The following can be considered classical methods:

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Logistic regression is a model based on historical data and borrower characteristics that allows for effective assessment of default probability and potential losses. This method is most often used to build scoring cards.
Linear regression is used to predict portfolio performance, allowing for the assessment of overall risks across loan portfolios.
Additionally, clustering methods are used to segment data , which help identify groups of borrowers with similar characteristics.
Classic ML models

Based on the predictions of regulatory models, the bank can reserve benin whatsapp phone number transactions and meet regulatory capital adequacy requirements; this risk assessment approach is called IRB (Internal Ratings-Based Approach) . In this case, the models must comply with the requirements of the Central Bank and be validated by independent departments.



Risk Model Development Cycle
Risk Model Development Cycle

CRISP-DM (Cross-Industry Standard Process for Data Mining) is a common methodology for data analysis. It breaks down the process into six main steps:

Business Understanding
Defining the project's business goals and requirements.
Translating goals into a data analysis problem statement.
Drawing up a preliminary plan for achieving goals.
Data Understanding
Collection of initial data.
Data description, quality control.
Primary data visualization.
Identifying interesting subsets for further analysis.
Data Preparation
Selecting data for modeling.
Cleaning data from errors and noise.
Transformation of data into the required format.
Combining data from different sources.
Formatting data for modeling tools.
Modeling
Selection and application of various models and modeling techniques.
Calibration of model parameters.
Evaluation of the model in terms of quality and efficiency.
Evaluation
Careful assessment of the model and its compliance with business goals.
Determining next steps based on the results.
Deployment
Planning the implementation of the obtained results into production.
Monitoring and support of the implemented solution.
Creating a report and documenting the project.
The sequence of stages is not strict. As a rule, most projects require returning to previous stages to move forward.



Working with client data
To build models, sufficient and high-quality information about bank borrowers is required. Client data for assessing bank clients in Russia can come from various sources.

For example, the Credit History Bureau (CHB) provides information about the credit history of clients, and the Federal Bailiff Service (FSSP) provides data on the presence of debts.

Also important are the data collected and provided by mobile operators (mobile payments and account activity). Transaction data can come from both external payment systems and from the banks' internal system.