Question 7: How many “True Negatives” is ADDS predicting?

“Milo Loner (ML)” is a bank that gives out personal loans to customers. Currently, the process of document verification is done manually by verifying the scores from 2 variables “Earnings” and “Tenure”. The aim of inspection process is to identify customers who will default on a loan (considered as the negative class). From historical experience, manual inspection correctly identifies 85% of loan defaulters in any given batch of only defaulter customers.
The top brass at ML, Dr. Milo, has embarked on a journey in business analytics. Hence, he has decided to replace the manual inspection with an Automatic Defaulter Detection System (ADDS). This ADDS runs a trained logistic model in the background which classifies an applicant as “Defaulter” or “Non-Defaulter” based on the scores for “Earnings” and “Tenure”. To test the ADDS, a sample of 200 applications are taken. The sample is imbalanced and only 30% of the sample applicants were non-defaulters. The sample applications are passed through the ADDS, and the system identifies 70% of the actual non-defaulters as defaulters and 10% of actual defaulters as non-defaulters.
Using this information, answer the given subquestions.

How many “True Negatives” is ADDS predicting?