A Guide to the Value of Reliable Data in Insurance

Table of Contents Intelligent  Business  Strategies  A Guide to the Value of Reliable Data in Insurance By Mike Ferguson Intelligent Business Strategies September 2009 Prepared for: Table of Contents INTRODUCTION.............................................................................................................................. 3  What is Data Reliability? ............................................................................................................ 4  IMPACT OF UNRELIABLE DATA ON YOUR BUSINESS ............................................................... 5  Key Insurance Processes .......................................................................................................... 5  Unreliable Data in Marketing ...................................................................................................... 7  Unreliable Data in Underwriting ................................................................................................. 8  Unreliable Data in Risk Management ......................................................................................... 9  Unreliable Data in Re-Insurance .............................................................................................. 10  Unreliable Data in Reserving and Investment .......................................................................... 10  Unreliable Data in Claims ......................................................................................................... 11  Unreliable Data in Finance and Administration ........................................................................ 12  IS YOUR DATA OUT OF CONTROL?........................................................................................... 13  GETTING STARTED WITH DATA RELIABILITY .......................................................................... 14  Enterprise data governance ..................................................................................................... 14  The Data Quality Firewall ......................................................................................................... 16  CONCLUSIONS ............................................................................................................................. 17  2 INTRODUCTION The underwriting and Within any insurance company, data and documents associated with customers, claims processes insured risks and policies, flow through the organisation as it processes quote are at the heart of requests, renewals, premium payments (revenue), re-insurance premiums and iInsurance claims. The main operational processes handling this data are underwriting, re- operations insurance, policy administration and claims. Insurance is also heavily dependent on data in analytical processes that help it manage risk, estimate claims and manage its product portfolios. Generally speaking, insurance revenue comes from actual net premiums, investment income (accrued from investing premium income in the financial markets) and re-insurance claims. Insurance costs on the other hand come from incurred claims, claims incurred but not reported (IBNR), third party fees (e.g. broker commissions and third party claims assessment fees), re-insurance premiums and operating expenses (e.g. salaries, buildings, etc.) While calculating profitability is not straight forward in the insurance business, most insurance companies strive to run as efficiently as possible with minimal cost at the best possible loss ratios across their entire portfolio. In addition they want to maintain high levels of customer service and pursue growth by continuing to attract new low risk business. A key concern is keeping costs to a minimum. This means: • Avoiding the underwriting of high risks by ensuring underwriters have access to risk factor data, incurred claims, and claims IBNR data during new business quote and renewal processing • Managing risks by undertaking risk inspections both before rating (pricing) and after writing business if deemed necessary • Continually strengthening rating rules through claims analysis • Striking good re-insurance deals with reputable re-insurers as early as possible while re-insurance capacity remains in the market • Reducing the cost of claims where possible • Managing portfolios by monitoring actual claims versus ultimate claims and loss ratios In a 2009 survey of 403 insurance companies in 39 countries by the Underwriting Centre for the Study of Financial Innovation (CSFI)1, the top ten risk areas facing insurers in this tough economy were: 1. Investment performance 2. Equity markets 3. Capital availability 4. Macro-economic trends 1 Insurance Banana Skins 2009 - The CSFI survey of the risks facing insurers Underwriting Centre for the Study of Financial Innovation (CSFI) in association with PriceWaterhouseCoopers 5. Too much regulation 6. Risk management techniques 7. Reinsurance security 8. Complex instruments 9. Actuarial assumptions 10. Long tail liabilities These risk areas show a clear concern about investment income and the ability to recover losses through re-insurance claims. Both of these issues stem from the current crisis in the financial markets. They also indicate that insurers that depend on the increasing flow of premiums to cover both claims and operating expense may find it difficult in a tough economy where investment income is low and growth is low. Efficient, low cost operational processes and strong analytical processes are therefore fundamental to performance. In these tough times, insurance companies need to attract the right customers, price correctly, write the right business, decline high-risk business, mitigate risk, reserve correctly while maintaining cash flow, manage outstanding claims and get the best re-insurance deals and minimise operating expense. Just imagine then, the impact on core insurance operational and analytical processes if the data flowing through these processes is unreliable. WHAT IS DATA RELIABILITY? Reliable data is data Data reliability is about guaranteeing that core insurance data is secure, correct that is correct, and complete wherever it is used throughout the enterprise. It is also about the complete and clearly meaning of that data being clearly understood everywhere it is used. Even if understood data is correct, ambiguous naming may well render it unusable simply because business users do not understand what the data means. Reliable, trustworthy data is therefore dependent on two things being in place for every insurance Reliable data depends data item in use wherever that data is used. These are: on high quality data and metadata • Data Quality – i.e. actual data values being correct and complete • Metadata Quality – i.e. commonly understood data definitions Investment in people, Only when data is trusted can it be used in confidence in all insurance processes and operational and analytical process activities. Guaranteeing reliable data technology are therefore is an obligation all companies should strive for. This means required to govern companies need to invest in the necessary people, processes and technology data on an enterprise required to govern their data on an enterprise wide basis. wide basis This paper examines the impact of unreliable data on insurance companies. It then defines the requirements needed to guarantee data reliability in insurance Reliable data helps and offers a practical approach to creating and governing that data.