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ACCA Strategic Professional · Strategic Business Leader · Enabling success: disruptive technologies

Mistral Insurance analysed claims data and found that policyholders who buy a particular brand of garden furniture claim more often for theft. Management proposes raising premiums for all buyers of that brand. A data scientist warns that the proposal rests on a weakness of big data analytics. Which is the most relevant warning?

The key warning is that correlation does not prove causation. Buying the furniture brand may simply coincide with another factor, such as location or wealth, that drives theft claims. Raising premiums on that basis could be inaccurate and unfair, so the pattern needs further investigation before it is used.

  1. ACorrelation found in the data may not indicate causation, so the pricing could be unfair and inaccurateCorrect
  2. BThe data is too low in volume to produce any pattern
  3. CAnalytics can only be applied to structured data, so the finding is invalid
  4. DReal-time processing is impossible, so the result is out of date

Explanation

A statistical association between furniture brand and theft claims does not show the brand causes theft; a hidden factor such as neighbourhood or income may explain it. Acting on correlation alone risks discriminatory and inaccurate pricing. The other options are false: the scenario implies enough data, analytics handles unstructured data, and timeliness is not the issue.

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