Use Case

Data Enhancement

Problem Statement

  • An insurance client had a schedule of properties that wanted insurance
  • The schedule was wrong and incomplete in crucial ways
  • Some buildings were missing the year build field making it impossible to tell which construction standard was used
  • Other buildings were priced incorrectly leaving a gap for a lawsuit if there is a claim

DPS Solution

  • The contents of the schedule were used to model each variable.
  • Where Sue predicted a different value, the properties were inspected.
  • Where years were missing, Sue predicted them.

Metrics Gathered

  • None were needed.
  • The data in the schedule had hidden relationships in it which DPS was able to extract. Where humans prefer direct cause and effect, DPS is able to look at the total information as a whole and see anomalies

Success and Improvements

  • Year built was predicted within 10% of the actual year built on average
  • 3 properties flagged for value differences were incorrect

Implementation Timeline

  • 10 days of a computer science intern

DPS Similar Possible Use Cases

Fraud detection – any payout or insurance rate which shows as anomalous by DPS has a strong chance of being fraudulent, including insurance, telecoms, utility billing…

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