Use Case | Credit and Loan Analysis with PDF Data

Use Case: Credit and Loan Analysis with PDF Data

Overview:

In the financial sector, evaluating credit and loan applications involves reviewing complex and voluminous documents that include credit histories, income, collateral, and associated risks. This generative model allows for the automatic analysis of PDF documents, extracting key information, assessing the applicant’s solvency, and providing clear insights for decision-making. It optimizes the analysis process, reduces evaluation time, and improves the accuracy of financial decisions.

How It Works:

1. Uploading Applications and Related Documents:
   • Users upload credit applications, financial statements, income reports, credit histories, and collateral documents in PDF format.
2. Extraction of Key Information:
   • The model analyzes the documents and extracts data such as:
     • Declared income
     • Existing liabilities
     • Offered collateral
     • Payment history
     • Credit scores and key financial ratios
3. Solvency and Risk Analysis:
   • Calculates key metrics such as:
     • Debt-to-Income Ratio (DTI)
     • Monthly payment capacity
     • Credit risk level based on the applicant’s history
   • Identifies potential risks such as:
     • Late payment history
     • Inconsistencies in declared data
4. Comparison with Credit Policies:
   • Automatically verifies whether the data meets the requirements of the financial institution’s internal policies.
5. Generation of Evaluation Reports:
   • Provides a clear report with:
     • Summary of the applicant’s data
     • Identification of financial strengths and weaknesses
     • Recommendation regarding the approval or rejection of the credit

Practical Example

Scenario: A bank receives mortgage credit applications from several clients and needs to quickly assess their viability.

Process with the Model:

1. Document Upload:
   • The bank uploads the applications in PDF format along with supplementary documents, such as financial statements and credit history reports.
2. Analysis and Evaluation:
   • Extracts and analyzes the data from each application:
     • Client A:
       • Monthly income: $5,000
       • Current debt: $20,000
       • Debt-to-Income ratio (DTI): 40%
       • Credit history: 2 late payments in the last 12 months
     • Client B:
       • Monthly income: $8,000
       • Current debt: $10,000
       • Debt-to-Income ratio (DTI): 20%
       • Credit history: No late payments, excellent credit score
3. Report Generation:
   • Report Summary:
     • Client A: High risk due to late payment history and high DTI. Recommendation: Reject or request additional collateral.
     • Client B: Solid profile with low debt levels. Recommendation: Approve with standard conditions.

Benefits of the Model in Credit and Loan Analysis

1. Automation of the Evaluation Process:
   • Significantly reduces the time required to analyze applications and documents.
2. Accuracy in Risk Analysis:
   • Calculates key financial metrics and detects potential risks based on objective data.
3. Compliance with Internal Policies:
   • Automatically verifies compliance with the institution’s credit policies.
4. Generation of Custom Reports:
   • Provides clear summaries and specific recommendations for each application.
5. Scalable Multilingual Support:
   • Analyzes documents in over 80 languages, ideal for financial institutions with international clients.

Additional Applications

1. Mortgage Credit Evaluation:
   • Analyzes mortgage loan applications, assessing income, debts, and collateral.
2. Commercial Loans:
   • Verifies the viability of loans for small and medium-sized businesses, considering cash flows and assets.
3. Client Portfolio Management:
   • Identifies high-risk client profiles for proactive measures.
4. Fraud Prevention:
   • Detects inconsistencies or suspicious data in application documents.
5. Optimization of the Approval Process:
   • Automates the validation of basic criteria to speed up decision-making.

Practical Example

Additional Scenario: A credit union evaluates personal loans from clients in Mexico, Spain, and the United States.

Without the model:

• Manually reviewing the documents and performing calculations takes days and increases the risk of omissions or errors.

With the model:

• Automatically translates and analyzes applications:
  • Client A (Mexico): DTI of 50%, moderate credit score. Recommendation: Rejection or adjustment of conditions.
  • Client B (Spain): Stable income and no active debts. Recommendation: Immediate approval.
  • Client C (USA): Insufficient collateral for the requested amount. Recommendation: Request more collateral or reduce the loan amount.
• Generates a clear report that facilitates decision-making for each client.

Conclusion: Credit and loan analysis using this model ensures accurate, fast evaluations that align with the financial institution’s credit policies. Its ability to analyze PDF documents, extract key data, and generate detailed reports makes it an indispensable tool for banks, credit unions, and financial companies seeking to improve efficiency and accuracy in the evaluation of credit applications. It is ideal for institutions dealing with large volumes of applications or international clients.