Managing an insurance portfolio. for Profit and Growth
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1 Managing an insurance portfolio for Profit and Growth Harold Clarke + Keith Chandler Bacon & Woodrow SEUGI 15 Madrid 1
2 Overview Background Portfolio Control Process Approach + results of new research + demonstration Conclusion 2
3 Background About B&W Major UK actuarial consultancy Non-life and life insurance, pensions... World-wide network (Woodrow Milliman) European focus - offices in Paris and Cologne Non-life emphasis includes pricing, claims reserving, M&A 3
4 Background The Business Problem Personal lines: motor + home insurance Quantify factors determining performance Costs: claims + expenses Customer behaviour Test decisions based on those factors Review performance in retrospect 4
5 Background Results of Market Research + recent experience UK insurers addressing business problem - but piecemeal Requirements vary significantly across insurers Package software inappropriate Clients do not want black box solutions Solution: Tailored software clients can develop further themselves 5
6 Background Why SAS Software? Provides required functionality Data handling, statistical analyses, quality presentation Complex processing on large data volumes is fast Already used by many insurers worldwide Platform independent Emphasis on research - same as Bacon & Woodrow 6
7 Policy+Claim Transactions Statistical Data 3rd Party Extracts Enhancement Data Data Quality Review Data Exploration Analysis (claim factors, customer retention ) "Cost-plus" prices Portfolio Modelling 7
8 Portfolio modelling New Business Renewals Portfolio Claims Expenses Portfolio + Costs Portfolio Drill Down Financial Results 8
9 Benefits from 3rd party data Competitor prices - monitor price v market Geographic risk costs - supplements own limited experience Eg subsidence and flood for home insurance Socio-economic data - customer lifestyle indicators 9
10 Benefits from Data Quality Review + Exploring Data Data Quality Review Identifies reliable data - usually not fully known Often reveals unknown features of data Data Exploration (eg via SAS EIS) Extends Data Quality Review Key characteristics of portfolio identified in disciplined way Concentrations of risks, claims Inter-relationships within risk, claim data 10
11 Exploring Data 11
12 Quality statistical analysis is essential for pricing 100k customers: all choose cheapest insurer Exposure Premium Loss Ratio Insurer A 35% 51% 114% (prices using 1-ways) Insurer B 65% 49% 97% (prices using GENMOD) Total 100% 100% 12
13 What is quality analysis? - results of B&W research Statistical techniques are only one aspect Business perspectives should determine analyses Quality of analysis more important than large portfolio Crude model + large portfolio = inaccurate analysis results Quality model + smaller portfolio = accurate analysis results 13
14 Forecasts: on average assumptions - misleading 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% Price change by Age Varying Price change by Age Constant 0.0% -2.0% -4.0% Premium Growth Exposure Growth Profit % to premium -6.0% 14
15 Benefits of policy by policy forecasting Forecast reflects decisions in detail Avoids on average assumptions Enhanced understanding of financial impact of all inputs Can compare forecast v actual in detail (after the event) Time is right to introduce 15
16 Forecasting process SAS Excel Policy+ Analyses Future Claim (Claim freq/severity Assumptions Transactions Retentions) (inc Prices) Forecasting Program Transaction Detail Revenue Account 16
17 Forecasting PORTFOLIO CONTROL with Forecast Assumptions External Factors Portfolio Characteristics Forecast Results Competitor A price New business Revenue account Competitor B price Retentions Detail Inflation, Seasonality.. Frequency (claim type 1) Variance Analysis (claim type 1) Inflation, Seasonality.. Frequency (claim type 2) (claim type 2) Severity (claim type 1) Decisions Severity (claim type 2) Set Up Own pricing Sample Claim Cost Timescales Expenses Development patterns NCD 17
18 Analysing actual v forecast Company A = 10% Company A = 5% PROFIT & LOSS/ Difference (10-5) REVENUE ACCOUNT WRITTEN EXPOSURE 6,343 6, WRITTEN PREMIUM 2,365,577 2,360,580 4,998 EARNED PREMIUM 2,924,313 2,921,181 3,131 INCURRED CLAIMS 2,296,139 2,294,023 2,116 WRITTEN EXPENSES 340, ,730 1,095 WRITTEN COMMISSION 189, , UNDERWRITING PROFIT 98,103 98, INVESTMENT INCOME 139, , INVESTMENT GAINS 139, , PROFIT BEFORE TAX 377, , TAX 124, , PROFIT AFTER TAX 252, ,
19 Conclusion Coordinated decision making Faster + more reliable response to market changes Requires an Integrated Process Policy+Claim Transactions Statistical Data 3rd Party Extracts Enhancement Data Data Quality Review Data Exploration Analysis (claim factors, customer retention ) "Cost-plus" prices Portfolio Modelling 19
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