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A Guide to the Value of Reliable Data in Insurance

By Mike Ferguson

Introduction

Within any insurance company, data and documents associated with customers, insured risks and policies, flow through the organisation as it processes quote requests, renewals, premium payments (revenue), re-insurance premiums and claims. The main operational processes handling this data are underwriting, reinsurance, 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), 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
  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.

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