Use Case | Financial Risk Management through Semantic Analysis and Topic Creation

Use Case: Financial Risk Management through Semantic Analysis and Topic Creation

General Description:

Financial risk management requires identifying risk patterns in large volumes of historical data, such as financial reports and audit documents. A model that organizes information into topics, uses vector databases for semantic searches, and analyzes historical documents in PDF format helps detect trends and potential financial issues. This approach enables proactive decision-making and reduces future risks with greater precision and efficiency.

How It Works:

1. Uploading Historical Financial Reports in PDF: Users upload historical reports such as financial statements, internal audits, and risk analysis.
2. Automatic Segmentation and Topic Creation:
   • The model automatically organizes data into topics related to financial risks, such as:
     • Liquidity Risks: Indicators like insufficient cash flow or excessive reliance on external financing.
     • Credit Risks: Identification of overdue receivables or high debt levels.
     • Operational Risks: Rising operational costs or decreasing margins.
     • Compliance Risks: Deviations from legal or accounting regulations.
3. Semantic Pattern Analysis:
   • The model detects historical patterns in reports indicating recurring risks, such as:
     • Increase in inventory turnover days.
     • Significant variations in operating margins.
     • Increase in long-term debt levels.
4. Specific Semantic Searches:
   • Users can make specific queries such as:
     • “What are the main causes of liquidity risk in the last three years?”
     • “Identify periods with significant over-indebtedness.”
5. Risk Reports Generation:
   • The model creates a summary of detected risks, including:
     • Risk trends by metric.
     • Recommendations to mitigate recurring risks.
6. Storage in Vector Databases:
   • Data is stored for future queries, historical comparisons, and audits.

Practical Example:

Scenario: A bank needs to identify financial risks from the last five years to improve its risk mitigation strategy for commercial loans.

Process with the Model:

1. Upload Documents: The team uploads five years of financial reports and credit analysis to the system.
2. Segmentation and Analysis:
   • The model organizes the information into topics like:
     • Credit Risks: Increase in overdue receivables from high-risk clients.
     • Liquidity Risks: Decrease in operational cash flow compared to short-term obligations.
     • Operational Risks: Increased maintenance costs in unprofitable projects.
3. Pattern Identification:
   • Semantic analysis detects:
     • 2019-2020: High dependence on external financing to meet short-term obligations.
     • 2021: 15% increase in overdue receivables.
     • 2022: Reduction in operating margin due to rising personnel costs.
4. Report Generation:
   • The system produces a summary that includes:
     • Main identified risks:
       • Excessive financing reliance in 2019-2020.
       • Growth in overdue receivables in 2021.
     • Recurring trends:
       • Progressive decline in operating margin.
     • Recommendations:
       • Diversify funding sources.
       • Strengthen collection policies to reduce overdue receivables.
5. Report Output: The team receives a consolidated report that serves as the foundation to adjust their risk mitigation strategy.

Benefits of the Model in Financial Risk Management:

1. Identification of Recurring Patterns:
   • Detects historical trends in financial risks that might go unnoticed in manual analysis.
2. Automatic Segmentation by Topics:
   • Organizes data into key categories, such as liquidity, credit, operations, and compliance, for more efficient review.
3. Contextual and Specific Searches:
   • Allows searches based on the meaning of the text, providing relevant and actionable results.
4. Clear Report Generation:
   • Highlights the most important risks and recommendations to mitigate them, facilitating strategic decision-making.
5. Storage and Historical Comparisons:
   • Provides quick access to processed data for audits, continuous monitoring, and future analysis.

Additional Applications:

1. Internal Risk Audits:
   • Detailed review of historical metrics to identify significant deviations.
2. Regulatory Compliance:
   • Ensures financial metrics comply with local or international regulations.
3. Strategic Management:
   • Provides key information to adjust business and financial strategies.
4. Project Evaluation:
   • Analyzes financial risks associated with specific initiatives or investments.

Practical Example:

Additional Scenario: A retail company analyzes financial risks related to its international expansion.

Without the Model:

• Analysts manually review hundreds of reports from international subsidiaries, a process that takes weeks and is prone to errors.

With the Model:

• The system segments the reports into key metrics and detects recurring risks, such as:
  • Increase in logistics costs in emerging markets.
  • Difficulties in collecting accounts in new regions.
  • Reduction in profitability in international operations.

Conclusion: Automated financial risk management through topic segmentation and semantic searches transforms a complex process into a precise, fast, and strategic task. This model allows organizations to identify historical patterns, assess recurring risks, and make informed decisions to mitigate future problems. It is ideal for banks, insurers, internal audits, and any organization managing large volumes of historical financial data.