The credit card company, VISCARD, with 2 million customers, has designed a promotional offer and wants to communicate this to customers via direct mail so that they can enroll into the program. It costs $1 to mail a flyer, and there is only $30,000 budgeted to reach out to customers. You are assigned the task of identifying a cohort of 30,000 customers that are most likely to respond to the marketing promotion. You are given historic enrollment data with 100 variables. This is a classic binary classification problem: A customer enrolls or ignores the offer and does not enroll. After a few weeks, you have built a complicated model that turns out to be very accurate but does not give you any insight into which variables are associated with high enrollment probability. Should this model be used for this purpose? Are there any benefits in understanding how the model works, or is it just important to know that it works? Explain and justify your answer.