Evaluating a Borrower's Probability of Default Using an Internal Model is explained as follows:

Usually a value between 0 and 1, it indicates the degree to which a borrower is likely to default. In general, the higher the probability of default, the weaker the borrower's ability to take on risk.

Thus, banks and financial institutions can determine a borrower's loan amount, interest rate, and repayment period based on the probability of default, and strengthen risk management measures for borrowers who may default. At the same time, the probability of default can also help banks more accurately assess their own risk level and business risk tolerance, effectively preventing the occurrence of systemic risk.

The default probability of a borrower is an important risk assessment indicator, and evaluation using internal models is one of the more commonly used methods. The following is the classic internal model evaluation process:

1. Data collection:

First of all, it is necessary to collect a certain amount of historical data, including information about the borrower's personal and financial status, repayment records and other aspects. These data need to be pre-processed, cleaned and standardized to ensure data quality.

2. Feature Selection:

In the feature selection phase, it is necessary to determine which data have important arguments for the default probability model. This can be done through exploratory data analysis, statistical methods, and machine learning based algorithms. The final selection of features should have the effect of being able to make some contribution to the default probability model, while excluding those potential neuronal interferences.

After the data is processed, feature selection is needed to filter out the feature dimensions that have the most impact on default prediction. Feature selection is usually performed using statistical methods, machine learning algorithms and domain knowledge.

3. Model building:

After feature selection is completed, a default prediction model needs to be built. The model can use various machine learning algorithms, statistical methods, or expert systems, etc., and is trained with a training dataset.

4. Model Validation:

After the training is completed, the model needs to be validated. A common method is to divide the dataset into a training set and a test set, and use the test set to test the model and evaluate the prediction accuracy of the model and the stability of the model.

5. Model application:

When the model validation is passed, it can be applied to new and unknown borrower data to predict their default probability. It should be noted that the internal model to evaluate the borrower's default probability is a complex systematic project, which needs to be finely designed and managed in many aspects, such as data processing, feature selection, model building and validation, in order to maximize the accuracy and effectiveness of the model.

In practical application, the use of internal models to evaluate the likelihood of borrower default requires the provision of security guarantees, while the filing, regulatory and risk management aspects need to be strictly handled. In addition, the process of model building and validation involves machine learning and big data technology, with multiple bottlenecks such as data privacy, security, interpretability, fairness, etc. that need to be fully considered.