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Actuarial IQ (Information Quality)

Actuarial IQ (Information Quality). Casualty Loss Reserve Seminar 2008. Raising Your Actuarial IQ. Moderator: Virginia Prevosto , Vice President ISO Panelists: Louise Francis , Consulting Principal and Founder , Francis Analytics and Actuarial Data Mining Inc. Aleksey Popelyukhin ,

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Actuarial IQ (Information Quality)

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  1. Actuarial IQ(Information Quality) Casualty Loss Reserve Seminar 2008

  2. Raising Your Actuarial IQ • Moderator: • Virginia Prevosto, • Vice President • ISO • Panelists: • Louise Francis, • Consulting Principal and Founder , • Francis Analytics and Actuarial Data Mining Inc. • Aleksey Popelyukhin, • Vice President, Information Systems

  3. Raising Your Actuarial IQ • Problem: unbearable COST of Data Quality. (real life horror stories) • by Alex Popelyukhin, • Investigation: why ACTUARIES? (the case for Data Quality) • by Alex Popelyukhin, • Research: Actuarial IQ paper (Working Party product) • by Louise Fransis, • Solution: What can YOU do today? (showcase of available Information Quality techniques) • by Alex Popelyukhin,

  4. Real-life example 1data transfer • Situation: • $150,000,000.00 loss gets reported by TPA • Insurer’s system accepts only 8 digits numbers for Incurred amounts • 150M is recorded as 50M • Result: • Severe under-reserving • Company in run-off is now

  5. Real-life example 2duplicates • Situation: • TPA pays losses, client receives Recoveries • Incorrectly performed join • Every claim with multiple recoveries was accounted for several times • Result: • Severe overpayment by Insurer • Company is in run-off now

  6. Real-life example 3absent fields • Situation: • A construction damaged foundations of quite a few houses • Multiple GL claims constituted the same occurrence – subject to a limit • TPA bordereaux didn’t have “Location” field • Result: • Severe overpayment by Insurer • Company is in run-off now

  7. Real-life example 4“statistical” payments • Situation: • Claim files moved to new TPA • New TPA tracks only new payments on open claims (and adds to a total of closed before) • Old payments on reopened claims are double-counted • Result: • Severe overpayment by Insurer • Company is in run-off now

  8. Real-life example 5classification • Situation: • Treaty has a deductible on Med-only claims • TPA doesn’t support Med-only indicator • TPA lumps Med and Ind payments into single NetLoss field • Result: • Severe overpayment by Insurer • Company is in run-off now

  9. Real-life example 6perfect data • Situation: • TPA data do not contain any errors • Except… • …their accounting department continues to bill a Carrier for a non-renewed account • Result: • Severe overpayment by Insurer • Company is in run-off now

  10. Avalanche of ERRORS Within Loss Run Across Multiple Loss Runs Across MultipleLoss Runs for Multiple Policies

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