Yesterday we created features such as:

ClaimsLast12Months
ComplaintsLast90Days
PreviousRenewalCount
PremiumIncreasePercent

But creating a correct feature once is not enough.

Suppose training uses:

Complaints during the previous 90 days

while production uses:

All complaints ever recorde

The application will still run, but the model will receive data different from what it learned.

Similarly:

Training PolicyType: Health, Motor, Life
Production PolicyType: HEALTH, MOTOR, LIFE, Travel

These differences can silently reduce prediction quality.

Today’s principle:

An AI model is useful only when its complete data and transformation pipeline can be repeated consistently.