OPTIMISING INSURANCE POLICY DECISIONS FOR THE POWER INDUSTRY USING PALISADE SOFTWARE May 2012.

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1 OPTIMISING INSURANCE POLICY DECISIONS FOR THE POWER INDUSTRY USING PALISADE SOFTWARE May 2012.

2 Who are we EPM is the second largest business group in Colombia, consisting of 42 enterprises (18 in Colombia, and 24 overseas) that acts in the sectors of Electric Energy, Natural Gas, Waters and Telecommunications, with investments in most of Colombia, Panama, Guatemala and El Salvador. Maxseguros is EPM s captive reinsurer, founded in Bermuda in 2008,, with the purpose of reinsuring the risks of its parent company and its subsidiaries. Under Bermuda Insurance Act, Maxseguros is a Class 2 captive reinsurer, and is authorized to negotiate policies for Property, Terrorism, All-Risk Construction, Directors and Officers, Infidelity and Financial Risk.

3 Who We Are JLT Re COLOMBIA is the first reinsurance brokerage company in Colombia with over 30 years of experience in the reinsurance brokerage. JLT Re Colombia is the leader in the Colombian reinsurance consultancy market for many industries including Oil & Gas, Energy Construction, Aviation, Financial Lines, among others. JLT RE Colombia is a subsidiary of Jardine Lloyd Thompson Group PLC (JLT Group PLC). JLT Group PLC, based in London and founded in 1832, is a leader in risk management, insurance and reinsurance brokerage and benefits. Through affiliated companies and subsidiaries, JLT has over 100 offices world-wide in 36 countries and employs over 6,000 people.

4 Introduction Transformers Model is: An in house development A real time decision tool An on going tool An idea that works With one policy: Keep it simple

5 What is the issue? Insurance policies are important mechanisms for transferring risk and are essential for businesses needing to protect themselves from unforeseen events. The insurance world contains an infinite amount of different policies based on changing parameters such as deductible, depreciation which affect compensations. How then can you choose the optimal policy which suits the randomness (risk) of your assets?

6 How do you solve it? Our model calculates an optimal policy (a policy which reduces Cost of Risk) given the following: A set of stochastic conditions (probability of failure). A set of insurance policies (premiums, deductibles and depreciation) Although you could apply this to many different forms of assets for the purpose of this presentation we have used Power Transformers.

7 What is a Power Transformer? Power transformers are one of the most valuable assets in sub-stations and power plants. They are high-voltage equipment used for power generation, transmission and distribution systems. Our model considers a fleet of generation and transmission transformers, utilizing their age as the key input for evaluating probability of failure and we utilize voltage capacity and price as key inputs for evaluating potential loss during an event.

8 Probability of failure

9 Application 1 Policy Negotiation Support: Considering the randomness of the insured risk. Policies conditions. Transformers conditions. The Cost of the Risk.

10 Application 1: Policy Conditions Deductible: amount of loss that insured pays in a claim; includes the following types: Absolute dollar amount Time period amount (Business interruption) Depreciation: Actual or accounting recognition of the decrease in the value if hand asset (property) over a period of time, according to a predetermined schedule such as straight line depreciation. Premium: rate that an insured is charged, reflecting his or her expectation of loss or risk. The insurance company will assume the risk of the insured in exchange of a premium payment.

11 DEPRECIATION Application 1: Cases ALTERNATIVES MATRIX DEDUCTIBLE Case 1,1 Case 1,2 2 Case 2,1 Case 2,2

12 Application 1: Potential Total Loss Sensitivities (%) Transferring/Retention Graphs X: Transformers; Y: Percent of the Loss Depreciation, Deductible, Compensation

13 Application 1: Potential Total Loss Sensitivities ($USD) Transferring/Retention Graphs X: Transformers; Y: Percent of the Loss Depreciation, Deductible, Compensation

14 Application 1: Potential Partial Loss Sensitivities(%) Transferring/Retention Graphs X: Transformers; Y: Percent of the Loss Depreciation, Deductible, Compensation

15 Application 1: Potential Partial Loss Sensitivities ($USD) Transferring/Retention Graphs X: Transformers; Y: Percent of the Loss Depreciation, Deductible, Compensation

16 Application 1: Monte Carlo Simulation The model is based on a Monte Carlo Simulation for the creation of possible failure scenarios for a population of power transformers using: INPUTS (211) Total probability of failure (includes partial and total failure); and Probability of total failure (includes only total failure) Bernoulli Distribution OUTPUTS (55) Number of Failures, Depreciation, Deductible and Compensation.

17 Application 1: Monte Carlo Simulation ILUSTRATION OF THE SIMULATION.

18 Application 1: Risk Results (Number of Failures and Total Loss) Mean:4.27 Mean: 2,367,648

19 Application 1: Risk Results (Depreciation)

20 Application 1: Risk Results (Deductible)

21 Application 1: Risk Results (Compensation)

22 Application 1: Risk Results

23 Application 1: Cost of Risk To fully assess and choose the optimal policy we now have to consider what are the potential costs to be paid for different levels of coverage (Cost of Risk). Cost of Risk = Depreciation + Deductible + Premium Self-Retention

24 Cost of Risk

25 Application 2 As the power transformers get older their risk profile changes and thus the chosen policy is no longer optimized. This application shows the changing condition of the policy over time and indicates when to commit a major overhaul and whether to improve the maintenance program over a period of 10 years.

26 Application 2:

27 Application 2:

28 Study Case This model was utilized by Maxseguros, the Reinsurance company of he EPM GROUP, to assess the material damage coverage for a subsidiary and to assist in policy negotiation. The conclusion of the policy negotiation was: A simplified policy structure Elimination of USD 500,000 per year of premium which only provided an average coverage of USD 130,000 and up to USD 350,000 percentile) (90 th Reduction in the deductibles implying a premium increase of USD and a risk transferring on average USD 120,000 and up to USD 270,000 (90 th percentile).

29 Conclusions More effective policy negotiation: Retention/Transferring analysis Analysis of offered policies from Insurance and Reinsurance agencies Conditions negotiation (Deductible and Depreciation) Improved risk management: Cost of Risk calculation based in a stochastic vision Evaluation of maintenance programs and overhauls Probable maximum loss (PML) calculation This decision-making model is a powerful tool for supporting the decisions in the policy negotiations, based on risk quantification by using random variables.

30 SUMMING UP Transformers conditions Total Prob of Failure Policy Conditions MODEL Depreciation Deductible Compensation Number of Failure Total Loss Premium Cost of Risk The Decision

31 What is next? Consider more variables that can affect the Probability of failure of Transformers, such as weather and operating conditions. Designing and optimizing of maintenance programs and overhauls. Adapt the model to be utilized by other asset classes (generators, vehicles) Adapt the model for the interests of Insurance or Reinsurance agencies.

32 Thanks!

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