOUTH AND WEST ARE AT HIGH RISK OF FURTHER DISRUPTION

• Northeast: Limited Risk– Generally hit hard by initial outbreak– Cautiously reopening after substantial case decline

• Midwest: Low-Moderate Risk– Mostly declining or stabilizing post-initial reopening – Some states (MO, KA) are seeing new case growth

• Rural States: Moderate Risk– Mountain and midwestern states that have very low

population density – Many are starting to see increased case growth, but

absolute case counts are low

• South/West: Moderate - High Risk– Driving majority of growth in US– Southern states among the first to reopen– Hot weather likely driving public indoors, facilitating

transmission0% or less

(50% – 100%) 100%+

(0 – 25%) (25% – 50%) % Change in New Daily Cases (2 Weeks):

Circle Size: # of Active Cases

LEGEND:Fully reopened1 more than 4 weeks

Fully reopened1 less than 4 weeks

Partially reopened

Washington

Oregon

California Nevada Utah

Arizona New Mexico

Colorado

Wyoming

Montana

Idaho

Minnesota

Iowa

Missouri

Arkansas

North Dakota

South Dakota

Nebraska

Kansas

OklahomaTexas

Louisiana

MississippiAlabama

GeorgiaSouth Carolina

Florida

North Carolina

Wisconsin

Illinois

Michigan

Tennessee

Kentucky

Indiana

OhioPennsylvania

New York

MaineVermont

New Hampshire

Massachusetts

Rhode IslandConnecticut

New JerseyDelawareMaryland

Washington, D.C.Virginia

West Virginia

Alaska

Hawaii

1. “Fully reopened” defined as when a majority of high risk businesses, including bars, movie theaters, or gyms, have been reopened with indoor service. This chart does not account for regulatory restrictions that may or may not be in place in those businesses, including mask wearing or capacity constraints. 2. Data for MS as of 6/22/20 due to reporting challenges on 6/21/20

Information as of 6/22/20

9© Oliver Wyman

AS THE SITUATION EVOLVES, RISK OF DISRUPTION IS MORE HEAVILY CORRELATED WITH POPULATION DENSITY, LEVEL OF LEARNED BEHAVIOR AND TEMPERATURE

Northeastern (e.g., NY, MA, ME, NH) and Mid-Atlantic states (e.g., ME, NH, VT, VA) – LOW• Experienced significant case and death counts in their own territory or nearby• Reopening slowly, most are still only partially reopened• Faring well with declining case growth and low positive test rates (≤6%)• Demonstrating relatively low correlation between mobility and transmission suggesting learned behavior• Likely benefitting from cooler temperatures allowing heavy mix of outdoor activities• Major cities remain at heightened risk due to population density and use of mass transit

Midwestern states (e.g., MN, IA, NE, OH, IN) – LOW-Moderate• All fully reopened, more than half for multiple weeks with sustained declining or flat case growth• Cases recently ticking up in several states, especially in the South of the Midwest (e.g., MO, KA) where temperature is likely driving the population inside;

testing remains below 7%

Largely rural states (e.g., WY, ID, SD, ND, AK) – Moderate• All fully reopened and fared well for multiple weeks• Have recently seen an uptick in cases, though still with low absolute case numbers and ≤5% positive testing• Benefitting from natural social distancing and outdoor culture• Growth is representative of overall growing rural trend and is a risk given lower access to care and possibility of seeding cases back into urban environments

South and West states (e.g., FL, TX, AL, CA, OR, WA) – Moderate-High• Mixed level of reopening ranging from earliest openers to more cautious ones• Seeing sustained increasing case counts for several weeks along with increase in % positive tests• Mobility tightly correlated with transmission for most, suggesting low levels of learned behavior (outliers include CO and NM)

2MACROECONOMIC OUTLOOKPanelist: Til Schuermann

11© Oliver Wyman

Worst global economy since the Great Depression

2020 global GDP forecasts• -4.9% (IMF)• -6%/-7.6% (OECD)

“No country has been spared”

Gita Gopinath – head of the IMF

Lost government revenue: $10TN

Global public debt will hit 100% of GDP• Surpass levels in WW2• U.S. deficit: 24% of GDP

2020: A YEAR FOR THE HISTORY BOOKS

12© Oliver Wyman

FEDERAL RESERVE TOTAL ASSETS HAVE INCREASED SHARPLY TO $7.1TN

Federal Reserve Balance Sheet2003 – 17 June 2020

• 68% increase in total assets since March 4th 2020

• Other central banks’ balance sheets have increased 30+%– Bank of England: £791B

(~$986B)1

– ECB: €5.6T (~$6.3T)2

– Bank of Japan: ¥639T (~$5.9T)3

Last updated: 6/22/2020

1. Bank of England Weekly Report and Balance Sheet2. European Central Bank Weekly Financial Statements3. Balance Sheets of the Bank of Japan

13© Oliver Wyman

LATEST GDP FORECASTS INDICATE A SEVERE SHOCK IN THE U.S. ECONOMYThe escalation of the COVID-19 crisis has lead to significant downward revisions in GDP forecasts globally

U.S. Real GDP Growth Forecasts – Q1, Q2, Q3, Q4, and annualAnnualized growth rate, by select economic analysts (9)1,2

Key observations from estimates

• Forecast updates have been frequent and sizable– Actual Q1 has been revised downwards (-4.8%

to -5%)– Q2 forecasts are now being revised upwards;

median at beginning of June was -38%

• Forecasted Q2 qoq annualized growth rate in the US (~30–40% drop) will be the worst since we have quarterly data available

• Key indicators to track include:– Trend for percent of U.S. population infected

(scenarios ranging up to 80%)3

– Reliance on “smart” mitigation strategies (e.g., mass testing, use of analytics)

1. Sources: Bank of America (June 19), Moody’s (May 15), UBS (June 10), Goldman Sachs (June 18), TD (June 17), JP Morgan (June 12), CBO (May 19), Deutsche Bank (June 16), Morgan Stanley (June 15), FRBNY Nowcast (May 1, May 29, June 19 Nowcast not included in table calculations), Q1 estimates based on latest forecast before release of Q1 GDP Actual2. Quarterly estimates in terms of qoq% seasonally adjusted annual rate (saar)3. Imperial College COVID-19 response team


Q1 2020 Q2 2020 Q3 2020 Q4 2020 2020 (annual)Median -3.9% -33.8% 17.5% 7.9% -5.7%Average -5.2% -35.0% 17.8% 8.2% -5.8%Max/Min -2.3%/-9.9% -31.0%/-40.0% 29.0%/7.0% 12.0%/4.5% -4.2%/-8.1%Actuals -5.0%

Institutional Forecasts Actual FRBNY Nowcast

Q3 2020 Q4 2020

-30

Q2 2020Q1 2020

10

-20

20

2020 (annual)

-10

30

0

-40

GoldmanJP Morgan

Goldman

Goldman

DB

CBO

B of A Moody’s

Morgan StanleyGoldman

DB

Morgan Stanley

TD

Moody’s

CBO

JP Morgan

JP Morgan

Annu

aliz

ed g

row

th ra

te (%

)

JP Morgan

B of A

Morgan Stanley

FRBNY Nowcast (May 1)

DBGoldman

UBSDB

DB

Moody’s

CBO

Morgan Stanley

UBS

Moody’sGDP Q1 Actual

JP Morgan CBO

FRBNY Nowcast (May 29)

TD

UBSMoody’sB of A

UBSMorgan Stanley

CBO TD

TD

B of A

FRBNY Nowcast (June 19)

14© Oliver Wyman

U.S. Unemployment Forecasts – Q1, Q2, Q3, and Q4Quarterly unemployment rate, by select economic analysts (5)1

Key insights

• Most annual unemployment forecasts assume a steady economic recovery starting in June, and appear not account for the possibility of subsequent significant waves of infection

• 46 million unemployment claims filed since start of the COVID-19 lockdown, wiping out the last eleven years of job gains2, 3

• Actual unemployment estimates will likely be quite noisy for a while

• Congressional Budget Office forecasts a slower employment recovery than most major banks

THE DOWNWARD SHOCK TO GDP IS MIRRORED IN UNEMPLOYMENTThe escalation of the Covid-19 crisis has lead to significant bearish revisions unemployment forecasts globally

1. Sources: Moody’s (May 15), Goldman Sachs (May 12), JP Morgan (June 12), CBO (May 19), Deutsche Bank (June 16), TD (June 17)2. Sources: U.S. Bureau of Labour Statistics3. Tracking unemployment forecasts against unemployment reports may be misleading – unemployment reports only record jobless workers actively searching for employment


Peak unemployment during financial crisis2

Q3 2020 Q4 2020

Institutional forecasts Actuals

Q1 202025

Q2 2020

20

10

5

15

0

Goldman

JP Morgan

TD

CBO

TD

JP MorganDeutsche

April Actual

JP Morgan

CBO

JP Morgan

TDDeutsche

CBO

May Actual

Goldman

Moody’s

Moody’s

Deutsche

Deutsche

CBO

Moody’s

Moody’s

Goldman

March Actual

Une

mpl

oym

ent r

ate

(%)

Q1 2020 Q2 2020 Q3 2020 Q4 2020Median 3.8% 13.5% 11.5% 10.9%Average 3.8% 15.6% 12.9% 10.2%Min/Max 3.8%/3.8% 13.0%/25.0% 9.2%/18.5% 7.9%/12.0%

Actuals2 4.4% (Mar) 14.7% (Apr) 13.3% (May)

15© Oliver Wyman

Time

GDP

V-shaped recovery

Economy recovers relatively quickly

GDP

Time

Time

GDP

W-shaped recovery

Economy ‘re-opened’ too quickly

Increase in cases causes GDP to suffer

Swoosh-shaped recovery

Recovery slower than V-shape, but faster

than U-shape

Time

GDP

U-shaped recovery

Economy recovers slower than V-shape

Time

GDP

L-shaped recovery

Economy never fully recovers

THERE ARE SEVERAL POTENTIAL PATTERNS FOR ECONOMIC RECOVERY

16© Oliver Wyman

89

87

94

93

88

92

90

91

95

96

97

98

99

100

Q5 (2009 Q1)

(1Q 21)

Q2 (2008 Q2)

(2Q 20)

Q6 (2009 Q2)

(2Q 21)

Start(2007 Q4)

(4Q 19)

Q1(2008 Q1)

(1Q 20)

Q3 (2008 Q3)

(3Q 20)

Q4 (2008 Q4)

(4Q 20)

Q7 (2009 Q3)

(3Q 21)

Q8(2009 Q4)

(4Q 21)

CCAR 2020 severely adverse

Financial Crisis

2020 COVID Crisis consensus1

U.S. Real GDP relative to Q4 2019 (100) and compared to CCAR and Financial crisisEstimates as of May-201 US GDP Indexed to P0 (CCAR 2020)2 and 4Q07 (Financial Crisis)3

CCAR projected quarterFinancial Crisis quarter2020 COVID Crisis projection

1. Consensus as the average of Bank of America (June 5), Morgan Stanley (June 15), CBO (May 19), UBS (June 10), Goldman Sachs (June 18), JP Morgan (June 12), Deutsche Bank (June 9), Q1 estimates based on latest forecast before release of Q1 GDP Actual2. Source: “CCAR 2020 data release”, “CCAR 2019 data release” - Federal Reserve3. Source: Federal Reserve Economic Data

GDP PROJECTIONS ASSUME A RETURN TO PRE-COVID LEVELS BY EARLY 2022We continue observing downward adjustments: as of last week, the expectation was to recover by early 2022


17© Oliver Wyman

U.S. Unemployment Forecasts compared to CCAR 2020 and Financial CrisisQ1 2020 – Q4 2021

CCAR projected quarterFinancial Crisis quarter2020 COVID Crisis projection

= range of COVID Crisis forecast estimates

1. Consensus as the average of Moody’s (May 15), Goldman Sachs (May 12), JP Morgan (June 12), CBO (May 19), Deutsche Bank (June 16)2. Source: “CCAR 2020 data release” - Federal Reserve3. Source “Unemployment Rate” – Federal Reserve Bank of St Louis

12%

2%

6%

14%

8%

16%

4%

10%

18%

20%

22%

24%

26%

Q1 (2008 Q1)

(1Q 20)

Q7(2009 Q3)

(3Q 21)

Q3 (2008 Q3)

(3Q 20)

Financial Crisis3

Une

mpl

oym

ent r

ate

Q2 (2008 Q2)

(2Q 20)

Q4(2008 Q4)

(4Q 20)

Q5(2009 Q1)

(1Q 21)

Q6(2009 Q2)

(2Q 21)

Q8(2009 Q4)

(4Q 21)

CCAR 20202

2020 COVID Crisis consensus1

UNEMPLOYMENT PROJECTED TO RETURN TO PRE-COVID LEVELS BY EARLY 2022Last updated: 6/22/2020

18© Oliver Wyman

Consensus 2020 Real GDP Growth Forecasts, Nov 20191 until June 20202

% growth YoY, median

LATEST GDP ESTIMATES IN SELECT REGIONSThe escalation of COVID-19 crisis has lead to significant downward revisions in GDP forecasts globally

1 Source: OECD.2. Sources, date of latest update: Bank of America (June 5), Goldman Sachs (June 17), Morgan Stanley (June 15), Deutsche (June 16), JP Morgan (June 12), TD (June 17), IMF (June 24) GDP growth forecasts obtained as the median of estimates.3. Q1 GDP results in terms of qoq annualized rates

1.6% 1.0%2.0%

2.9%

-3.3%

-1.1%

-4.2%-4.0%-5.8%-5.5%

-8.7%

-3.9%

-8.0%-9.6%

1.0%

-4.5%

-8.3%

0.4%

-6.2%

5.7%

1.5% 1.3%

-4.9%

-8.4%-10.2% -10.2%

-7.8%

1.0%

2020 OECD est. (Nov 2019) 2020 forecasters est. (~early April 2020) 2020 forecasters est. (~early June 2020) 2020 IMF est. (June 2020)

2020 Q1 GDP3 -5.0% -8.2% -7.7% -14.2% -8.6% -34.7%

Global U.S. Canada U.K. Euro Germany China


19© Oliver Wyman

GDP FORECAST FROM OECD ACCOUNT FOR A POTENTIAL SECOND WAVE OF INFECTIONSGlobal outlook varies significantly when accounting for a potential second major outbreak


2020 Real GDP Growth Forecasts1

% growth YoY

4.5%5.2%

1.9%

4.1%

1.7%

5.0%

2.8%3.9%

6.5%

1.5%

9.0%

3.5%

5.8%6.8%

Single wave scenario forecast Two-wave scenario forecast

-2.6%

-7.6%-6.0% -7.3% -8.5% -9.4%

-11.5%-8.0%

-14.0%

-9.1%-11.5%

-6.6%-8.8%

-3.7%



Forecasting two scenarios – one in which a second wave of infections, with renewed lock-downs, hits before the end of 2020,

and one in which it is avoided

1. Source: OECD.

2021 Real GDP Growth Forecasts1

% growth YoY

3SHOPPING OUTLOOK SURVEY UPDATEPanelist: Beth Costa

21© Oliver Wyman

The Shopping Outlook Survey was first launched in the US, and is now expanding to Canada, Mexico and Brazil

Today’s focus

SHOPPING OUTLOOK SURVEYThe Shopping Outlook Survey measures consumer shopping, banking and bill payment attitudes and moods

Each week, we survey ~30 questions, a subset of which is used to derive the COVID-19 Shopping Confidence Index

The COVID-19 Shopping Confidence Index (SCI) … is an Oliver Wyman proprietary measure of consumer shopping propensity and financial confidence. The SCI detects weekly shifts in confidence impacting commerce as countries navigate COVID-19.

Shopping sentiment: Plans for routine as well as discretionary shopping

Personal financial outlook: Confidence around job and personal finances

COVID-19 effects: Pandemic factors impacting consumers

Demographics: Standard demographics such as age and income

Banking and debt views: Pandemic’s impact on banking, debt, paying bills

22© Oliver Wyman

CONSUMER SENTIMENT IMPROVED HALTINGLY IN JUNE …

The SCI slowly continues its upwards trajectoryThe SCI considers shopping plans, financial confidence, and willingness to return to public life; based on external benchmarks, the SCI would be ~700 in a normal environment

Confidence in job and personal finances is correlated and improving% of respondents confident in personal finances and job/level of income

About half are comfortable returning to public lifeDo you feel comfortable in restaurants or other public areas such as sporting venues?

Source: Oliver Wyman Shopping Outlook Survey, April 13 2020 – June 15 2020

241 251267 260

299315

295 304

343

312

5/185/114/274/13 4/20 5/04 6/015/25 6/08 6/15

62%

70%67%

75%

4/13 4/20 5/184/27 5/04

80%

5/11 5/25 6/01 6/08

65%

55%

6/1550%

60%

70%75%

Confident in job / level of incomeConfident in personal finances

21%

12%

16%

4/20

16%

11%

4/13

24%

18%26%

12%

4/27

16%

19%

5/04

23% 23%

17%

5/11 6/01

26%

5/18

15%

21%

16%

51%

5/25

30%

21%

6/08 6/15

39%

Yes, with a mask27% 30% 28%

Yes

41%37%

48% 47%

35%

23© Oliver Wyman

Flying intentions within the next 2 months has shown some improvementHow soon would you feel comfortable traveling by plane?

… WITH SHOPPING INDICATORS SHOWING SLOW GROWTH

Propensity for discretionary spending slightly higher than mid-AprilAre you planning shopping besides food, medicine, household necessities next week?

Despite looser restrictions, a surprising number are staying home anywayDo you currently have shelter in place restrictions?


Big ticket spending has risen slightly since mid-AprilAre you planning any large purchases (over $500) in the next month?

16% 18% 23% 18% 24% 25% 21% 22% 26% 22%

46% 47% 44% 50% 42% 45% 46% 47% 47% 46%

39% 35% 32% 32% 34% 30% 33% 32% 27% 32%

6/015/254/13 5/185/044/20 5/114/27 6/08 6/15

Yes

Maybe

No

9% 11% 14% 10% 14% 13% 12% 11% 15% 15%23% 25% 23% 23% 21% 26% 24% 25% 28% 25%

68% 63% 62% 68% 64% 61% 64% 64% 57% 61%

4/204/13 4/27 5/04 5/11 5/18 5/25 6/01 6/08 6/15

YesMaybe

No

16% 21% 15% 20% 21% 25% 19% 25% 28% 22%

27% 22% 27% 21% 20% 20%22% 19% 18%

16%

57% 57% 58% 59% 60% 55% 60% 56% 55% 62%

5/184/204/13 4/27 6/01 6/155/255/04 5/11 6/08

Within 2 months 3-6 months 6 months or longer (or never)

85%

45%

12%38%17%

5/184/274/13 4/20 5/115/04 5/25

50%

6/01 6/08 6/150%

100%

3%

Yes, we have restrictions

No, we do not have restrictions, but I am staying home anyway

No, we do not have restrictions and I am not sheltering-in-place

24© Oliver Wyman

CONSUMER PROFILES: AGE

Shopping profile The return to normal

Believe finances are better than before the pandemic Plan to make a non-essential purchase in the next week Willingness to travel on a plane in the next two months

Confident in job / level of income More shopping online in the past week Comfortable in public spaces without a mask

Plan to save more after the pandemic Increased spending in the past week Confident shopping at a physical store

Financial stability

Younger consumers (18-34 years) indicate having better financial stability…

…which is reflected in higher shopping propensity (correlated with age)

However, this does not translate into readiness to return to BAU

6/15

40%

0%

10%

20%

6/1

30%

4/13 4/20 4/27 5/4 5/11 5/18 5/25 6/8

0%

80%

60%

20%

40%

80%

0%

20%

60%

40%

100%

18-34 55 or older35-54

5/25

40%

30%

0%

10%

20%

50%

4/13 4/20 4/27 5/4 5/11 5/18 6/1 6/8 6/15

30%

0%

20%

10%

60%

50%

40%

50%

20%

40%

0%

10%

30%

0%

10%

20%

30%

5/25

40%

5/4

50%

6/15/114/204/13 4/27 6/85/18 6/15

0%

25%

5%

10%

15%

20%

20%

0%

40%

60%

80%

18-34 35-54 55 or older 18-34 35-54 55 or older


25© Oliver Wyman

CONSUMER PROFILES: INCOME





Financial stability

Income is not a significant driver of greater financial confidence…

…nor is it a strong predictor of shopping propensity … … or readiness to return to BAU

0%

5%

5/18

10%

4/20

15%

20%

25%

6/155/114/13 4/27 5/4 5/25 6/1 6/80%5%

10%15%20%

6/8

25%30%

4/274/13 6/155/184/20 5/4 5/11 5/25 6/1

60%

0%

50%

30%

10%

40%

20%

0%

20%

100%

40%

60%

80%

30%

0%

10%

20%

40%

10%

0%

30%

20%

40%

0%

10%

20%

30%

40%

6/154/13 5/115/4 5/184/20 4/27 5/25 6/1 6/8

30%

0%

5%

25%

10%

15%

20%

0%

80%

20%

40%

60%

Income $50-100KIncome <$50K Income > $100K Income <$50K Income $50-100K Income >$100K Income <$50K Income >$100KIncome $50-100K


26© Oliver Wyman

CONSUMER PROFILES: WORKING ARRANGEMENTS





Financial stability

Consumers show similar levels of financial stability regardless of working arrangements

Consumers working from home are (unsurprisingly) more likely to shop online

Consumers working at their workplace show greater readiness to return to BAU

5/25

40%

0%

10%

20%

30%

4/13 4/20 4/27 5/4 5/11 5/18 6/1 6/8 6/15 6/15/4

50%

4/13 4/20 4/27 6/155/180%

5/11 5/25 6/8

10%

20%

30%

40%

0%

50%

10%20%30%40%

60%

40%

0%

80%

20%

100%

60%

0%

50%

10%

20%

30%

40%

40%

0%10%20%

60%

30%

50%

0%

10%

6/15

20%

30%

5/4

40%

50%

5/114/27 5/184/13 4/20 5/25 6/1 6/8

0%

10%

20%

40%

30%

20%

0%

80%

40%

60%

Working from home Working from regular workplace Working from home Working from regular workplace Working from regular place of workWorking from home


27© Oliver Wyman

PERSPECTIVES ON BANKS DURING (AND AFTER) THE PANDEMIC

Who is happy with their bank?Most consumers have neutral or positive feelings towards their banks, and many satisfied consumers are younger (ages 18-34)

What do consumers want from their banks going forward?Many consumers are happy with their banks’ existing features and services, but some, especially urban consumers, are interested in more financial guidance

81%59%

81% 91%

18%15%

4%

7%

Ages 18-34 Ages 35-54

6%

1%Full sample

7%1%

7%7%

3%2%

2%2%

4%2%Ages 55+

My bank has helped me a little I haven’t needed help

My bank has helped me a lot There have been some issues

My bank has been a problem

21% 32% 16%

28%21%

30%

42% 35% 45%

Full sample

4%

5% 6%

8%

4%Affected by COVID

3%

Not affected by COVID

In-person By video Through an app OtherVia phone call

How do consumers prefer to engage with their banks post-pandemic?Nearly half of consumers are still interested in engaging with their banks in-person, but more may opt for digital interactions as COVID spreads

11% 11%

22%14%

55%

17% 18%26%

15%

48%

8% 7%

22%15%

57%

9% 10%18%

10%

59%

Help with managing investments and financial security

Help with managing money

More options for saving and investing

Flexible options for payments

and/or refinancing

I am happy with how things are

Full sample Urban Suburban Rural

Source: Oliver Wyman Shopping Outlook Survey, combined June 8 2020 – June 15 2020

Will consumers continue to use cash?Half of all consumers plan to use cash only occasionally after the pandemic

35% 34% 40% 32%

52% 46%47% 57%

13% 20% 13% 11%

Ages 35-54Full sample Ages 18-34 Ages 55+

Cash occasionallyCash all the time Pay electronically

28© Oliver Wyman

New debt resulting from the pandemic, by age37% of younger consumers (ages 18-34) have taken on debt as a result of the pandemic, in comparison to 19% of the general population

Confidence in ability to repay debt compared to before the pandemic, by ageAlthough younger consumers have taken on more debt, they are more confident in their ability to repay their debt in comparison to older consumers

New debt resulting from the pandemic, by employment statusMore consumers out of work have taken on debt due to the pandemic

DEBT DURING THE PANDEMIC

19%11%17%

14%

67%50%

63% 79%

10% 10% 12%

Ages 35-54

4% 4%

9%

3%

Full sample Ages 18-345%8%

3%7%

6%

Ages 55+

Much more confidentMuch less confident

Somewhat less confident

Just as confident

Somewhat more confident

1% 1%1% 2%2%

2%11%18%

81% 74% 65%

Full sample

6%8%

9%10%

4%

Employed

2%3%Laid off / furloughed

No new debt

Debt <$1,000 Debt $5,000-9,999

Debt $1,000-4,999 Debt $10,000-19,999

Debt >$20,000

1% 2% 1% 1%1%2%

13%

18%

79%91%81%

63%

3%8%

6% 2%

Full sample Ages 18-34

8%7%4%

Ages 35-54

5%2%Ages 55+

No new debt

Debt <$1,000

Debt $10,000-19,999Debt $1,000-4,999

Debt >$20,000Debt $5,000-9,999

12%11% 16%

15%

67% 60%49%

10%

15%

16%2%

Full sample

4%

9%

9%

Employed5%

Laid off / furloughed

Much more confidentMuch less confident

Somewhat less confident

Just as confident

Somewhat more confident

Confidence in ability to repay debt compared to before the pandemic, by employment statusThose who are employed show greater confidence in paying off debt


29© Oliver Wyman

CONSUMER BILL PAYMENT

Concerns about ability to pay bills, by residenceMore urban consumers are concerned about their ability to pay their bills compared to suburban and rural consumers

55%

71% 70% 66%

32%

24% 25%26%

13%6% 5% 8%

RuralUrban Suburban Full sample

Not at all concerned Very concernedSomewhat concerned

4%

63%

16%

7%

6%

Concerned about

paying bills

Rent / mortgage

InsuranceUtilities DebtInternet

4%

Medical expenses

Top priority bills for those concerned about paying billsPaying the rent / mortgage takes first priority for the majority of consumers


30© Oliver Wyman

www.oliverwyman.com

Questions? Please reach out to us at [email protected]

FOR ADDITIONAL SURVEY RESULTS

Results from the Shopping Outlook Survey will be shared regularly as we continue the study

#OWPayments

Listen to our June 3, 2020 webinar replay,COVID-19 and Impact on the US Financial System: Payments

http://www.oliverwyman.com/

mailto:[email protected]

https://twitter.com/OliverWyman

https://www.linkedin.com/company/oliver-wyman/

https://www.oliverwyman.com/our-expertise/insights/2020/jun/growing-consumer-confidence.html

https://www.oliverwyman.com/our-expertise/events/2020/jun/covid-19-impact-on-payments.html

4PANDEMIC NAVIGATOR UPDATE AND USE CASESPanelist: Ugur Koyluoglu

32© Oliver Wyman

CONTENTS

• Quick (re) introduction to Pandemic Navigator• Update on new capabilities since last webinar in late April

– Inference model– Death count model– Deeper modeling of heterogeneity and non-stationarity

• Select observations from recent forecasts• Use cases of scenarios and forecasts

33© Oliver Wyman

OUR PANDEMIC NAVIGATOR PROVIDES COVID-19 FORECASTS AND SCENARIOS, AND HELPS TRANSLATE THEM TO ECONOMIC AND BUSINESS IMPACTS

PANDEMICNAVIGATOR

Epidemiology and Govt health response

Macro overlay and recovery• Industry capacity and earnings by

sector• Fiscal/Monetary Stimulus

Outlook, earnings, liquidity• Demand/supply/operations• Cash flow• Loss-bearing capacity, funding• Risk/Loss Transmission

Lockdown Patterns • Timing and frequency• Severity (sophisticated vs blunt)

SELECT USE CASES

FORECASTS

Robust model for 90+ countries, 50 states, and 3000+ counties…

…with predictive forecasts and plausible scenarios…

…to support high-stakes business decisions to help you emerge stronger from the pandemic

Learn more on our website https://pandemicnavigator.oliverwyman.com/

1. Granular near-term forecasts 2. Long-term scenario design3. Macroeconomic impact4. Sector level impact and credit risk assessment5. Demand and supply chain management6. Re-baselining and planning / budgeting7. Return to work and managing over the long

haul of suppression

SCENARIOS

https://pandemicnavigator.oliverwyman.com/

34© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTS THE PANDEMIC NAVIGATOR PROVIDES FORECASTS FOR REPORTED CASES AS WELL AS ESTIMATES FOR UNDETECTED CASES

Los Angeles, CA

Explore this view at our website https://pandemicnavigator.oliverwyman.com/

https://pandemicnavigator.oliverwyman.com/forecast?mode=states&region=US_US-VT&panel=baseline


35© Oliver Wyman

Weekly percent change of new cases (7-day rolling average)As of June 14th, 2020

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSYOU CAN FIND A NUMBER OF HEAT MAPS AT COUNTY LEVEL AT OUR WEBSITE

36© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSWHAT-IF SCENARIOS LINKED TO MOBILITY AND TESTING

• Select what-if scenarios for speed of testing and changes in mobility

• Example shows – Increase in mobility following

the recent trend– Slow down of testing efforts

SPAIN

• Covid-19 case projections under selected mobility and testing scenarios– Confirmed cases– Active cases– New cases

Explore this view at our website https://pandemicnavigator.oliverwyman.com/


37© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSIN ADDITION TO CASE COUNTS, WE FORECAST DEATH COUNTS AND CONFIDENCE LEVELS AROUND OUR PROJECTIONS

New YorkCumulative deaths; forecasts from 5/24

CaliforniaCumulative deaths; forecasts from 5/24

MichiganCumulative deaths; forecasts from 5/24

IllinoisCumulative deaths; forecasts from 5/24

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

5/2 5/9 5/16 5/23 5/30 6/6 6/13 6/20

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

5/2 5/9 5/16 5/23 5/30 6/6 6/13 6/20

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

5/2 5/9 5/16 5/23 5/30 6/6 6/13 6/20

24,000

25,000

26,000

27,000

28,000

29,000

30,000

31,000

32,000

33,000

34,000

5/2 5/9 5/16 5/23 5/30 6/6 6/13 6/20

Actuals (in sample) Actuals (Out of Sample) Point estimates 5th percentile 95th percentile

38© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSYOU CAN FIND PANDEMIC NAVIGATOR FORECASTS FOR DEATH COUNTS FOR THE US AT THE REICH LAB COVID-19 FORECAST HUB AND CDC WEBSITE, ALONGSIDE THOSE OF UNIVERSITIES

Center for Disease Control and PreventionUnited States cumulative deaths; forecasts from 6/14

1. Institutions shown contributed to a model submitted for the week of June 14. Some teams are formed by multiple institutions, and some institutions contribute to multiple teams.

Modelling institutions1

UniversitiesColumbia Georgia Institute of Technology Harvard Imperial College London Iowa State Johns Hopkins London School of Hygiene & Tropical Medicine MIT Northeastern Notre Dame UCLA University of Arizona University of Illinois University of Geneva / Swiss Data Center University of Massachusetts University of Texas – Austin

Research organizationsBoston Medical Center Institute for Health Metrics and Evaluation Los Alamos National Labs Massachusetts General Hospital US Army Engineer R&D Center

Companies/otherAuquan Data Science COVID Act Now COVID-19 Simulator Consortium Oliver Wyman Predictive Science, Inc. Snyder Wilson Computing Youyang Gu

Oliver Wyman forecasts are published on the CDC and Reich Lab websites for the US and individual states. International forecasts are produced internally and can be made available

39© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSOUR HETEROGENOUS MODEL FOLLOWS A MATRIX FORMULATION AND CAPTURES NON-STATIONARITY DRIVEN BY LOCKDOWN AND CONTAINMENT MEASURES IN DISCRETE MATRICES

0. Starting matrix (baseline)Age group (5 year brackets, 0-80 yrs)

Age

grou

p (5

yea

r bra

cket

s, 0

-80

yrs)

Source: Social mixing pattern raw data for baseline matrix from Prem K, Cook AR, Jit M (2017). “Projecting social contact matrices in 152 countries using contact surveys and demographic data.” PLoS Comput Biol 13(9): e1005697. https://doi.org/ 10.1371/journal.pcbi.1005697

https://doi.org/%2010.1371/journal.pcbi.1005697

40© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSHOW HAVE ACTIVE CASE COUNTS CHANGED OVER TIME ACROSS VARIOUS COUNTRIES?

Italy

IranSpain Germany

Mainland China South Korea France

U.S. U.K.

Jan 22 Jun 14 Jul 5

292,879

21,127 5,474

4,108

35,961

4,449

189 625 5,144

Brazil Russia122,424 352,775

Jan 22 Jun 14 Jul 5 Jan 22 Jun 14 Jul 5

Jan 22 Jun 14 Jul 5 Jan 22 Jun 14 Jul 5 Jan 22 Jun 14 Jul 5


Sweden14,072


Second wave

Actual active case count Forecasted active case count

41© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSLIKE COUNTRIES, US STATES EXHIBIT DIFFERENT TRAJECTORIES AND CONTAINMENT PHILOSOPHIES

Illinois

PennsylvaniaMassachusetts Michigan

California Texas Florida

New York New Jersey

Jan 22 Jun 14 Jul 5

12,5546,436

12,283

3,3437,0745,135

40,349 23,871

19,405

Georgia North Carolina15,47910,618

Jan 22 Jun 14 Jul 5 Jan 22 Jun 14 Jul 5



Arizona15,770

Jan 22 Jun 14 Jul 5 Jan 22 Jun 14 Jul 5 Jan 22 Jun 14 Jul 5Jul 5

Actual active case count Forecasted active case count

42© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSUSING MAPE TO ASSESS MODEL ACCURACY OFFERS AN INTUITIVE WAY TO COMMUNICATE MODEL ACCURACY BASED ON OUT-OF-SAMPLE TESTS

Advantages of using MAPE

• Mean absolute percent error (MAPE) allows for an intuitive way of comparing model accuracy– I.e.: On average, the model is within X% of actual case counts number when

predicting Y days out

• Formula used is:

• Mean percent error (MPE) is also used to visualize over/underestimation of the model

Confirmed cases MAPE across select states – Two weeks outAs of May 28th, 2020

1.5%

5.8%

8.4%

3.5%

5.7%

0%

2%

4%

6%

8%

10%

NY MA RI MO TX

Out-of-sample MAPE calculation for confirmed cases (select states)As of May 28th, 2020 using 4 weeks of model results

Geography 1 day out 2 days out 3 days out 4 days out 5 days out 6 days out 1 week out 2 weeks out

New York 0.2% 0.3% 0.5% 0.6% 0.7% 0.8% 0.8% 1.5%

Massachusetts 0.5% 1.0% 1.5% 2.0% 2.4% 2.8% 3.2% 5.8%

Rhode Island 0.7% 1.4% 2.1% 2.7% 3.4% 3.9% 4.4% 8.4%

Missouri 0.5% 0.8% 1.2% 1.6% 1.9% 2.2% 2.6% 3.5%

Texas 0.6% 0.8% 1.1% 1.3% 1.4% 1.8% 2.1% 5.7%

43© Oliver Wyman

1. REVIEW THE EVOLUTION OF THE PANDEMIC FROM GRANULAR NEAR-TERM FORECASTSOUR PREDICTIVE FORECASTS HAVE BEEN HIGHLY ACCURATE THROUGH THE CRISIS: BACKTESTING RESULTS – NEW YORK ON APRIL 10, 2020

Forecasted trajectories for Confirmed Cases from yesterday (T0) and earlier projections from the previous 7 days

Forecasted trajectories for New Cases

Out-sample test results comparing Actuals 4/9/2020 with historically calibrated versions from the past

Forecast trajectories for Active Cases (confirmed-death-recovered)

Past predictions have been highly accurate

Forward projections of new cases fall exhibit high stability

Active cases are core driver of hospitalization rate and ICU needs

Ultimate confirmed cases fall within a stable range of estimates.

44© Oliver Wyman

Forecasted trajectories for Confirmed Cases from yesterday (T0) and earlier projections from the previous 7 days

-

250,000

500,000

1/22 2/22 3/22 4/22 5/22 6/22

Actuals T-0T-1 T-2T-3 T-4T-5 T-6T-7 T-8T-9 T-10T-11 T-12T-13 T-14

Forecasted trajectories for New Cases

Out-sample test results comparing Actuals 5/27/2020 with historically calibrated versions from the past

375,000

380,000

385,000

390,000

395,000

T-14T-13T-12T-11T-10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1

Forecast Actual

Forecast trajectories for Active Cases (confirmed -death-recovered)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1/22 2/22 3/22 4/22 5/22 6/22

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

1/22 2/22 3/22 4/22 5/22 6/22

… TWO MONTHS LATER…NEW YORK ON JUNE 10, 2020 – ACTUAL OUTCOMES TRACK PROJECTIONS FROM APRIL CLOSELY

April forecast predicted New cases to peak around 11,000

Active case curve adjusted to reflect absence of recovery data

45© Oliver Wyman

2. DESIGN AND STUDY LONG-TERM SCENARIOSFAST-EVOLVING SCENARIOS, THAT ARE LINKED TO EPIDEMIOLOGY, ARE CRITICAL TO ENABLE AGILE AND PRAGMATIC DECISION MAKING

Active Confirmed Cases (US Total, Thousands)Select scenariosWe run 10+ scenarios for each region every day

Smart and also lucky

• Daily new cases and active cases remain within a target range of about 10K and 100K, respectively, for the foreseeable future

• No major outbreaks (single peak)

Plausible but optimistic

• Daily new and active cases remain within a target range through the end of the summer

• Future outbreaks are localized; while we see additional peaks, they are substantially smaller

Plausible but pessimistic

• We open successfully but daily new and active cases growth through the summer; we experience a second peak at the end of July, prompting a lockdown in August

• Future outbreaks are localized

Frequent blunt lockdowns

• We open too fast and too soon, prompting a spike in daily new cases (above recent peak) and subsequent lockdown

• Multiple peaks, some as high as or even exceeding the first peak

Health Crisis

• We open too fast and too soon, prompting a spike in daily new cases (above recent peak) and subsequent lockdown

• Cases grow unchecked throughout the summer and into the fall and eventually decline as we get broad immunity

010,00020,00030,000

Feb-20 Aug-20 Feb-21 Aug-21

0200400600800


0200400600800


0200400600800


0200400600800


46© Oliver Wyman

3. ASSESS MACROECONOMIC IMPACTWE THEN LINKED EACH PANDEMIC SCENARIO TO MACRO ECONOMIC VARIABLES

Macro scenarios derived from epidemiological scenarios Methodology for macro scenarios

• The Pandemic Navigator enables defining archetypical pandemic scenarios that are translated into macro- projections using

– Connections of pandemic to economic variables –e.g. sector shocks, unemployment

– Economic theory and statistical models to translate these into GDP, household indebtedness, real disposable income, …

– Structural approaches and historical events studies to project interest rates, foreign exchange rates and equity performance

Pandemic Navigator outputsNumber of Covid-19 cases

Macro variables for each scenarios: Unemployment rate

Scenario 3:Frequent

blunt lockdowns

Scenario 1:Smart and also lucky

Scenario 2:Plausible but pessimistic

0

5

10

15

20

Mar-20 Sep-20 Mar-21 Sep-21

0

5

10

15

20


0

5

10

15

20


Select scenarios

0

200

400

600

800


0

200

400

600

800


0

200

400

600

800


47© Oliver Wyman

4. EVALUATE SECTOR LEVEL IMPACT AND CREDIT RISKVIABILITY OF COMPANIES BY NAME/SECTOR/SCENARIO TO GUIDE BUSINESS OPPORTUNITIES AND CREDIT WORTHINESS

* Source: Company filings; Capital IQ; Oliver Wyman analysis; sample of public companies in select US industries -

Smart and also lucky% of companies in sector

Plausible but pessimistic% of companies in sector

Frequent blunt lockdowns% of companies in sector

10025 50 75

Pandemic Navigator scenarios*

Select Sectors

10025 50 75 25 50 75 100No liquidity need to weather the crisis Liquidity required but affordable High risk – potentially not viable

Human health and social work activities

Manufacture of computer products and electronic equipment

Scientific research and development

Computer programming, consultancy and related activities

Manufacture of air and spacecraft and related machinery

Manufacturing of plastic and metallic products

Programming and broadcasting activities

Manufacturing (tobacco, textiles and wearing apparel, furniture etc.)

Land transport and transport via pipelines

Manufacture of coke, refined petroleum products and chemicals

Arts, entertainment and recreation

Air transport

Food service activities

48© Oliver Wyman

4. EVALUATE SECTOR LEVEL IMPACT AND CREDIT RISKWE LINKED PANDEMIC SCENARIOS TO CONSUMER CREDIT POOLS AND STUDIED IMPACT ON LOSSES

Mortgage – cumulative loss by credit score* Credit Card – cumulative loss by credit score*

Personal Loan – cumulative loss by credit score* HELOC – cumulative loss by credit score*

Sample projections under plausible but pessimistic scenario

* Oliver Wyman Ascend Portfolio Loss Forecaster

49© Oliver Wyman

5. ASSESS DEMAND AND MANAGE SUPPLY CHAINSUPPLY CHAIN RISK MODELLING IS INFORMED BY A WIDE RANGE OF DATA SOURCES

Case developmentand trends

Expected peak case count and timing

Internet SearchImplies self-reported symptoms as additional indicator of virus spread

Restrictions and re-openings

Likelihood and timing of restrictions and re-openings

Google/Apple Mobility indices

Oliver Wyman Pandemic Navigator forecasts

# of tests conductedas percentage of population

Library of reopening plans

Oxford Stringency Index evaluates the robustness of worldwide

government responses by country

Supply chain impactTiming of supply chain

disruptions, allowing for optimized planning

Business impactForecasts for business impact encompassing all anticipated

COVID-19 effects

Risk modelling scorecard

News inputsAnnounced plans and sentiment of

local leaders

50© Oliver Wyman

6. RE-BASELINE AND REVISIT PLAN / BUDGETABILITY TO DEFINE AND STUDY WHAT-IF SCENARIOS AND TO ‘RE-BASELINE’ P&L AND BALANCE SHEET

51© Oliver Wyman

7. CONTROL TOWER FOR RETURN TO WORK AND MANAGING OVER THE LONG HAUL OF SUPPRESSIONPANDEMIC NAVIGATOR FEEDS CRITICAL INFORMATION TO THE RECOVERY TOWER

WORKFORCE WORKPLACE WORKFLOW• Where is community spread likely

impacting employee sentiment?• When should a location open?• What facilities may need to close

again? • What phase of recovery is a

geography in?• What are leading indicators?

• Where should temperature checks remain in force?

• Where should additional cleaning be conducted?

RECOVERYTOWER

• Up-to-date case information tailored to corporate footprint

• Country, state and county level information

• Peak forecasts and estimated active cases

52© Oliver Wyman

READ OUR LATEST INSIGHTS ABOUT COVID-19 AND ITS GLOBAL IMPACT ONLINE

Oliver Wyman and our parent company Marsh & McLennan (MMC) have been monitoring the latest events and are putting forth our perspectives to support our clients and the industries they serve around the world. Our dedicated COVID-19 digital destination will be updated daily as the situation evolves

Visit our dedicated COVID-19 website:https://www.oliverwyman.com/coronavirus

https://www.oliverwyman.com/our-expertise/hubs/coronavirus.html?utm_source=marketing-collateral&utm_medium=pdf&utm_campaign=covid-19&utm_content=fs-webinar-presentation

QUALIFICATIONS, ASSUMPTIONS, AND LIMITING CONDITIONSThis report is for the exclusive use of the Oliver Wyman client named herein. This report is not intended for general circulation or publication, nor is it to be reproduced, quoted, or distributed for any purpose without the prior written permission of Oliver Wyman. There are no third-party beneficiaries with respect to this report, and Oliver Wyman does not accept any liability to any third party.

Information furnished by others, upon which all or portions of this report are based, is believed to be reliable but has not been independently verified, unless otherwise expressly indicated. Public information and industry and statistical data are from sources we deem to be reliable; however, we make no representation as to the accuracy or completeness of such information. The findings contained in this report may contain predictions based on current data and historical trends. Any such predictions are subject to inherent risks and uncertainties. Oliver Wyman accepts no responsibility for actual results or future events.

The opinions expressed in this report are valid only for the purpose stated herein and as of the date of this report. No obligation is assumed to revise this report to reflect changes, events, or conditions, which occur subsequent to the date hereof.

All decisions in connection with the implementation or use of advice or recommendations contained in this report are the sole responsibility of the client. This report does not represent investment advice nor does it provide an opinion regarding the fairness of any transaction to any and all parties. In addition, this report does not represent legal, medical, accounting, safety, or other specialized advice. For any such advice, Oliver Wyman recommends seeking and obtaining advice from a qualified professional.

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WEBINAR: COVID-19 AND IMPACT ON THE US ......WEBINAR: COVID-19 AND IMPACT ON THE US FINANCIAL SYSTEM Epidemiology modeling and consumer confidence June 24 th, 2020 CONFIDENTIALITY