Risk assessment. Some statistics and data are necessary to assess the default risk of new customers; these are the standard deviation of late payments, unusual late payments, and probability of late payments. An unusual late payment is detected when a payment is presented with a very high number of days late. (number of days after the payment due date), and the number of days in arrears in the previous period and next is minor; graphically, you would see a peak. For this exercise, we need the anomaly with the peak higher. 

Additionally, the probability of late payments must be calculated based on the late payments of other products that the customer already owns for a number of time periods. Entry. For both the standard deviation calculation and unusual late payment, you will receive an array of integers with the number of days of delay in payments. Each position in the arrangement represents a payment period, usually a month, 0 means that there was no payment period. late payment, 1 means one-day late payment, and so on.

To calculate the probability of late payments, you will receive a two-dimensional array, where each row represents the late payments of a product for different periods of time (columns). the expected output for standard deviation The expected output for the standard deviation is a value of type double, which represents the result of the calculation of the standard deviation. (The population standard deviation). Expected departure for unusual late payment The expected output is a data type int that represents the index in the input array where:

Find the anomalous payment with the highest peak. Expected output for calculation of probability of late payments The expected output for the probability of late payments is an array of doubles where each position represents the probability of late payment for the period (index) of the different products (rows of the array).