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["Instructions"]
[]
["1.) Find a quiet place and allow yourself the full four hour exam period to practice. Treat this just as you would the real exam."]
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["2.) Use the Navigator worksheet to see a brief description of each question, its point value and navigate between questions in your preferred order."]
[]
["3.) Answer each question either to the side or below the grey question area."]
[]
["4.) Use cell B1 on each question worksheet to mark whether you finished the question or not. You should use this feature to"]
["       reflect your confidence in your answer. This way you'll know if you're on the money or your guesses were lucky."]
[]
["5.) Grade each question harshly. It's better to put in extra reviewing time now than find the examiners aren't as generous as you hoped."]
["       Use increments of 0.25 when scoring; pay attention to the language used in the question and the point value. An \"identify\" question"]
["       is likely 0.25 per item while an \"explain\" or \"briefly justify\" gets 0.25 for the item and another 0.25 for the justification."]
[]
["6.) Enter your estimated score for each sub-part of a question in the box beneath the sub-part point value. They look like:"]
[]
["7.) Use the Point Grid to see your score and a breakdown of where you scored well and where you didn't."]
[]
["8.) Find the top five questions where you lost the most points. These are the topics you need to review the most intensely in the"]
["      next day or so."]
[]
["9.) On the BattleActs site → Custom Battles → BattleCards - Prior Exams → Select this exam and mark the cards you got right and wrong."]
["      This allows your Battle Readiness Quotient (BRQ) to track your strengths and weaknesses."]
[]
["10.) Use the Reset Exam button on the Point Grid worksheet to clear all of your work ready for a new attempt at this exam later."]
["slay the beast"]

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["FORMULA SHEET"]
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["• For the negative binomial distribution (for use on question 3):"]
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[null,"○"]
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[null,"○"]
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["• For the shifted Pareto distribution (for use on questions 7, 13 & 15):"]
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[null,"○"]
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[null,"○"]
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[null,"○"]
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[null,"○"]

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[]
[]
[]
["2017 Exam 8"]
[null,"Total Point Value","Sub-Part of Question",null,null,null,null,null,null,null,null,"Your Score","Sub-Part of Question"]
["Question",null,"(a)","(b)","(c)","(d)","(e)","(f)","(g)","Status",null,"Question","(a)","(b)","(c)","(d)","(e)","(f)","(g)"]
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[2,{"$f":"SUM(C9:I9)","v":2},1.75,0.25,null,null,null,null,null,{"$f":"INDIRECT(\"'\"&A9&\"'!B1\")","v":"Incomplete"},null,{"$f":"A9","v":2},"","","","","","",""]
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[4,1.75,0.5,0.75,0.5,null,null,null,null,"Incomplete",null,4,"","","","","","",""]
[5,1.75,1.75,null,null,null,null,null,null,"Incomplete",null,5,"","","","","","",""]
[6,3.5,1,1.5,0.5,0.5,null,null,null,"Incomplete",null,6,"","","","","","",""]
[7,1.75,1,0.75,null,null,null,null,null,"Incomplete",null,7,"","","","","","",""]
[8,1.75,0.75,1,null,null,null,null,null,"Incomplete",null,8,"","","","","","",""]
[9,3.25,3.25,null,null,null,null,null,null,"Incomplete",null,9,"","","","","","",""]
[10,1,1,null,null,null,null,null,null,"Incomplete",null,10,"","","","","","",""]
[11,2.5,1.5,1,null,null,null,null,null,"Incomplete",null,11,{"$f":"_xlfn.IFNA(IF(ISBLANK(INDEX(INDIRECT(\"'\"&$L18&\"'!$A:$A\"),MATCH(M$7,INDIRECT(\"'\"&$L18&\"'!$A:$A\"),0)+2)),\"\",INDEX(INDIRECT(\"'\"&$L18&\"'!$A:$A\"),MATCH(M$7,INDIRECT(\"'\"&$L18&\"'!$A:$A\"),0)+2)),\"\")","v":""},"","","","","",""]
[12,3.25,1,0.5,0.5,1.25,null,null,null,"Incomplete",null,12,"","","","","","",""]
[13,2.5,2,0.5,null,null,null,null,null,"Incomplete",null,13,"","","","","","",""]
[14,3,1.5,1.5,null,null,null,null,null,"Incomplete",null,14,"","","","","","",""]
[15,3.25,1.75,1,0.5,null,null,null,null,"Incomplete",null,15,"","","","","","",""]
[16,1.75,0.5,1.25,null,null,null,null,null,"Incomplete",null,16,"","","","","","",""]
[17,2,1.5,0.5,null,null,null,null,null,"Incomplete",null,17,"","","","","","",""]
[18,{"$f":"SUM(C25:I25)","v":3.25},2,0.75,0.5,null,null,null,null,"Incomplete",null,{"$f":"A25","v":18},"","","","","","",""]
[19,3,1.5,0.5,1,null,null,null,null,"Incomplete",null,19,"","","","","","",""]
[20,2.5,0.75,1.75,null,null,null,null,null,"Incomplete",null,20,"","","","","","",""]
[]
["Total",{"$f":"SUM(B8:B27)","v":53.75},null,null,null,null,null,null,null,null,null,"TOTAL",{"$f":"IF(SUM(IF(M8:S27=\"\",0,1))=0,\"\",SUM(M8:S27))","v":""},null,"Percentage:",{"$f":"IF(M29=\"\",\"\",M29/B29)","v":""}]
[]
[null,null,null,null,null,null,null,null,null,null,null,"Passing Score:",37.5]

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[]
[]
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[]
[]
["Navigator – Select a question to go to it"]
[]
["Question #",null,null,null,null,null,null,"Status"]
["Q1 -- Goldburd (GLM) -- Variable Selection (IQ) -- 8.50 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J8","v":"Incomplete"}]
["Q2 -- Robertson -- Weighted k-means -- 2.00 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J9","v":"Incomplete"}]
["Q3 -- Bailey & Simon -- Relative Credibility -- 1.50 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J10","v":"Incomplete"}]
["Q4 -- Goldburd (GLM) -- Model Selection -- 1.75 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J11","v":"Incomplete"}]
["Q5 -- Goldburd (GLM) -- Null, Saturated & Practical Models -- 1.75 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J12","v":"Incomplete"}]
["Q6 -- Goldburd (GLM) -- Logistic Regression --3.50 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J13","v":"Incomplete"}]
["Q7 -- Bahnemann -- Risk Loaded ILFs -- 1.75 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J14","v":"Incomplete"}]
["Q8 -- Bahnemann -- Layer Inflation -- 1.75 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J15","v":"Incomplete"}]
["Q9 -- ISO CGL -- Experience Modified Premium -- 3.25 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J16","v":"Incomplete"}]
["Q10 -- ISO CGL -- Schedule Rating -- 1.00 point",null,null,null,null,null,null,{"$f":"'Point Grid'!J17","v":"Incomplete"}]
["Q11 -- Fisher -- Efficiency Test -- 2.50 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J18","v":"Incomplete"}]
["Q12 -- Fisher -- Lee Diagrams -- 3.25 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J19","v":"Incomplete"}]
["Q13 -- Fisher -- Retrospective Premium -- 2.50 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J20","v":"Incomplete"}]
["Q14 -- Bahnemann -- Excess Severity -- 3.00 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J21","v":"Incomplete"}]
["Q15 -- Fisher -- Retrospective Rating Cashflow -- 3.25 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J22","v":"Incomplete"}]
["Q16 -- Clark -- Modeling Approaches -- 1.75 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J23","v":"Incomplete"}]
["Q17 -- Fisher -- ICRLL Adjustment -- 2.00 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J24","v":"Incomplete"}]
["Q18 -- Bernegger -- Exposure Curves -- 3.25 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J25","v":"Incomplete"}]
["Q19 -- Clark -- Swing Plans -- 3.00 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J26","v":"Incomplete"}]
["Q20 -- Grossi -- Risk Loaded Premiums -- 3.00 points",null,null,null,null,null,null,{"$f":"'Point Grid'!J27","v":"Incomplete"}]

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[1,"Incomplete",null,null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","An actuary has constructed a pure premium model using the Tweedie distribution with parameter"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('1'!A1,'Point Grid'!A:A,0))","v":8.5},"1 < p < 2 to determine manual rates for a Workers' Compensation book of business. The output of the model "]
[null,"is pure premium per $100 of payroll."]
[]
[null,"The following variables were considered for inclusion in the model:"]
[null,"Variable",null,"Description or Source of Data",null,null,"p-value"]
[null,"Industry",null,"Construction, Manufacturing, All Other",null,null,2E-3]
[null,"Return to Work Program",null,"Yes or No",null,null,8.0000000000000002E-3]
[null,"Employee Age",null,"Average Age in Years",null,null,3.0000000000000001E-3]
[null,"Employee Tenure",null,"Average Number of Years of Employment",null,null,5.0000000000000001E-3]
[null,"Location",null,"State of Jurisdiction",null,null,0.08]
[null,"Employee Morale",null,"Based on Results of Annual Company Survey",null,null,0.15]
[null,"Number of Back Injuries",null,"Supplied by Employer",null,null,0.01]
[]
[null,"The actuary has decided to use the following variables in the model: Industry, Employee Tenure, and"]
[null,"Return to Work Program."]
[]
["(a)","Discuss the statistical and non-statistical considerations of including each of the three variables (Industry,"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A20,'Point Grid'!$C$7:$I$7,0))","v":1.5},"Employee Tenure, and Return to Work Program) in the model."]
[]
[]
["(b)","Discuss the statistical and non-statistical considerations of excluding each of the remaining four variables"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A24,'Point Grid'!$C$7:$I$7,0))","v":2},"(Employee Age, Location, Employee Morale, and Number of Back Injuries) from the model."]
[]
[]
["(c)","The actuary fits the log link GLM model using the three selected variables. Given the fitted model parameters"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A28,'Point Grid'!$C$7:$I$7,0))","v":1.5},"below and the following information for a Manufacturing Workers' Compensation risk, calculate the "]
[null,"standard premium for this risk for an annual policy period."]
[]
[null,null,"Parameter",null,"Coefficient"]
[null,null,"Intercept",null,-0.63100000000000001]
[null,null,"Employee Tenure",null,-0.04]
[null,null,"Return to Work Program: Yes",null,-0.2]
[null,null,"Industry Type: Construction",null,0.35]
[null,null,"Industry Type: All Other",null,-0.55000000000000004]
[]
[null,null,"Data for Manufacturing Risk",null,null,"Value"]
[null,null,"Payroll",null,null,1000000]
[null,null,"Employee Tenure",null,null,"5 Years"]
[null,null,"Return to Work Program",null,null,"No"]
[null,null,"Actual Losses for Experience Rating",null,null,12500]
[null,null,"Fixed Expenses",null,null,1500]
[null,null,"Variable Expenses (as % of premium)",null,null,0.2]
[null,null,"Experience rating Constant (K)",null,null,10000]
[]
[null,"The actuary has graphed actual vs. modeled expected loss ratios by size of risk for the company's entire"]
[null,"Workers' Compensation book, which is shown below."]
[]
["(d)","In order to improve the model fit for larger risks, the actuary is considering incorporating the variable"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A51,'Point Grid'!$C$7:$I$7,0))","v":1.5},"\"Latest 3 Year Historical Losses\" into the model. Explain three reasons against doing so."]
[]
[]
[]
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[]
[]
[]
[]
[]
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[]
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[]
[]
[]
[]
[null,"The actuary is now developing a quote for a new Construction risk with $50,000,000 of payroll using this "]
[null,"rating plan."]
[]
["(e)","Describe two potential issues in developing a premium for this risk under this rating plan."]
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[]
[]
["(f)","Provide an alternative rating approach for this risk and briefly discuss the advantages of this alternative for"]
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[2,"Incomplete",null,null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","An actuary wants to cluster five Workers' Compensation classes based on excess ratios at"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('2'!A1,'Point Grid'!A:A,0))","v":2},"two limits: 500,000 and 1,000,000. The actuary decides to use a weighted k-means"]
[null,"algorithm with two clusters. Given the following:"]
[]
[null,"Class","On-Leveled Earned Premium ($000)","Normalized Excess Ratio at 500,000 Limit","Normalized Excess Ratio at 1,000,000 Limit","Initial Cluster"]
[]
[]
[null,1,6500,0.24,0.08,"A"]
[null,2,5000,0.35,0.2,"A"]
[null,3,4000,0.21,0.08,"A"]
[null,4,3000,0.11,0.03,"A"]
[null,5,5000,0.18,7.0000000000000007E-2,"B"]
[]
[null,"• Distance will be measured using the L2 (Euclidean) norm."]
[null,"• At the start of the algorithm, the actuary randomly assigns each class to a cluster."]
[]
["(a)","Determine the cluster for each class after the first iteration of the weighted k-means algorithm."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A19,'Point Grid'!$C$7:$I$7,0))","v":1.75}]
[]
[]
["(b)","Briefly describe one advantage of using the L1 measure rather than L2 when computing clusters."]
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[3,"Incomplete",null,null,null,"Navigator",null,"Solution"]
[]
["Points","The following data shows the experience of a merit rating plan for private passenger "]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('3'!A1,'Point Grid'!A:A,0))","v":1.5},"vehicles. The merit rating plan uses multiple rating variables, including territory."]
[]
[null,"Number of Accident-Free Years","Earned Car Years (000s)","Earned Premium ($000s)","Number of Incurred Claims"]
[]
[null,"5 or More",250,500000,15000]
[null,"3 and 4",100,90000,13500]
[null,"1 and 2",80,60000,8000]
[null,0,70,50000,10500]
[null,"Total",{"$f":"SUM(C8:C11)","v":500},{"$f":"SUM(D8:D11)","v":700000},{"$f":"SUM(E8:E11)","v":47000}]
[]
[null,"Territory","Frequency","Average Premium"]
[null,"A",0.05,1500]
[null,"B",0.1,2000]
[null,"C",0.15,1250]
[]
["(a)","Recommend and justify an exposure base for this merit rating plan."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A19,'Point Grid'!$C$7:$I$7,0))","v":0.75}]
[]
[]
["(b)","Calculate the relative credibility of an exposure that has been three or more years "]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A23,'Point Grid'!$C$7:$I$7,0))","v":0.75},"accident-free using the exposure base from part (a) above."]

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[4,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","An actuary has split data into training and test groups for a model. The chart below shows"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('4'!A1,'Point Grid'!A:A,0))","v":1.75},"the relationship between model performance and model complexity. Model performance"]
[null,"is represented by model error and model complexity is represented by degrees of freedom."]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["(a)","Briefly describe two reasons for splitting modeling data into training and test groups."]
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[]
[]
["(b)","Briefly describe whether each of the following model iterations has an optimal balance of"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A28,'Point Grid'!$C$7:$I$7,0))","v":0.75},"complexity and performance."]
[null,"   i. Model iteration 1: 10 degrees of freedom"]
[null,"  ii. Model iteration 2: 60 degrees of freedom"]
[null," iii. Model iteration 3: 100 degrees of freedom"]
[]
["(c)","Identify and briefly describe one situation where it is an advantage to split the data by time"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A34,'Point Grid'!$C$7:$I$7,0))","v":0.5},"rather than by random assignment."]

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[5,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","An analyst has fit several different variations of a logistic GLM to a data set containing 1,000"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('5'!A1,'Point Grid'!A:A,0))","v":1.75},"records of fraudulent claims and 9,000 records of legitimate claims. For each model variation"]
[null,"listed below, draw a quintile plot based on the training data. Label the axes and identify each"]
[null,"data series."]
[]
["(a)","   i. A saturated model"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A8,'Point Grid'!$C$7:$I$7,0))","v":1.75},"  ii. A null model"]
[null," iii. A model that could be used in practice"]

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[6,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","A logistic model was built to predict the probability of a claim being fraudulent. Consider the"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('6'!A1,'Point Grid'!A:A,0))","v":3.5},"predicted probabilities for the 10 claims below to be a representative sample of the total "]
[null,"model."]
[null,null,"Claim Number","Actual Fraud Indicator","Predicted Probability of Fraud"]
[]
[null,null,1,"Y",0.11]
[null,null,2,"N",0.23]
[null,null,3,"N",0.15]
[null,null,4,"N",0.7]
[null,null,5,"Y",0.91]
[null,null,6,"Y",0.3]
[null,null,7,"N",0.11]
[null,null,8,"Y",0.75]
[null,null,9,"N",0.57999999999999996]
[null,null,10,"N",0.27]
[]
["(a)","Construct confusion matrices for discrimination thresholds of 0.50 and 0.25."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A19,'Point Grid'!$C$7:$I$7,0))","v":1}]
[]
[]
["(b)","Plot the Receiver Operating Characteristic (ROC) curve with the discrimination thresholds of 0.50"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A23,'Point Grid'!$C$7:$I$7,0))","v":1.5},"and 0.25. Label each axis and the coordinates and discrimination threshold of each point on the"]
[null,"curve."]
[]
["(c)","Describe an advantage and a disadvantage of selecting a discrimination threshold of 0.25 instead"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A27,'Point Grid'!$C$7:$I$7,0))","v":0.5},"of 0.50."]
[]
[]
["(d)","Describe whether a discrimination threshold of 0.25 or 0.50 is more appropriate for a line of"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A31,'Point Grid'!$C$7:$I$7,0))","v":0.5},"business with low frequency and high severity."]

{"sheet":"7","index":10,"n_rows":18,"n_cols":10,"rows_kept":18,"cut":false,"hidden":false,"merged":[]}
[7,"Incomplete",null,null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","Given the following information:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('7'!A1,'Point Grid'!A:A,0))","v":1.75},"• Allocated loss adjustment expense is 15% of the indemnity amount."]
[null,"• The variance method has been selected to include a risk load in the"]
[null,"   Increased Limits Factors (ILFs) with k = 0.000064 and δ = 0."]
[]
[null,null,"Limit, L","E[X; L]","E[X2; L]"]
[null,null,1000,840,790123]
[null,null,5000,2485,9467456]
[]
["(a)","Calculate the following:"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A12,'Point Grid'!$C$7:$I$7,0))","v":1},"• The risk loads for each limit."]
[null,"• The ILF with and without risk load for the 5,000 limit."]
[]
["(b)","Assuming portfolio weights of 75% for a 1,000 limit and 25% for a 5,000 limit,"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A16,'Point Grid'!$C$7:$I$7,0))","v":0.75},"determine the overall impact on premium by using the ILFs with the risk loads"]
[null,"instead of the ILFs without risk loads."]

{"sheet":"8","index":11,"n_rows":21,"n_cols":9,"rows_kept":21,"cut":false,"hidden":false,"merged":[]}
[8,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","An insurer sells coverage with an attachment point of 5,000 and a layer limit of 5,000."]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('8'!A1,'Point Grid'!A:A,0))","v":1.75},"The following table represents the expected cumulative severity distribution and limited"]
[null,"expected severity for the current year at various limits."]
[null,null,"L","F(L)","E[X; L]"]
[null,null,4545,0.84199999999999997,1807]
[null,null,5000,0.85899999999999999,1875]
[null,null,5500,0.875,1941]
[null,null,9091,0.93799999999999994,2256]
[null,null,10000,0.94699999999999995,2308]
[null,null,11000,0.95399999999999996,2357]
[]
[null,"The actuary expects a 10% severity increase for all claim sizes next year."]
[]
["(a)","Calculate the percent change in frequency of claims in the layer."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A16,'Point Grid'!$C$7:$I$7,0))","v":0.75}]
[]
[]
["(b)","Calculate the percent change in pure premiums in the layer."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A20,'Point Grid'!$C$7:$I$7,0))","v":1}]

{"sheet":"9","index":12,"n_rows":42,"n_cols":11,"rows_kept":42,"cut":false,"hidden":false,"merged":[]}
[9,"Incomplete",null,null,null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","Consider the following claims-made commercial general liability policy:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('9'!A1,'Point Grid'!A:A,0))","v":3.25},"• The insurance contract was originally written January 1, 2011, and has been "]
[null,"   renewed annually as a claims-made policy."]
[null,"• The annual Premises/Operations basic limits manual premium is $200,000 and"]
[null,"   there is no products exposure."]
[null,"• The expected loss ratio is 70%."]
[null,"• Loss experience is evaluated as of June 30, 2016."]
[]
[null,"Claim Number","Policy Year","Indemnity","ALAE"]
[null,1,2011,5000,5000]
[null,2,2012,15000,25000]
[null,3,2013,58000,0]
[null,4,2013,20000,85000]
[null,5,2014,118000,82000]
[null,6,2015,8000,5000]
[]
[null,"You may use the information in the following tables:"]
[null,"Table 13.B","Occurrence","Claims-Made Year in Program"]
[null,"Subline","Policy","Mature","Fourth","Third","Second","First"]
[null,"Prem/Ops",1,1.03,1.1399999999999999,1.2,1.32,1.62]
[]
[null,"Table 13.C","Occurrence","Claims-Made Year in Program"]
[null,"Subline","Policy","Mature","Fourth","Third","Second","First"]
[null,"Prem/Ops",1,0.94,0.91,0.88,0.78,0.67]
[]
[null,"Table 14."]
[null,"Experience",null,"Factors For"]
[null,"Period Year","Subline","Rule 5.B","Rule 5.C"]
[null,"Latest","Prem/Ops",0.90700000000000003,0.92600000000000005]
[null,"2nd Latest","Prem/Ops",0.86399999999999999,0.89200000000000002]
[null,"3rd Latest","Prem/Ops",0.82299999999999995,0.85799999999999998]
[]
[null,"Table 16."]
[null,"CSLC","Credibility","EER","MSL"]
[null,"318,537 – 331,584",0.53,0.93700000000000006,170200]
[null,"331,585 – 345,206",0.54,0.94,173150]
[null,"345,207 – 359,440",0.55000000000000004,0.94399999999999995,178700]
[]
["(a)","Calculate the experience modified premium for the policy effective January 1, 2017."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A41,'Point Grid'!$C$7:$I$7,0))","v":3.25}]

{"sheet":"10","index":13,"n_rows":13,"n_cols":10,"rows_kept":13,"cut":false,"hidden":false,"merged":[[0,6,0,7]]}
[10,"Incomplete",null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","An actuary is evaluating a risk to determine the appropriateness of applying any schedule"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('10'!A1,'Point Grid'!A:A,0))","v":1},"credits or debits. In order to reduce expenses the insured removed a safety program "]
[null,"two years ago and in the next month will be reducing its staff."]
[]
[null,null,null,"Range of Modifications"]
[null,"Risk Characteristic","Description","Credit",null,"Debit"]
[null,"Cooperation","Safety Program",0.1,"to",0.1]
[null,"Employees","Selection, training, supervision, experience",0.05,"to",0.05]
[]
["(a)","Recommend and defend an appropriate schedule modification."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A12,'Point Grid'!$C$7:$I$7,0))","v":1}]

{"sheet":"11","index":14,"n_rows":36,"n_cols":10,"rows_kept":36,"cut":false,"hidden":false,"merged":[[0,6,0,7],[9,2,10,2],[9,1,10,1],[19,6,20,6],[9,6,10,6],[9,5,10,5],[9,4,10,4],[9,3,10,3],[19,1,20,1],[19,2,20,2],[19,3,20,3],[19,4,20,4],[19,5,20,5]]}
[11,"Incomplete",null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","A Workers' Compensation insurer is facing an increasingly competitive market. "]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('11'!A1,'Point Grid'!A:A,0))","v":2.5},"Its management is concerned about customer retention, premium growth, and loss ratio"]
[null,"deterioration. The insurer's actuaries have proposed an updated experience rating plan."]
[null,"After grouping insureds for the purposes of an efficiency test, the projected impact of"]
[null,"this proposal is below (values in thousands):"]
[]
[null,"Current Plan"]
[null,"Quintile","Manual Premium","Loss","Standard Premium","Loss Ratio to Manual","Loss Ratio to Standard"]
[]
[null,"A",4750,1978,3278,0.42,0.6]
[null,"B",4825,2824,4005,0.59,0.71]
[null,"C",4450,2915,4450,0.66,0.66]
[null,"D",4845,3608,5378,0.74,0.67]
[null,"E",4400,3520,5500,0.8,0.64]
[null,"Total",{"$f":"SUM(C12:C16)","v":23270},{"$f":"SUM(D12:D16)","v":14845},{"$f":"SUM(E12:E16)","v":22611},{"$f":"ROUND(D17/C17,2)","v":0.64},{"$f":"ROUND(D17/E17,2)","v":0.66}]
[]
[null,"Proposed Plan"]
[null,"Quintile","Manual Premium","Loss","Standard Premium","Loss Ratio to Manual","Loss Ratio to Standard"]
[]
[null,"A",4220,1494,2068,0.35,0.72]
[null,"B",5100,2922,4233,0.56999999999999995,0.69]
[null,"C",4150,3088,4109,0.74,0.75]
[null,"D",4950,3689,5346,0.75,0.69]
[null,"E",4850,3652,5723,0.75,0.64]
[null,"Total",{"$f":"SUM(C22:C26)","v":23270},{"$f":"SUM(D22:D26)","v":14845},{"$f":"SUM(E22:E26)","v":21479},{"$f":"ROUND(D27/C27,2)","v":0.64},{"$f":"ROUND(D27/E27,2)","v":0.69}]
[]
[null,"It is estimated that the upfront cost to adopt the new rating plan will be $500,000."]
[]
["(a)","Perform an efficiency test and evaluate the proposed experience rating plan relative to"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A31,'Point Grid'!$C$7:$I$7,0))","v":1.5},"the current plan."]
[]
[]
["(b)","In light of management's concerns, evaluate the merits of adopting the new rating plan"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A35,'Point Grid'!$C$7:$I$7,0))","v":1},"versus keeping the current plan, and provide a recommendation."]

{"sheet":"12","index":15,"n_rows":23,"n_cols":9,"rows_kept":23,"cut":false,"hidden":false,"merged":[]}
[12,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","A policy has a flat dollar deductible of M and a maximum payout on a loss by the insurer of N."]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('12'!A1,'Point Grid'!A:A,0))","v":3.25}]
[]
["(a)","Draw a Lee diagram representing the expected amount of loss incurred by this policy."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A6,'Point Grid'!$C$7:$I$7,0))","v":1},"Label the following:"]
[null,"   i. The axes"]
[null,"  ii. The deductible amount"]
[null," iii. The policy limit"]
[null," iv. The expected insured loss."]
[]
["(b)","Assume cumulative losses follow a distribution F(x). Write the formula for covered losses"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A13,'Point Grid'!$C$7:$I$7,0))","v":0.5},"for this policy using:"]
[null,"   i. The layer method"]
[null,"  ii. The size method."]
[]
["(c)","Briefly describe when the layer method may be preferred and when the size method may"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A18,'Point Grid'!$C$7:$I$7,0))","v":0.5},"be preferred."]
[]
[]
["(d)","Use a Lee diagram to demonstrate the consistency test of ILFs."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A22,'Point Grid'!$C$7:$I$7,0))","v":1.25}]

{"sheet":"13","index":16,"n_rows":35,"n_cols":9,"rows_kept":35,"cut":false,"hidden":false,"merged":[[10,2,11,2],[10,4,11,4],[10,3,11,3],[10,1,11,1],[24,2,25,2],[24,1,25,1]]}
[13,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","A risk is written using a balanced retrospective rating plan with the following characteristics:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('13'!A1,'Point Grid'!A:A,0))","v":2.5},"• Losses at the minimum premium = $50,000"]
[null,"• Losses at the maximum premium = $300,000"]
[null,"• Loss conversion factor = 1.05"]
[null,"• e = $10,000"]
[]
[null,"The following table shows actual experience from a representative sample of risks that are"]
[null,"similar to the risk in question:"]
[null,"Risk","Actual Aggregate Loss","Risk","Actual Aggregate Loss"]
[]
[null,1,25000,6,175000]
[null,2,50000,7,200000]
[null,3,100000,8,300000]
[null,4,100000,9,350000]
[null,5,150000,10,550000]
[]
["(a)","Determine the maximum premium that the insured can be charged."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A19,'Point Grid'!$C$7:$I$7,0))","v":2}]
[]
[]
[null,"An actuary has acquired actual aggregate data from a new book consisting of risks within the"]
[null,"same industry. The loss experience of five representative risks from this book is shown below:"]
[null,"Risk","Actual Aggregate Loss"]
[]
[null,1,275000]
[null,2,300000]
[null,3,500000]
[null,4,700000]
[null,5,800000]
[]
["(b)","The actuary proposes to combine the data from the two books, stating that the combined data"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A33,'Point Grid'!$C$7:$I$7,0))","v":0.5},"will result in a more accurate calculation of the insured's retrospective premium."]
[null,"Assess the validity of the actuary's statement."]

{"sheet":"14","index":17,"n_rows":22,"n_cols":9,"rows_kept":22,"cut":false,"hidden":false,"merged":[[4,2,5,2],[4,1,5,1],[4,3,5,3]]}
[14,"Incomplete",null,null,null,null,"Navigator",null,"Solution"]
[]
["Points","A reinsurer has been supplied the following information from a large insurance company:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('14'!A1,'Point Grid'!A:A,0))","v":3}]
[null,"Claim Size Range","Expected Number of Claims","Expected Ultimate Losses ($000)"]
[]
[null,"$0 to $1,000,000",19000,6750500]
[null,"$1,000,001 to $2,000,000",359,525300]
[null,"$2,000,001 to $3,000,000",230,566500]
[null,"$3,000,001 to $4,000,000",147,507700]
[null,"above $4,000,001",264,1650000]
[null,"Total",{"$f":"SUM(C7:C11)","v":20000},{"$f":"SUM(D7:D11)","v":10000000}]
[]
[null,"The reinsurer is entering into an excess of loss contract with the primary insurance company."]
[null,"The reinsurer will pay all losses above a $5,000,000 per claim retention."]
[]
["(a)","Construct a graph of the excess severity function for claim sizes of $1,000,000, $2,000,000,"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A17,'Point Grid'!$C$7:$I$7,0))","v":1.5},"$3,000,000, and $4,000,000."]
[]
[]
["(b)","Calculate the reinsurer's expected losses under the proposed contract."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A21,'Point Grid'!$C$7:$I$7,0))","v":1.5}]

{"sheet":"15","index":18,"n_rows":32,"n_cols":11,"rows_kept":32,"cut":false,"hidden":false,"merged":[[0,7,0,8]]}
[15,"Incomplete",null,null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","The following applies to an incurred loss retrospectively rated policy effective January 1, 2017."]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('15'!A1,'Point Grid'!A:A,0))","v":3.25}]
[null,"Per-occurrence limit",null,null,150000]
[null,"Maximum ratable loss",null,null,600000]
[null,"ULAE as a percentage of loss & ALAE",null,null,0.08]
[null,"Expected Loss & ALAE limited to $150,000",null,null,400000]
[null,"Basic Premium",null,null,375000]
[null,"Tax Rate",null,null,0.03]
[]
[null,"The expected loss & ALAE limited to $150,000 is used to determine the initial premium."]
[null,"Retrospective rating adjustments start at 18 months and occur every twelve months thereafter."]
[]
[null,"The following loss experience occurs during the policy period. All claims are closed at"]
[null,"42 months."]
[null,null,null,null,"Maturity (months)"]
[null,null,null,null,18,30,42]
[null,"Unlimited Incurred Loss & ALAE",null,null,425000,650000,950000]
[null,"Incurred Loss & ALAE Limited to $150,000",null,null,375000,475000,700000]
[null,"Incurred Loss & ALAE Excess of $150,000",null,null,50000,175000,250000]
[]
["(a)","Determine the amount and timing of each incremental cash flow payment made by the insured"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A23,'Point Grid'!$C$7:$I$7,0))","v":1.75},"for this policy beginning with time 0."]
[]
[]
["(b)","Propose and justify an alternative policy that would increase the insured's cash flow benefit"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A27,'Point Grid'!$C$7:$I$7,0))","v":1},"without the insured retaining any additional excess loss risk."]
[]
[]
["(c)","Identify and briefly describe one disadvantage to the insurer of the proposed plan in part (b)"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A31,'Point Grid'!$C$7:$I$7,0))","v":0.5},"above compared to the existing retrospectively rated plan."]

{"sheet":"16","index":19,"n_rows":14,"n_cols":9,"rows_kept":14,"cut":false,"hidden":false,"merged":[[0,5,0,6]]}
[16,"Incomplete",null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","An insurance company generally uses two different methods to price excess layer"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('16'!A1,'Point Grid'!A:A,0))","v":1.75},"insurance contracts:"]
[null,"   • Empirical construction of Table M; or"]
[null,"   • Approximating the distribution of aggregate losses with a continuous approximation "]
[null,"      model."]
[]
["(a)","Briefly describe two potential disadvantages of using a continuous approximation model."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A9,'Point Grid'!$C$7:$I$7,0))","v":0.5}]
[]
[]
["(b)","Fully describe the process of constructing an empirical Table M and estimating the "]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A13,'Point Grid'!$C$7:$I$7,0))","v":1.25},"aggregate loss cost of an excess layer using the table."]

{"sheet":"17","index":20,"n_rows":34,"n_cols":11,"rows_kept":34,"cut":false,"hidden":false,"merged":[[0,7,0,8]]}
[17,"Incomplete",null,null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","A Workers' Compensation policy has the following limits and expected losses:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('17'!A1,'Point Grid'!A:A,0))","v":2}]
[null,"Per-Occurrence Limit",null,null,300000]
[null,"Expected Unlimited Aggregate Losses",null,null,700000]
[null,"Expected Limited Aggregate losses",null,null,500000]
[null,"Aggregate Deductible Limit",null,null,800000]
[null,"State Hazard Group Differential",null,null,0.95]
[]
[null,"Table of Expected Loss Groups"]
[null,"Expected Loss Group","Range of Values"]
[null,31,"630,000 – 720,000"]
[null,30,"720,001 – 830,000"]
[null,29,"830,001 – 990,000"]
[null,28,"990,001 – 1,180,000"]
[null,27,"1,180,001 – 1,415,000"]
[null,26,"1,415,001 – 1,744,000"]
[]
[null,"Table of Insurance Charges"]
[null,null,"Expected Loss Group"]
[null,"Entry Ratio",31,30,29,28,27,26]
[null,0.43,0.5675,0.56259999999999999,0.55840000000000001,0.55430000000000001,0.54969999999999997,0.54559999999999997]
[null,0.6,0.48649999999999999,0.47989999999999999,0.47370000000000001,0.46760000000000002,0.46129999999999999,0.45529999999999998]
[null,1.1399999999999999,0.26579999999999998,0.25519999999999998,0.24479999999999999,0.2341,0.22339999999999999,0.21279999999999999]
[null,1.6,0.16439999999999999,0.1537,0.13669999999999999,0.13220000000000001,0.1215,0.11070000000000001]
[null,2.67,8.2199999999999995E-2,7.6899999999999996E-2,6.8400000000000002E-2,6.6100000000000006E-2,6.08E-2,5.5399999999999998E-2]
[]
["(a)","Calculate the total expected loss cost for this policy using the Insurance Charge Reflecting"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A29,'Point Grid'!$C$7:$I$7,0))","v":1.5},"Loss Limitation (ICRLL) adjustment procedure."]
[]
[]
["(b)","Describe how the ICRLL procedure is used to adjust expected losses for a Workers'"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A33,'Point Grid'!$C$7:$I$7,0))","v":0.5},"Compensation policy."]

{"sheet":"18","index":21,"n_rows":35,"n_cols":14,"rows_kept":35,"cut":false,"hidden":false,"merged":[[0,9,0,11],[8,2,8,5],[9,2,9,5],[3,1,4,1],[3,2,4,5],[5,2,5,5],[6,2,6,5],[7,2,7,5]]}
[18,"Incomplete",null,null,null,null,null,null,null,"Navigator",null,null,null,"Solution"]
[]
["Points","A homogeneous group of property risks exhibit the following risk profile:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('18'!A1,'Point Grid'!A:A,0))","v":3.25},"Exposure Distribution","Losses as a percent of the Maximum Possible Loss (MPL)"]
[]
[null,0.8,0]
[null,0.06,0.25]
[null,0.08,0.5]
[null,0.04,0.75]
[null,0.02,1]
[]
["(a)","Plot the exposure curve, G(x). Label the axes and the points on the curve."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A12,'Point Grid'!$C$7:$I$7,0))","v":2}]
[]
[]
["(b)","Determine the parameters b and g for the 2-parameter MBBEFD distribution that fits this "]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A16,'Point Grid'!$C$7:$I$7,0))","v":0.75},"exposure curve using the following information:"]
[]
[null,"Calculated value of parameter b"]
[null,null,"μ"]
[null,"Parameter g",0.4,0.42499999999999999,0.45,0.47499999999999998,0.5,0.52500000000000002,0.55000000000000004,0.57499999999999996,0.6]
[null,50,2.3E-3,1.6999999999999999E-3,1.2999999999999999E-3,8.9999999999999998E-4,5.0000000000000001E-4,2.9999999999999997E-4,2.0000000000000001E-4,1E-4,1E-4]
[null,25,9.5999999999999992E-3,7.3000000000000001E-3,5.4999999999999997E-3,4.1000000000000003E-3,2.2000000000000001E-3,1.5E-3,1E-3,5.9999999999999995E-4,4.0000000000000002E-4]
[null,16.7,2.3900000000000001E-2,1.8100000000000002E-2,1.37E-2,1.03E-2,5.7000000000000002E-3,3.8999999999999998E-3,2.5999999999999999E-3,1.6999999999999999E-3,1E-3]
[null,12.5,4.8300000000000003E-2,3.6499999999999998E-2,2.76E-2,2.0899999999999998E-2,1.18E-2,8.0999999999999996E-3,5.4999999999999997E-3,3.5999999999999999E-3,2.3E-3]
[null,10,8.77E-2,6.59E-2,4.9700000000000001E-2,3.7499999999999999E-2,2.12E-2,1.47E-2,0.01,6.7000000000000002E-3,4.4000000000000003E-3]
[null,8.3000000000000007,0.14979999999999999,0.1116,8.3599999999999994E-2,6.2799999999999995E-2,3.5400000000000001E-2,2.4500000000000001E-2,1.6799999999999999E-2,1.1299999999999999E-2,7.4999999999999997E-3]
[null,7.1,0.247,0.1817,0.13469999999999999,0.1004,5.6099999999999997E-2,3.8899999999999997E-2,2.6700000000000002E-2,1.8100000000000002E-2,1.21E-2]
[null,6.3,0.4,0.28920000000000001,0.21160000000000001,0.15609999999999999,8.6099999999999996E-2,5.9400000000000001E-2,4.0800000000000003E-2,2.7699999999999999E-2,1.8499999999999999E-2]
[null,5.6,6.4460000000000003E-2,0.45569999999999999,0.32769999999999999,0.23849999999999999,0.12909999999999999,8.8499999999999995E-2,6.0499999999999998E-2,4.1099999999999998E-2,2.76E-2]
[null,5,1.0468999999999999,0.71899999999999997,0.55049999999999999,0.36159999999999998,0.191,0.12970000000000001,8.8200000000000001E-2,5.9700000000000003E-2,0.04]
[]
["(c)","A reinsurer uses this exposure curve to price a property excess of loss treaty where the "]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A33,'Point Grid'!$C$7:$I$7,0))","v":0.5},"underlying limit is $100 million. Calculate the proportion of total losses in the layer"]
[null,"$25 million excess of $25 million."]

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[19,"Incomplete",null,null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","A primary insurer wants to institute a swing plan with the following terms:"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('19'!A1,'Point Grid'!A:A,0))","v":3},"   • Layer: $500,000 xs $500,000 per claim"]
[null,"   • Premium = Layer Losses * 1.3"]
[null,"   •  Minimum Premium = $100,000"]
[null,"   • Maximum Premium = $325,000"]
[]
[null,"The reinsurer has analyzed the insurer's experience. All claims are closed and trend has"]
[null,"been applied."]
[null,"Accident Year","On-Level Premium","Claim #1","Claim #2","Claim #3","Claim #4"]
[]
[null,2012,400000,465899,230567,512017]
[null,2013,420000,240785,125766,80154,470666]
[null,2014,1200000,704895,106528]
[null,2015,2000000,45988,349145,376011,1246852]
[null,2016,2200000,437100,654158,250000,156750]
[]
["(a)","Calculate the expected loss ratio for the reinsurer based on the empirical distribution"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A19,'Point Grid'!$C$7:$I$7,0))","v":1.5},"model using equal weights for each year."]
[]
[]
["(b)","Determine if an empirical distribution model is appropriate for this account and give one"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A23,'Point Grid'!$C$7:$I$7,0))","v":0.5},"reason in support of your answer."]
[]
[]
["(c)","The use of a collective risk model is an alternative approach. Explain two concerns"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A27,'Point Grid'!$C$7:$I$7,0))","v":1},"when using this type of model."]

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[20,"Incomplete",null,null,null,"Navigator",null,null,"Solution"]
[]
["Points","An earthquake model produces the following damage function for a $1,000,000 home in"]
[{"$f":"INDEX('Point Grid'!B:B,MATCH('20'!A1,'Point Grid'!A:A,0))","v":2.5},"California:"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,"The probability of an event occurring with a given intensity is:"]
[null,null,"Intensity","Probability"]
[null,null,3.5,0.1]
[null,null,6,0.05]
[null,null,8.5,0.02]
[]
["(a)","Identify what A, B, and C represent in the above graph."]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A29,'Point Grid'!$C$7:$I$7,0))","v":0.75}]
[]
[]
["(b)","Determine the premium for this home given the following information:"]
[{"$f":"INDEX('Point Grid'!$C$8:$I$29,MATCH($A$1,'Point Grid'!$A$8:$A$29,0),MATCH(A33,'Point Grid'!$C$7:$I$7,0))","v":1.75},"• The insurer's expense load is 20% of premium"]
[null,"• The risk load is set to 8% of the standard deviation of the loss."]