Bad debts
When offering credit terms to customers, there is always a risk that the customer does not pay their debt. In the finance world, this is known as "bad debts".
As we have already recorded sales, we need to record the loss of sales now, as we never received the payment.
This affects both the income statement and the balance sheet. In the income statement, we record a negative value in the sales for the month we write off the debt. In the balance sheet, we need to reduce our debtor’s asset.
The following variables have been defined for January:
debtors_jan = 1500
In February, we received news that a customer has gone into liquidation. This customer currently owes 500 USD.
We expect to recover 70% of this amount; the rest has to be written off as bad debts.
This exercise is part of the course
Financial Forecasting in Python
Exercise instructions
- Calculate the bad debts amount for February.
- Reduce the debtors' value by the amount in
bad_debts_feb. - Print the bad debts for February.
- Print the new debtors' value
debtors_feb.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the bad debts for February
bad_debts_feb = ____*____
# Calculate the feb debtors amount
debtors_feb = (____ - ____)
# Print the debtors for January and the bad debts and the debtors for February
print("The debtors are {} in January, {} in February. February's bad debts are {} USD.".format(____, ____, ____))