Personal Loan Campaign.

Background and Context.

AllLife Bank is a US bank that has a growing customer base. The majority of these customers are liability customers (depositors) with varying sizes of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).

A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio.

Objective.

  1. To predict whether a liability customer will buy a personal loan or not.

  2. Which variables are most significant.

  3. Which segment of customers should be targeted more.

Data Dictionary

  • ID: Customer ID

  • Age: Customer’s age in completed years

  • Experience: #years of professional experience

  • Income: Annual income of the customer (in thousand dollars)

  • ZIP Code: Home Address ZIP code.

  • Family: the Family size of the customer

  • CCAvg: Average spending on credit cards per month (in thousand dollars)

  • Education: Education Level. 1: Undergrad; 2: Graduate;3: Advanced/Professional

  • Mortgage: Value of house mortgage if any. (in thousand dollars)

  • Personal_Loan: Did this customer accept the personal loan offered in the last campaign? (0: No, 1: Yes)

  • Securities_Account: Does the customer have securities account with the bank? (0: No, 1: Yes)

  • CD_Account: Does the customer have a certificate of deposit (CD) account with the bank? (0: No, 1: Yes)

  • Online: Do customers use internet banking facilities? (0: No, 1: Yes)

  • CreditCard: Does the customer use a credit card issued by any other Bank (excluding All life Bank)? (0: No, 1: Yes)

Data Transformation:

The data transformations in this project include:

  1. Dropping Irrelevant Columns: The "ID" column was removed from the dataset as it has no impact on the model's prediction goals​.

  2. Error Correction in Experience: Negative values in the "Experience" column, likely due to data entry errors, were addressed. These values were adjusted or removed as they could skew results​.

  3. Handling Outliers: Outliers in columns such as "Income" and "CCAvg" were noted, especially given large gaps in high-end values compared to the 75th percentile, suggesting potential outliers were likely removed or capped to improve the model's performance​.

  4. Categorical Encoding: Variables such as "Education" were transformed into categorical formats to enable model processing.

  5. Balancing the Dataset: Given the imbalance in target variables like "Personal_Loan," where far fewer customers had taken a loan compared to those who had not, the dataset may have been balanced either through under-sampling the majority class or over-sampling the minority to improve model training​.

Business Insights and Recommendations

To optimize business opportunities, the bank should target higher-income customers earning above $116,000 and focus on offering personal loans to those with education levels above undergraduate, as they are more likely to take loans. Additionally, customers with larger family sizes (three or more members) are strong prospects for personal loans. Finally, customers with a certificate of deposit (CD_Account) should be prioritized, as approximately 50% of them demonstrate a need for personal loans.