Financial Prompt Framework

Advanced AI-Powered Personal Wealth Management System

Multi-Layer Prompt Engineering for FinTech Applications

View on GitHub 📄 Documentation

Project Overview

This project demonstrates the integration of advanced AI language models (GPT-5, Gemini 2.5, Claude Sonnet 4) to build a sophisticated personal wealth management application combining multi-bank account aggregation with automated investment advisory capabilities. Developed as part of the Prompt Engineering internship at ZeTheta Algorithm Private Limited, this work showcases the practical application of the Financial Prompt Framework (FPF) and Multi-Layer Prompt Strategy.

The system architecture follows industry best practices for FinTech applications, implementing PCI DSS compliance, Zero Trust security principles, OAuth 2.0 authentication, and advanced financial algorithms including Modern Portfolio Theory (MPT) and Tax-Loss Harvesting optimization. The codebase provides a complete implementation spanning microservices architecture, backend financial logic, responsive frontend interfaces, and comprehensive security measures.

🏗️
Microservices Architecture
Scalable, secure design with 99.99% uptime SLA, implementing strict network segmentation and Zero Trust security principles.
🤖
AI-Powered Financial Logic
Advanced algorithms for expense categorization, Modern Portfolio Theory optimization, and automated tax-loss harvesting.
🔒
Enterprise Security
Multi-factor authentication, end-to-end encryption (AES-256), secure key management with HSM integration, and fraud detection.
📊
Interactive Dashboard
React-based responsive interface with real-time portfolio tracking, budget visualization, and goal progress indicators.
🏦
Bank Integration
Seamless connection to multiple financial institutions via Plaid API with secure OAuth 2.0 token management.
📈
Investment Optimization
Robo-advisory capabilities with portfolio rebalancing, risk assessment, and personalized investment recommendations.

Multi-Layer Prompt Architecture

The project follows a systematic four-layer prompt engineering methodology designed specifically for complex FinTech applications:

Layer 1: Strategic Architecture
High-level system design including microservices architecture, database schemas, API specifications, and compliance framework.
Layer 2: Backend Development
Implementation of financial logic modules including MPT engine, expense categorization, budget tracking, and tax optimization algorithms.
Layer 3: Frontend Development
React-based user interface with interactive dashboards, real-time notifications, and mobile-responsive design.
Layer 4: Integration & Security
Security implementation, fraud detection algorithms, compliance monitoring, and automated vulnerability scanning.

Documentation

Comprehensive documentation covering all aspects of the project, from architectural decisions to implementation details and security considerations.

📋 Project Vision
Introduction to the Personal Wealth Management application, objectives, and technical approach.
🎯 Methodology
Complete guide to the four-layer prompt engineering methodology with code examples and API responses.
🗂️ Core Framework
Detailed task decomposition, workflow planning, and sequential prompt construction methodology.
Architecture Diagram - Microservices Structure

Example Architecture Diagram (Original Mermaid, Portfolio View)

Technology Stack

Python Flask/Django React PostgreSQL Redis Plaid API AWS Docker OAuth 2.0 NumPy/Pandas Scikit-learn Recharts