Data Requirements: A robust predictive model for loan defaults requires a comprehensive dataset. This would typically include:

• Historical Loan Data: Information on past loans, including loan amount, interest rate, loan term, borrower demographics (age, income, employment history), and credit score.
• Economic Indicators: Data on macroeconomic factors such as GDP growth, unemployment rates, inflation, and interest rates. These can influence borrowers' ability to repay.
• Borrower Behavioural Data: Data on payment history, credit card usage, and other financial behaviours that may indicate risk.
• External Data: Potentially, data from credit bureaus and other external sources providing creditworthiness assessments.

Model Selection: Several suitable models could be employed:

• Logistic Regression: A simple and interpretable model that predicts the probability of default.
• Decision Trees: A tree-like model that partitions the data based on predictor variables to identify default patterns.
• Support Vector Machines (SVM): A powerful model that finds the optimal hyperplane to separate defaulting and non-defaulting loans.
• Neural Networks (Deep Learning): Complex models capable of capturing non-linear relationships in the data, potentially leading to higher accuracy.

The choice of model depends on the size and complexity of the dataset, the desired level of interpretability, and the required accuracy.

Evaluation Methods: The model's performance should be rigorously evaluated using techniques such as:

• Accuracy: The proportion of correctly predicted defaults and non-defaults.
• Precision: The proportion of predicted defaults that are actually defaults.
• Recall: The proportion of actual defaults that are correctly identified.
• F1-Score: A harmonic mean of precision and recall, providing a balanced measure of performance.
• AUC-ROC: Area Under the Receiver Operating Characteristic curve, which measures the model's ability to distinguish between defaulting and non-defaulting loans.

Limitations: Predictive models are not perfect. Potential limitations include:

• Data Bias: If the historical data is biased (e.g., reflecting past discriminatory lending practices), the model will perpetuate those biases.
• Changing Economic Conditions: Models trained on historical data may not accurately predict defaults in periods of significant economic change.
• Black Box Models: Complex models like neural networks can be difficult to interpret, making it challenging to understand why a particular loan is predicted to default.
• Overfitting: The model may fit the training data too closely and perform poorly on unseen data. Regularization techniques can mitigate this.

To perform a what-if analysis using the sales volume, I would set up a spreadsheet with the following components:

1. Input Cell: A cell (e.g., cell B1) would be designated for the user to input the estimated units sold.
2. Cost Calculations: A cell (e.g., cell C1) would calculate the total variable cost based on the number of units sold (e.g., =B1*5).
3. Revenue Calculation: A cell (e.g., cell D1) would calculate the total revenue based on the number of units sold (e.g., =B1*15).
4. Profit Calculation: A cell (e.g., cell E1) would calculate the total profit by subtracting total costs from total revenue (e.g., =D1-C1-10000).
5. What-If Analysis Setup: I would then use the "What-If Analysis" feature in the spreadsheet program. Specifically, I would use the "Scenario Manager" or "Scenario Tool". This allows me to define different scenarios with varying values for the input cell (B1). For example, I could create scenarios for 3,000 units, 4,000 units, and 6,000 units. Each scenario would have a different value entered in cell B1.
6. Analysis and Interpretation: After defining the scenarios, the spreadsheet program would automatically calculate the profit for each scenario. I would then analyze the results to understand the relationship between the number of units sold and the potential profit. This would help the business make informed decisions about production and sales targets. The results could be presented in a table.

Example Table:

To use Goal Seek, I would set up the spreadsheet as described in Question 1. Then, I would use the Goal Seek function to find the number of units sold required to achieve a profit of £50,000 for each campaign.

1. Set up the spreadsheet: Ensure the spreadsheet contains the same cost and revenue calculations as in Question 1.
2. Identify the Target Cell: Identify the cell containing the profit calculation (e.g., E1). This is the cell we want to reach a value of £50,000.
3. Use Goal Seek: Go to the "Data" tab in the spreadsheet program and select "What-If Analysis" -> "Goal Seek".
4. Specify the Target Cell and Target Value: In the Goal Seek dialog box, enter the cell containing the profit calculation (e.g., E1) in the "Set cell" field and enter 50000 in the "To value" field.
5. Specify the Changing Cell: Specify the cell containing the number of units sold (e.g., B1) in the "By changing cell" field.
6. Click OK: The spreadsheet program will automatically adjust the value in the "By changing cell" (B1) until the "Set cell" (E1) reaches the target value (50000). The value in B1 will then display the number of units required.
7. Results: The Goal Seek function will return the number of units required for each campaign. For Campaign A (10% increase), the required units would be approximately 7,777.78. For Campaign B (15% increase), the required units would be approximately 6,666.67.

Note: The exact values may vary slightly depending on the spreadsheet program used.