SmartLend 🏦

AI-Powered Financial Loan Decision Engine

Enterprise-grade Loan Processing System built with Clean Architecture, Domain-Driven Design (DDD), and .NET 10.

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🚀 Overview

SmartLend is a compliance-ready financial engine designed to automate loan risk assessment. It moves beyond simple CRUD to demonstrate how Deterministic Logic and Generative AI can work together in a secure banking environment.

The system enforces strict financial rules using C# (safe), while leveraging Google Gemini 2.5 Flash to generate empathetic, human-readable explanations for customers (smart).

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🏗️ Architecture & Design Decisions

The solution follows strict Clean Architecture principles to ensure that Business Logic is isolated from Technical Details.

The 4 Layers (The Onion)

1. 🟡 Domain (The Core): Contains the Enterprise Business Rules.
   • Rich Domain Model: Logic is encapsulated in LoanApplication.Evaluate(), ensuring a loan can never exist in an invalid state.
   • Pure: Zero dependencies on external libraries.
2. 🔴 Application (The Orchestrator): Contains the Use Cases.
   • Orchestrates the flow: DTO -> Domain Logic -> Database -> AI Advisor.
3. 🔵 Infrastructure (The Plugins): Contains the external tools.
   • Persistence Strategy: Implements the Repository Pattern with Dual Support (In-Memory & SQL Server).
   • AI Service: Google Gemini 2.5 Flash implementation via HttpClient.
4. 🟢 API (The Entry Point): The REST API/Swagger UI.
   • Wires everything together using Dependency Injection.

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🌟 Key Features

🤖 1. AI Financial Advisor (Google Gemini 2.5)

I integrated Google Gemini 2.5 Flash into the Infrastructure layer to act as a "Financial Advisor."

• Workflow: When a loan is rejected by the C# math engine, the AI analyzes the user's data (Salary, Credit Score) and generates a polite, personalized financial tip.
• Safety: The AI never makes the approval decision. It only explains it. This prevents "AI Hallucinations" from approving risky loans.

💾 2. Dual Persistence Strategy (SQL & In-Memory)

To demonstrate architectural flexibility, I implemented two database providers in the Infrastructure layer:

• InMemoryLoanRepository: Optimized for rapid development, unit testing, and demos (Currently Active).
• SqlServerLoanRepository: Production-ready implementation using Entity Framework Core.
• Note: The application is currently configured to run in In-Memory Mode for ease of review (no local SQL setup required). To switch to SQL, simply uncomment the line in Program.cs.

📜 3. Compliance Audit Log

Every decision is immutable. The system records a timestamped (UTC) Audit Log for every action.

• Example Log: [AUDIT] Action: Rejected | Time: 12/05/2025 | AI Advice: "Debt-to-Income ratio too high..."

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🛠️ Tech Stack

• Framework: .NET 10 (Preview)
• Architecture: Clean Architecture / Onion Architecture
• AI Model: Google Gemini 2.5 Flash
• Database: In-Memory (Active) / SQL Server (Implemented)
• Testing: xUnit
• Documentation: Swagger / OpenAPI

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🏃‍♂️ How to Run

Prerequisites

• .NET 10.0 SDK installed.
• (Optional) A Google Gemini API Key. Note: If no key is provided, the system gracefully degrades to "Offline Mode" and still processes loans.

Steps

1. Clone the repository:

   git clone [https://github.com/TaGoat/SmartLend.git](https://github.com/TaGoat/SmartLend.git)
   cd SmartLend

2. Run the API:

   dotnet run --project SmartLend.Api

3. Test with Swagger: Open your browser to the URL shown in the terminal (e.g., http://localhost:5xxx/swagger).

   • POST /api/loans: Submit a loan request.
   • Check Console: Watch the VS Code terminal to see the AI Advice and Audit Logs printed in real-time.

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🧪 Testing Strategy

Core business logic is covered by Unit Tests in the SmartLend.Domain.Tests project.

• Run Tests: dotnet test
• Coverage: Verifies that the "Debt-to-Income" and "Credit Score" rules trigger the correct Rejection status.

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Built by [Tareq AL-Suraihi]