Wednesday, March 5, 2014 Houston, TX. 1:30 2:45 p.m. IMPROVING RISK MANAGEMENT AND INSURANCE PLACEMENTS USING ANALYTICS

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1 Wednesday, March 5, 2014 Houston, TX 1:30 2:45 p.m. IMPROVING RISK MANAGEMENT AND INSURANCE PLACEMENTS USING ANALYTICS Presented by Joe Beesack Senior Vice President, Alternative Risk Solutions Practice Willis Calgary William (Bill) Chan Senior Vice President, Alternative Risk Solutions Practice Willis Calgary The quality of a marketing submission can impact the interest of the underwriter in the account and can drive the breadth of coverage under and cost-effectiveness of the program. Often, when preparing submissions for marketing their accounts, companies rely on traditional peer benchmarking. This session will provide an overview of how to use a specific company s financials to help model the optimal insurance program for the company. Copyright 2014 International Risk Management Institute, Inc. 1

2 Notes This file is set up for duplexed printing. Therefore, there are pages that are intentionally left blank. If you print this file, we suggest that you set your printer to duplex. 2

3 Joe Beesack Senior Vice President, Alternative Risk Solutions Practice Willis Calgary Joe Beesack s primary role is to serve as a practice leader for Statistical and Quantitative Analysis. At the strategic level, he provides statistical, analytical, and financial analysis for projects that aid our clients in making informed decisions to establish the most appropriate risk retention and risk transfer structure to suit their unique needs and objectives. At the operational level, his specific area of specialization is in the creation of computerized applications that reflect the complexities of sophisticated risk financing quantification needs. He has developed numerous analytical tools to enhance the practice s ability to deliver quick and accurate analysis, as well as developed complex conceptual risk quantification models. Prior to joining Willis, Mr. Beesack was the chief analytical officer and senior vice president for all quantitative risk analysis for Aon Reed Stenhouse Inc., specializing in alternative risk financing mechanisms. He completed his honors bachelor of science degree in mathematics at McMaster University, immediately followed by a bachelor of education degree from the University of Windsor, and subsequently obtained his certificate in Canadian Risk Management. William (Bill) Chan Senior Vice President, Alternative Risk Solutions Practice Willis Calgary Bill Chan is also a charter holder with the Chartered Financial Analyst Institute. Mr. Chan began his strategic planning and risk management career with a multinational oil and gas company in Prior to his work in risk management, he worked in strategic planning, statistics, and econometrics. Over the years, his clients have included major corporations from the oil and gas, power generation and distribution, entertainment, hospitality, home building, and agriculture industries, as well as governments at the federal, provincial, territorial, and municipal levels. He provides analysis of risk and the impact it has on a company s financials and can help assess a variety of alternative solutions and risk management tools beyond insurance. Mr. Chan has published articles in risk management trade journals and has spoken at numerous seminars, including those presented by the Institute of Corporate Directors, CRIMS, and SARIMS, in addition to guest lecturing at the University of Calgary. He also teaches risk financing at the University of Calgary, continuing education, and has taught risk management at the Haskayne School of Business. Mr. Chan holds an undergraduate degree in political science, a master s degree in economics, and an M.B.A. 3

4 Notes This file is set up for duplexed printing. Therefore, there are pages that are intentionally left blank. If you print this file, we suggest that you set your printer to duplex. 4

5 Improving Risk M anagement and Insurance Placements Using Analytics Presented By: 1 Joe Beesack Senior Vice President Alternative Risk Solutions Practice Willis Calgary William (Bill) Chan Senior Vice President Alternative Risk Solutions Practice Willis Calgary IRMI.com Improving Risk Management and Insurance Placements Using Analytics IRMI Energy Conference March 4-6, 2014 Houston, TX 5

6 Agenda Analytics and the risk management decision process Analytics for insurance purchasing Analytics in enterprise risk management (ERM) Analytics for captives Page 1 Analytics and the risk management decision process Page 2 6

7 How To Think About Risk 1. Understand industry trends, competition and business strategy 2. Define risk tolerance 3. Identify priority exposures and risk scenarios 4. Model loss frequency and severity 5. Quantify Total Cost of Risk for Priority Risks / Define a Crisis 6. Avoidance 7. Mitigation 8. Retention Balance Sheet Captive 9. Transfer To Insurers To Capital or Other Markets R I S K S T R A T E G Y I M P L E M E N T A T I O N Page 3 Analytics The transformation and value derived can be summarized as: Better Decisions Knowledge Information Data The goal is to transform data, through appropriate tools and technologies, to make informed decisions Page 4 7

8 Applications of analytics in risk management: What are my exposures? / What coverages do I need? How much coverage / limit do I need? What is my optimal deductible? How much risk can I assume? Should I access alternative risk solutions? How should I allocate my cost of risk? Page 5 8

for insurance purchasing Page 6 Analytics A typical submission to Underwriters addresses these points: Risk Identification Risk Assessment / Quantification / Forecasts Retention Capacity / Loss

9 for insurance purchasing Page 6 Analytics A typical submission to Underwriters addresses these points: Risk Identification Risk Assessment / Quantification / Forecasts Retention Capacity / Loss Forecasts Loss Forecasts / Market Conditions Management knowledge of business and risks Benchmarking with peers What are my exposures? / What coverage do I need? What limits should I buy? What deductible should I have? What is a reasonable range for my premiums? Page 7 9

10 In the context of the insurance purchasing decision: Probabilistic Loss Forecasts can be used to: 1. Evaluate theoretically appropriate attachment points 2. Assess the reasonableness of market quotes 3. Determine appropriate funding levels 4. Determine appropriate annual aggregates 5. Compare total cost of risk for alternative market options 6. Prepare management for the true cost of risk, should soft market pricing be prevalent Page 8 Analytics Example 1: Data available: Company specific loss data Company specific engineering scenario analysis Energy/industry databases Multiple analytical loss models developed to understand and quantify the risk Result: able to demonstrate to markets the low likelihood of event, thereby reducing attachment point without materially increasing premium Page 9 10

11 Example 2: Company loss and exposure data was available for modeling Developed loss model to ascertain appropriate attachment points Compared forecasts to market prices at various attachment points Result: Provided client with a rational basis for program design Lowered total cost of risk for client Page 10 11

12 WITHIN EACH AND EVERY RETENTION Probability Retention 1,000 10,000 25,000 50, , , ,000 1,000,000 2,000,000 25,000,000 of Loss Mean: 195, ,455 1,161,022 1,517,190 1,833,314 2,084,347 2,155,007 2,174,991 2,178,790 2,179,313 Aggregation St. Dev'n: 13,436 72, , , , , , , , ,823 1 Year in: Percentile , ,424 1,157,022 1,511,757 1,822,181 2,055,439 2,115,466 2,123,989 2,124,155 2,124, , ,730 1,218,685 1,603,013 1,950,134 2,234,384 2,317,961 2,332,003 2,332,047 2,332, , ,330 1,252,938 1,653,662 2,026,617 2,342,833 2,441,652 2,465,512 2,466,028 2,466, , ,248 1,275,141 1,693,485 2,080,143 2,419,826 2,528,006 2,557,557 2,560,516 2,560, , ,116 1,338,493 1,791,334 2,227,060 2,629,888 2,769,640 2,820,673 2,824,909 2,824, , ,401 1,391,873 1,873,440 2,338,270 2,785,542 2,973,701 3,058,135 3,073,285 3,073, , ,429 1,408,019 1,897,821 2,375,913 2,837,450 3,034,171 3,130,889 3,150,525 3,150, , ,653 1,431,923 1,928,681 2,420,564 2,899,484 3,121,153 3,225,841 3,250,133 3,250, , ,095 1,444,101 1,948,534 2,450,952 2,941,071 3,177,076 3,286,958 3,316,206 3,316, , ,637 1,458,719 1,975,425 2,486,388 2,994,305 3,230,000 3,343,701 3,394,120 3,394, , ,121 1,496,919 2,051,775 2,578,976 3,163,252 3,394,706 3,554,165 3,619,798 3,619, , ,499 1,622,154 2,210,070 2,894,899 3,560,240 3,892,340 4,208,356 4,480,016 4,518,984 1, EXCESS EACH AND EVERY RETENTION Probability Retention 1,000 10,000 25,000 50, , , ,000 1,000,000 2,000,000 of Loss Mean: 1,983,341 1,431,858 1,018, , ,999 94,966 24,306 4, Aggregation St. Dev'n: 499, , , , , , ,193 64,049 28,243 1 Year in: Percentile 50 1,929,829 1,368, , , , ,132,716 1,567,922 1,134, , ,701 58, ,267,483 1,689,291 1,244, , , , ,358,261 1,776,937 1,322, , , , ,623,127 2,037,785 1,568,520 1,137, , ,133 2, ,869,977 2,269,970 1,793,535 1,358, , , , ,945,293 2,358,368 1,868,747 1,428, , , , ,051,231 2,439,482 1,963,901 1,518,124 1,080, , , ,116,042 2,505,651 2,023,029 1,577,000 1,137, , , ,184,143 2,583,926 2,096,648 1,651,746 1,206, , , ,422,238 2,814,125 2,338,124 1,892,371 1,442, , ,112 51, ,324,845 3,709,884 3,203,934 2,653,887 2,204,671 1,647,822 1,267, , ,000.0 Page 11

13 in Enterprise Risk Management Page 12 Analytics Tools Available Establish The Context Discussions with Client Communicate and Consult Identify Risks Analyze Risks Evaluate Risks Risk Assessment Monitor and Review Risk Assessment Loss Forecasting, Custom Risk Modeling Retention Capacity Analysis Treat Risks Insurance Optimization, Captive Consultation, Risk Mitigation Strategy Page 13 13

14 Discussions with Clients to gain an understanding of the clients : Industry Corporate objectives Position in the energy industry Risk management philosophy and focus (earnings vs cash flow vs profitability; short term vs long term) Risk bearing capacity, tolerance and appetite Page 14 Analytics Analytical Tools: Risk Assessment Probabilistic Loss Forecasting Customized Risk Modeling Risk Bearing Capacity Analysis Captives Page 15 14

15 Risk Assessment What risks do we have? What risks are of a priority concern? What controls do we have in place for those risks? What can we do to mitigate the risks? Part of good corporate governance Page 16 15

Risk assessment, recruiting, training 5: 4: 3: 2: 1: Time Commitment Control Causes Monitor After Immediate Action Before Contingency Plans 1: 2: 3: 4: 5: P(Event) 1 yr in: 10.

st. Cost: 5,000 Paid over: 4 Ann. Benefit: 99,800,000 Ann. Inv.

Assessment of the major risks affecting achievement of 2 Unusual (has occurred somewhere) 2 Low ABC Company Limited's growth objectives during the 3 Possible (known to occur occasionally) 3 Moderate

Triggers Consequences Current Controls Cat Category L I Gross Risk Further Risk Treatment Measures L I Gross Risk No.

16 Risk assessment, recruiting, training 5: 4: 3: 2: 1: Time Commitment Control Causes Monitor After Immediate Action Before Contingency Plans 1: 2: 3: 4: 5: P(Event) 1 yr in: ,000,000 Pr 1 yr in: Invest. Cost: 5,000 Paid over: 4 Ann. Benefit: 99,800,000 Ann. Inv. Cost: 1,250 Analytics 16 Risk Assessment Risk Assessment for:abc Company Limited Score Likelihood Score Impact Date: 21-Jun-10 1 Remote (could happen but unlikely) 1 Negligible Business Objective(s): Assessment of the major risks affecting achievement of 2 Unusual (has occurred somewhere) 2 Low ABC Company Limited's growth objectives during the 3 Possible (known to occur occasionally) 3 Moderate next 3 years 4 Probable (known to occur) 4 Significant 5 Expected (occurs often) Assessment of the risk 5 Catastrophic / Major With treatment measures [As it is now] implemented Risk Underlying Triggers Consequences Current Controls Cat Category L I Gross Risk Further Risk Treatment Measures L I Gross Risk No. Vulnerabilities Risk Rank Risk Rank 1 Over-commitment of Acquisition or key client Failure to meet project Surety capacity, Go - No Go, strong 8 Strategy and Risk assessment, recruiting, training resources demand escalates and deadlines and leadership, client surveys policy new major client expectations, loss of future revenue, hiring or promotion of unqualified individuals 2 Emerging markets using Failure to respond to new Loss of market share newer technology entrants to existing Excess capacity markets Competitor analysis Customer feedback 9 Technology / Industry change Likelihood 'Risk Matrix' - Before and After Controls Expected 5 8 Probable 4 8 2, 10, Possible 3 14 Unusual , 17, 18 Control Causes Monitor 6, 7, 10, 12 5, 12 3, 15, 16 4, 7, 9, 11, 18 1, 2, 9 Remote 1 5, 11, Immediate Action 3, 15 1 Contingency Plans Impact Negligible Low Moderate Significant Catastrophic Risk Scores Likelihood Impact Gross Risk Risk Rank Technology / Industry change Analysis by Source of Risk and Stratified by Risk Rank (Before Improvements) Risk No.: 1 Underlying Vulnerabilities: Over-commitment of resources Category: Strategy and policy Triggers: Acquisition or key client demand escalates and new major client Consequences: Failure to meet project deadlines and expectations, loss of future revenue, hiring or promotion of unqualified individuals Before Improvement After Improvement Strategy and policy Products / Services Processes Political / Social People Natural events Investments Likelihood Current Controls: Surety capacity, Go - No Go, strong leadership, client surveys Further Risk Treatment Measures: Responsible: Joe3 Target Date: 1/1/2010 Risk Number: Risk Description: Responsible: Target Date: Last Updated: Cost : Benefit Ratio Event Cost BEFORE Over-commitment of resources Joe3 1 1/1/ : 79, ,000,000,000 Budgeted? Costs Describe investment to complete improvements Capital Cost (Yes/No) Likelihood Impact Budgeted? Approved (Yes/No) (Yes/No) Economic <RR<=25 15<RR<=20 10<RR<=15 5<RR<=10 RR<= Impact Action Deliverables Measure of Success Allocated to Delivery (when) Budget Cost Budget time Event Cost AFTER Page 17

17 Risk Assessment Know: your risks what controls you have in place to mitigate What actions are to be put in place to further mitigate those risks Be prepared to explain these to the market Demonstrates knowledge of the risks you are trying to have insured Page 18 Analytics Analytical Tools: Risk Assessment Probabilistic Loss Forecasting Risk Bearing Capacity Analysis Customized Risk Modeling Captives Page 19 17

18 18 CLAIM AMOUNT ($) Legend Actual Fitted Fitted PERCENTILE Page 20 20

19 19 WITHIN EACH AND EVERY RETENTION Probability Retention 1,000 10,000 25,000 50, , , ,000 1,000,000 2,000,000 25,000,000 of Loss Mean: 195, ,455 1,161,022 1,517,190 1,833,314 2,084,347 2,155,007 2,174,991 2,178,790 2,179,313 Aggregation St. Dev'n: 13,436 72, , , , , , , , ,823 1 Year in: Percentile , ,424 1,157,022 1,511,757 1,822,181 2,055,439 2,115,466 2,123,989 2,124,155 2,124, , ,730 1,218,685 1,603,013 1,950,134 2,234,384 2,317,961 2,332,003 2,332,047 2,332, , ,330 1,252,938 1,653,662 2,026,617 2,342,833 2,441,652 2,465,512 2,466,028 2,466, , ,248 1,275,141 1,693,485 2,080,143 2,419,826 2,528,006 2,557,557 2,560,516 2,560, , ,116 1,338,493 1,791,334 2,227,060 2,629,888 2,769,640 2,820,673 2,824,909 2,824, , ,401 1,391,873 1,873,440 2,338,270 2,785,542 2,973,701 3,058,135 3,073,285 3,073, , ,429 1,408,019 1,897,821 2,375,913 2,837,450 3,034,171 3,130,889 3,150,525 3,150, , ,653 1,431,923 1,928,681 2,420,564 2,899,484 3,121,153 3,225,841 3,250,133 3,250, , ,095 1,444,101 1,948,534 2,450,952 2,941,071 3,177,076 3,286,958 3,316,206 3,316, , ,637 1,458,719 1,975,425 2,486,388 2,994,305 3,230,000 3,343,701 3,394,120 3,394, , ,121 1,496,919 2,051,775 2,578,976 3,163,252 3,394,706 3,554,165 3,619,798 3,619, , ,499 1,622,154 2,210,070 2,894,899 3,560,240 3,892,340 4,208,356 4,480,016 4,518,984 1,000.0 EXCESS EACH AND EVERY RETENTION Probability Retention 1,000 10,000 25,000 50, , , ,000 1,000,000 2,000,000 of Loss Mean: 1,983,341 1,431,858 1,018, , ,999 94,966 24,306 4, Aggregation St. Dev'n: 499, , , , , , ,193 64,049 28,243 1 Year in: Percentile 50 1,929,829 1,368, , , , ,132,716 1,567,922 1,134, , ,701 58, ,267,483 1,689,291 1,244, , , , ,358,261 1,776,937 1,322, , , , ,623,127 2,037,785 1,568,520 1,137, , ,133 2, ,869,977 2,269,970 1,793,535 1,358, , , , ,945,293 2,358,368 1,868,747 1,428, , , , ,051,231 2,439,482 1,963,901 1,518,124 1,080, , , ,116,042 2,505,651 2,023,029 1,577,000 1,137, , , ,184,143 2,583,926 2,096,648 1,651,746 1,206, , , ,422,238 2,814,125 2,338,124 1,892,371 1,442, , ,112 51, ,324,845 3,709,884 3,203,934 2,653,887 2,204,671 1,647,822 1,267, , ,000.0 Page 21

Probabilistic Loss Forecasting Understand the underlying data Is it representative of the risk moving forward Agree with the exposure assumptions Agree with the loss assumptions; development,

20 Probabilistic Loss Forecasting Understand the underlying data Is it representative of the risk moving forward Agree with the exposure assumptions Agree with the loss assumptions; development, trending Agree with the loss modeling applied Be prepared to articulate this with the markets and be able to discuss pricing in a meaningful and rational basis Page 22 Analytics Analytical Tools: Risk Assessment Probabilistic Loss Forecasting Customized Risk Modeling Risk Bearing Capacity Analysis Captives Page 23 20

21 Custom Risk Modeling Understand the magnitude of potentially severe risks with low likelihood For risks where historical data is not available or not representative of our risks, develop customized, credible loss models of the risk Similar output and application as Probabilistic Loss Forecasts Page 24 21

22 Scenario Based 22 Risk Risk Description Severity Range 1 XYZ has outsourced processing of transactional data to Y Corp. Disgruntled employee of Y erases transactions resulting in legal liability for XYZ Low End of Impact XYZ Corp Risk Register Products Liability High End of Impact Probability of Event (Likelihood Loss Event (x of loss in a out of y given year) years) Frequency 1-a 50.00% b ERASURE OF A SMALL SET OF SPECIFIC 2,500,000 7,500, % TRANSACTIONS 1-c 10.00% d ERASURE OF ONE DAY S TRANSACTIONS FOR A FUND 10,000,000 17,500, % GROUP 1-e 2.00% f ERASURE OF ALL TRANSACTIONS IN A QUARTER FOR A 45,000,000 75,000, % FUND GROUP 1-g 0.40% h 0.20% i 0.10% ,000 x years out of y years Comments Lognormal ( , ) Smoothed Fit Page 25

23 Fitting distributions to the sub-scenarios 1,000 Risk 1: Expected Losses = $2.671M Pr(Loss) < Expected = 88.5% Pr(Loss) > Expected = 11.5% 1, Risk 1: Expected Losses = $2.671M Pr(Loss) < Expected = 88.5% Pr(Loss) > Expected = 11.5% Return Period = Number of Years Between Loss Events (Logarithmic Scale) <--- Higher Freq/Lower Severity Lower Freq/Higher Severity ---> Projected Loss Severity ($Millions) 1-2yr 1-7yr 1-10yr 1-13yr 1-50yr 1-55yr 1-250yr 1-500yr yr Fitted Fitted Severity ($MM) Percentile 1-13yr 1-50yr 1-55yr yr 1-2yr 1-7yr 1-10yr 1-250yr 1-500yr Fitted Page 26

24 Custom Risk Modeling: Conditional relationships Dependency analysis Model predicated on potential paths/outcomes depending on underlying combination of events 24 Outcome 1: 10% 30% Impact A Impact 70% B 20% Impact A Scenario A Outcome 2: 50% Outcome 3: 40% 50% 25% 25% Impact C Impact E Impact D 20% 60% Impact D Impact C Page 27

25 Custom Risk Modeling Identify and understand the potential events Draw on industry and company specific data Identify likelihood and impact ranges Develop representative risk model Quantify risk Communicate it with the market Page 28 Analytics Analytical Tools: Risk Assessment Probabilistic Loss Forecasting Customized Risk Modeling Risk Bearing Capacity Analysis Captives Page 29 25

26 Risk Bearing Capacity Helps us understand: how much financial impairment could be absorbed in risk on an annually recurring basis ability to assume various retention structures Enables Risk Manager / CFO rationalize taking on higher retentions / new risks Page 30 26

27 Retention Capacity FINANCIAL MEASURES AMOUNTS WEIGHTS ESTIMATED US$(million) MINIMUM MAXIMUM MEDIAN SELECTED RET. CAP. Wgt 1 27 LIQUIDITY: Value Include Cash Balance: 4, % 3.0% 2.0% 2.0% % Inventories: 5, % 4.0% 2.5% 2.5% % Working Capital: 3, % 4.0% 2.5% 2.5% % Operating Cash Flow: 7, % 5.0% 3.5% 3.5% % FINANCIAL STRENGTHS: Shareholders' Equity: 30, % 2.0% 1.3% 1.3% % Total Assets: 60, % 1.5% 0.9% 0.9% % EARNINGS: Gross Revenue: 20, % 1.5% 1.0% 1.0% % Pre-Tax Earnings: 7, % 5.0% 3.0% 3.0% % Page 31

28 Retention Capacity 28 Page 32

29 Retention Capacity 29 Page 33

30 Risk Bearing Capacity Understand your capacity to retain risk Addresses the issue of: Am I buying too much insurance? Can I afford to buy less? Take advantage of hard and soft market insurance cycles Analytics Page 34 How To Think About Risk 1. Understand industry trends, competition and business strategy 2. Define risk tolerance 3. Identify priority exposures and risk scenarios 4. Model loss frequency and severity 5. Quantify Total Cost of Risk for Priority Risks / Define a Crisis 6. Avoidance 7. Mitigation 8. Retention Balance Sheet Captive 9. Transfer To Insurers To Capital or Other Markets R I S K S T R A T E G Y I M P L E M E N T A T I O N Page 35 30

31 for Captives Page 36 Analytics Analytical Tools: Risk Bearing Capacity Analysis Probabilistic Loss Forecasting Risk Assessment Customized Risk Modeling Captives Page 37 31

32 In the context of Captives, analytics can provide the basis for decisions on: Captive Feasibility Value Created by the Captive New Product Development Captive Governance Capital Adequacy Transfer Pricing Audit Preparation and Support Page 38 Analytics Captives Is there a role for a captive in my insurance program A well structured captive demonstrates to markets knowledge and understanding of, and a willingness to participate in, the risks being insured Flexibility in program design Page 39 32

33 Analytics do not replace good business judgment Analytics supplement the business decision process by: making explicit the hidden assumptions encouraging a critical validation for assessment providing a rational basis for program design enabling and providing a framework for organizing knowledge about the business Page 40 Improving Risk Management and Insurance Placements Using Analytics Questions? IRMI Energy Conference March 4-6, 2014 Houston, TX Page 41 33

34 Improving Risk Management and Insurance Placements Using Analytics Thank-you! IRMI Energy Conference March 4-6, 2014 Houston, TX Page 42 34

Tuesday, March 17, 2015 Houston, TX. 3:45 5:00 p.m. CAPTIVATING RISK: ART MARKET AND CAPTIVE SOLUTIONS

Tuesday, March 17, 2015 Houston, TX 3:45 5:00 p.m. : ART MARKET AND CAPTIVE SOLUTIONS Presented by Michael O Neill, CPCU, ARM President and CEO American Contractors Insurance Group Many companies look