Credit risk analysis is an important application of analytics in the financial industry. Here is an example of how you can analyze credit risk using Excel:
Data Preparation: The first step is to prepare the data for analysis. In this example, we will use a data set containing information on loan applicants, such as their income, credit score, employment status, and loan default status.
Descriptive Analysis: Next, we can perform some descriptive analysis to get a better understanding of the data. This may include calculating summary statistics such as mean and standard deviation, creating histograms to visualize the distribution of variables, and calculating correlations between variables.
Model Development: We can then develop a credit risk model using logistic regression. Logistic regression is a statistical method that is commonly used for binary classification problems, such as predicting whether a loan applicant will default on their loan or not.
Model Evaluation: Once we have developed the model, we need to evaluate its performance using metrics such as accuracy, precision, and recall. We can also create a confusion matrix to visualize the performance of the model.
Scenario Analysis: Finally, we can perform scenario analysis to see how changes in variables such as income or credit score would impact the predicted probability of default. This can help us identify which variables are most important in determining credit risk.
Here are some specific steps you can follow in Excel to perform these tasks:
Data Preparation: Import the loan applicant data into Excel and clean the data as necessary (e.g., removing missing values or outliers). You can use the data cleaning and manipulation tools in Excel such as filters, sorting, pivot tables, and formulas.
Descriptive Analysis: Use Excel's built-in functions to calculate summary statistics such as mean, median, and standard deviation. You can create histograms using the "Data Analysis" tool in Excel or by creating a chart and setting the data range and bin size. Use the "Correlation" function to calculate correlations between variables.
Model Development: Use the "Logistic Regression" tool in Excel's "Data Analysis" add-in to develop the credit risk model. You will need to select the input variables and the target variable (i.e., loan default status), and set the model parameters such as regularization strength and convergence criteria. Excel will output the coefficients of the logistic regression model, which can be used to predict the probability of default for new loan applicants.
Model Evaluation: Calculate the accuracy, precision, and recall of the model using Excel's built-in functions or by creating a confusion matrix. You can also visualize the performance of the model using a ROC curve or a lift chart.
Scenario Analysis: Use Excel's "Goal Seek" function to perform scenario analysis. For example, you can set the income variable to different values and see how this affects the predicted probability of default. You can also create sensitivity analysis tables or charts to visualize the impact of changes in multiple variables on credit risk.