In python jupyter notebook and word answers

In python jupyter notebook and word answers

Universal Bank Home Equity Loan

Universal Bank is located in a northeastern US city; its business is largely focused on the city and its surrounding metropolitan area. It has roughly 80,000 checking account customers and it would like to expand its business by offering these customers personal loans secured by home equity. On average, the stream of interest payments generated by a personal loan amounts to roughly $1000 per customer.

Under the direction and management of a marketing consultant, the bank has done an experiment with 4000 checking account customers, attempting to draw them into applying for the loan. It sent them an offer for “no-fee-anywhere” ATM services for one year, coupled with a personal line of credit secured by home equity. Those accepting the offer don’t get the ATM deal unless they also supply some details relative to their need/qualification for the loan, then the bank will extend (or not extend) the loan as a line of credit. The customer is not obligated to use the loan.

Costs: The promotion costs $1 apiece to mail, but this is not the biggest cost driver. The ATM deal, when you account for the fees to other banks that Universal must cover, plus the setup and transaction costs, costs about $105 per customer. To this must be added the processing/review costs of the loan information, about $35 per customer.

The ATM deal is a powerful offer – about 50% of the offer recipients accept it.  However, only about 19% of those customers end up using the personal loan (380 out of the original mailing, to be exact).


4000 mailed $1 apiece or $4000

2000 accept ATM offer $140 per customer, or $280,000

So the total cost = $4000 + $280,000 = $284,000


380 take the loan, $1000 apiece or $380,000

PROFIT = $380,000 – $284,000 = $96,000

So the promotion is profitable – if applied to all 80,000 customers the results would be


$80,000 mailing

$140 * 40,000 cost of fulfilling offer = $5,600,000

So the total cost = $80,000 + $5600,000 = $5,680,000


0.19 * 40,000 * $1000 = $7.60 million

Management is anxious about the costs associated with the project. Someone points out that the profit is projected a $1.92 million, but there is considerable uncertainty about this. If customers fall short on either the rate at which they accept the loan offer, or the average size of the loan, the profit could shift to loss.

The marketing consultant suggests building a predictive model, to better target the offer at those likely to accept it. While overall revenue might be less, the profit would be greater and the risk reduced.


You are the analyst working with the marketing consultant, tasked to proceed with this project. Conduct the analysis as you think best, and present a recommendation that includes a technical discussion that would allow another analysts later to understand what was done and replicate or modify it, as well as a management discussion of the financial implications.

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