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CMA Intermediate · Financial Management and Business Data Analytics · Introduction to Data Science for Business Decision-making

A bank in Mumbai builds a model using past borrower income, existing debt and repayment history to estimate the probability that a new loan applicant will default. This is an example of:

This is predictive analytics, because the bank uses historical borrower data to estimate the probability of a future event, namely loan default. Descriptive analytics would only summarise past repayments, and prescriptive analytics would recommend the specific action to take.

  1. ADescriptive analytics, as it uses historical data
  2. BPredictive analytics, as it estimates a future outcomeCorrect
  3. CPrescriptive analytics, as it supports a lending decision
  4. DData visualisation, as it presents borrower information

Explanation

The model uses historical data to estimate the probability of a future event, default. That is the defining feature of predictive analytics. Option A is wrong because merely using historical data does not make it descriptive; descriptive work summarises the past without estimating a future outcome.

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