This project demonstrates how Prompt Engineering can be used with Large Language Models (LLMs) to evaluate loan applications while considering financial regulatory guidelines.
The goal of this project is to design prompts that guide an AI system to analyze loan applicant information, evaluate financial risk, and provide explainable approval or rejection decisions.
The implementation is done using a Google Colab Notebook, which allows users to run and experiment with the system interactively.
Financial institutions must carefully evaluate loan applications to ensure responsible lending and regulatory compliance.
Traditional manual evaluation can be:
- Time-consuming
- Inconsistent
- Prone to human bias
This project explores how AI with prompt engineering can assist in performing structured and explainable loan evaluations.
The system guides the AI model to:
- Analyze applicant financial data
- Evaluate potential risks
- Follow regulatory considerations
- Provide structured reasoning for decisions
The system evaluates loan applications using a Large Language Model guided by structured prompts.
The evaluation considers several financial attributes including:
- Applicant income
- Credit score
- Existing debt
- Employment status
- Requested loan amount
Prompt engineering is used to control how the AI model behaves and responds.
Carefully designed prompts instruct the AI to:
- Act as a financial loan evaluation expert
- Analyze applicant information
- Apply financial reasoning
- Produce structured output
The AI evaluation follows common financial risk assessment principles such as:
- Debt-to-Income ratio analysis
- Credit score thresholds
- Employment stability
- Loan affordability assessment
This helps simulate real-world regulatory decision making used in financial institutions.
Instead of simply approving or rejecting a loan, the AI provides:
- Decision (Approve / Reject)
- Risk analysis
- Reasoning behind the decision
This ensures transparency and interpretability in AI-based decision making.
The project is implemented in Google Colab Notebook, allowing users to:
- Modify input data
- Test different prompts
- Observe how prompt structure changes AI responses
| Technology | Purpose |
|---|---|
| Python | Core programming language |
| Google Colab | Execution environment |
| Prompt Engineering | Designing AI instructions |
| Large Language Models (LLM) | AI reasoning for loan evaluation |
TEAM_015_Capstone_Project/ │ ├── Team_15_Capstone_Project_PromptEngineering.ipynb │ └── README.md
- Loan applicant information is provided as input.
- A structured prompt template is created.
- The prompt is sent to the AI language model.
- The AI analyzes the applicant’s financial data.
- The AI generates a response containing:
- Loan approval decision
- Risk assessment
- Explanation of reasoning.
Applicant Name: John Doe
Annual Income: $60,000
Credit Score: 720
Existing Debt: $10,000
Employment Status: Full Time
Loan Amount Requested: $25,000
Loan Decision: Approved
Reason:
The applicant has a strong credit score and stable employment.
The debt-to-income ratio is within acceptable limits.
Overall financial risk is low.
Download the file:
Team_15_Capstone_Project_PromptEngineering.ipynb
https://colab.research.google.com
- Click Upload Notebook
- Select the
.ipynbfile - Open the notebook
Runtime → Run All
The notebook will execute the prompt engineering workflow and generate AI responses.
- Banking systems
- Financial technology (FinTech)
- Automated loan evaluation
- AI-based financial risk analysis
- Regulatory compliance assistance
- Prompt Engineering
- AI decision support systems
- Explainable AI
- AI applications in financial services
- Responsible AI development
This project is a demonstration prototype.
- Uses simulated loan data
- Not connected to real banking systems
- AI recommendations should not replace human decision making
- Integration with real financial datasets
- Risk scoring models
- Web-based user interface
- Database integration
- Real-time compliance checking