Dale Pryor is the management accountant at Juniper Bank, where the data science department is leading an initiative to predict whether loans will default or repay. The default rate in the training set is 20%. After building a model on the training set that predicts whether a loan will default or repay, the data scientist applies it to the validation set of 4,000 observations to evaluate its performance.

Requirement 1. Help the data scientist complete the confusion matrixes below for different model thresholds. Start by completing the confusion matrix for the cutoff point 0.40. Confusion Matrix (0.40) Predicted Outcomes Default Repay Total Actual Default 380 420 800 Outcomes Repay 2,220 980 3,200 Total 2,600 1400 4,000 Complete the confusion matrix for the cutoff point 0.55.. . . Confusion Matrix (0.55) Predicted Outcomes Default Repay Total Actual Default 360 440 800 Outcomes Repay 1440 1760 3,200 Total 1800 2,200 4000 Requirement 2. Assume that Juniper Bank has $1,000 to invest in each loan of the validation sample. If Juniper Bank does not invest in a loan, it keeps the money in a risk-free investment at 2% a year for 3 years (ignore the time value of money). If Juniper invests in a loan that eventually repays, it receives 10% a year for 3 years. If Juniper invests in a loan that eventually defaults, Juniper loses 65% of the amount of the loan. Fill in the payoff matrix below. Which model threshold should Dale and the data scientist use? Start by construting the payroll matrix. (Enter a loss amount with a minus sign orStart by construting the payroll matrix. (Enter a loss amount with a minus sign or parentheses.) Payoff Matrix Predicted Outcomes Default Repay (Do Not Invest in Loan) (Invest in Loan) Actual Default Outcomes Repay Calculate the payoff at each cutoff probability. Total payoff at cutoff 0.40 = Total payoff at cutoff 0.55 =