Module 8

Question 1

Module 8

Answer 1

Organizational level risk

Question 2

What is expected to be learned from Module 8

Answer 2

Describe the categories of organizational risk

Question 3

Lots of ways to categorize the risk that organizations face

Answer 3

Hazard risk
Financial risk
Operational risk
Business risk
Strategic risk

Question 4

Why categorize

Answer 4

Expertise (Anna make sure its being addressed by good people), risk management technique, reports to stakeholders
Categories might be mutually exclusive

Question 5

Business Risk

Answer 5

Deviations in profitability

• Can be caused by many things, prices, regulation, competition
• operational, hazard, financial

Question 6

Operational Risk

Answer 6

Focus on BP case and analytics reading 
Def- Potential losses from internal sources
 -Manufacturing process
 -Fraud (employee selling info)
 -Mismanagement 
 -Employee mistake

Question 7

Hazard Risk

Answer 7

Very traditional risk management stuff

Defintion- potential losses that only have a down side ex- fire car accidents

Question 8

Financial Risk (Module 10A)

Answer 8

Potential variation due to financial causes

-Losses due to exchange rate, investment losses, credit risks, liquidity, risk, etc…

Question 9

Strategic Risk (Module 9)

Answer 9

Def- Potential losses (variation) from poor business decisions.

• Product mix
• Supplier Choice

Many decisions fall under “strategy” if any of the are wrong/faulty= strategic risk

Question 10

Reputational risk

-Strategic risk

Answer 10

Does the product sell individual or does its reputation also sell it.
Ex- Starbucks

Question 11

BP Case

Answer 11

Explain overriding themes

risk/reward trade-offs presented in the BP case

Question 12

Case Study Pt 2

Answer 12

-Focuses on BP, 2 safety issues in 2005-2006

Question 13

Overriding themes of case

Answer 13

Corporate strategy
Public relations
Corporate culture
Workplace safety
Process management

Question 14

BP works as a triangle

Answer 14

Vertically integrated firm
Top is exploration and extraction

Bottom is transport issue, and distributing

Question 15

1st Incidents

Answer 15

March 23, 2005
-Explosion at Texas City Refinery
15 dead
170 injured

Question 16

2nd Incident

Answer 16

March 2006

-Oil Spill at Prudhue Bay Alaska

Question 17

Result of two incidents

Answer 17

Jan. 2007
CEO Lord Browne Resigns
-widely recognized as one Britains greatest business men

Question 18

BP

Answer 18

British Petroleum
formed in1998
-Merger of British Petroleum and AMOCO
-Top 5 Global Oil Co

BP: over 100 years old

Question 19

Browne becomes CEO in 1995

Answer 19

Rebranding

• Environmentally friendly oil company
• HSE performance. Health, Safety and Environmental
• Growth through Merge and Acquisition (with Amaco)
• Green Company- Climate Change talk in 2002

Alternative Fuels: Solar

Question 20

Corporate strategy

Answer 20

Growth through MA
-Do more with less
-Cost cutting
or Cost conscious

Question 21

1st incident

Answer 21

Texas refinery
OSHA give 21.3M in fines

US Chemical Safety and Hazard Investigation Board
-BP endangered workers to cut costs

Question 22

Baker Report

Answer 22

Former secretary of state does a safety report on Texas

Question 23

2nd Incident

Answer 23

Leak discovered in Prudhoe Alaska
-Largest oilfield in US

Corroded Pipe
BP shuts down old fields
16 of 22 miles of pipe needs to be replaced

Question 24

Baker Report

Answer 24

BP did a horrible job at process safety

• Fact that you shouldn’t have leaks etc.
• Personel safety is fine

Question 25

Followup

Answer 25

BP keeps fucking up and getting fined -Market Exon mobile surpasses BP because they don't suck shit

Question 26

Overriding themes

Answer 26

-Corporate Strategy -Public Relations -Corporate Culture -Workplace safety -Process Management What is the risk/reward

Question 27

Big Data Pt 3

Answer 27

Big Data and Black Swan Explain the role of behavioral analytics in risk management Explain how to manage the risks of data driven decision making

Question 28

Big Data

Answer 28

We create so much data, how can organizations use it - Data mining - Buying trends (FB ad) - Targeted marketing - Mitigate risky business

Question 29

Big Data pt 2

Answer 29

-Mitigate risky business practices, employee fraud (operational risk), and Black Swan -rare event-

Question 30

Most Risk Management

Answer 30

Predict whats going to happen by looking at past data

Question 31

Most Risk Management pt 2

Answer 31

Article is presented as a Q and A | -Business risk, operational risk, black swans, and behavioral analytics

Question 32

Q & A

Answer 32

What are the diff types of risk organizations need to think about from operational risk to the so-called black swans

Question 33

Operational risk

Answer 33

the people, processes, and systems in place to produce the company's product/service -Ex: internal fraud, employment practices (sexual misconduct), etc

Question 34

Black Swan

Answer 34

Extreme outlier: hard to predict, rare event that generally people don't expect -Ex- Mortgage crisis, 9/11

Question 35

How has risk changed ins recent years

Answer 35

- Globalization, cascading network effect | - Think dominos

Question 36

Where does data analytics fit in?

Answer 36

Tools to predict areas of vulnerability in organization to black swans Look for factors of human behavior -Laziness, fatigue, moral or ethical "opportunities", stressors

Question 37

Could data analytics have helped BP (Gulf Oil Spill)

Answer 37

- Reports that on-site employees were worried about such an event, but no channel to communicate - BP didn't monitor system closely, costly to do so, cut corners

Question 38

Behavior Analytics

Answer 38

Uses big data to predict future individuals actions Ex- Drug test

Question 39

Data Driven Decision Making

Answer 39

-Base decisions on data analysis rather than intuition

Question 40

Whats new about Data Driven decision making

Answer 40

Predictive analytics and AI

Question 41

What are the risks of DDDM

Answer 41

``` Poorly designed or incorrect models Poorly though out goals Protection against privacy Poor communication of results Statistical anomalies Inappropriate criteria ```

Question 42

Poorly designed or incorrect models

Answer 42

- Ex: Housing prices always increase - Wrong assumptions - Poor proxies - Algorithms that don't account for source (i.e. "fake news") - No fact checking - No results - Poor data sources

Question 43

Inappropriate criteria

Answer 43

-Race -Religion -Gender -Age (too old) -Credit score +Insurance pricing and insurance score (credit score). Trying to discriminate against their customers. Proxy by race -Zip code +Banking and redlining

Question 44

How do you manage the risks?

Answer 44

``` Oversight/regulation -Laws -Auditors Embedded morality Data models are inputs to decision makers, not the decision makers themsleves ```