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Actuarial IQ (Information Quality)

The 2008 seminar led by Virginia Prevosto explored the critical importance of data quality in casualty loss reserving. Panelists, including experts Louise Francis and Aleksey Popelyukhin, shared real-life horror stories showcasing the unbearable cost of poor data practices. These included severe under-reserving due to digit limitations, duplications leading to overpayments, and absent fields causing critical claim mismanagement. The session concluded with actionable solutions and techniques to improve data quality, empowering actuaries to mitigate risks against financial losses.

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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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