1. Analysis of financial statements: Credit personnel evaluate the borrower's financial status and solvency by analyzing the borrower's financial statements, such as balance sheet, income statement and cash flow statement. Commonly used financial indicators include current ratio, quick ratio, asset-liability ratio, interest guarantee multiple and so on. These indicators can help to judge the short-term and long-term solvency of borrowers. In addition, the borrower's profitability can also be examined. Common indicators include sales profit rate, sales gross profit rate and asset profit rate.

2. Collateral evaluation: For mortgage loans, banks need to evaluate collateral to determine its value, so as to provide guarantee for loans.

3. Credit rating: determine the credit rating of the borrower by evaluating the borrower's credit record and repayment record.

4. Quantitative analysis of risk assessment: for example, computer simulation (such as Monte Carlo simulation), decision tree analysis, sensitivity analysis and other methods are used to quantify risks.

5. Artificial intelligence (AI) algorithm: In modern credit risk control, AI algorithms such as logistic regression are widely used to enhance the accuracy, efficiency and automation of credit approval.

When banks or financial institutions conduct credit analysis, they usually follow some basic principles, such as the principle of data accuracy, the principle of checks and balances and the principle of cross-validation. These principles aim to ensure the accuracy and fairness of credit decisions.