Overview

This project involved developing a secure, cloud-based financial management application designed to assist users with account management, expense tracking, market analysis, and financial forecasting. The application leveraged AI algorithms to analyze financial data, provide insights on expense trends, generate projections, and offer actionable recommendations to optimize users’ financial health.

Objectives

• Automate account management by consolidating inflows and outflows.
• Analyze expense patterns to identify spending trends.
• Predict future financial states using AI-driven projections.
• Provide actionable recommendations for better financial decision-making.
• Ensure security and scalability via a cloud-based infrastructure.

Technology Stack

• Cloud Infrastructure: Hosted securely on AWS Cloud, utilizing services such as Amazon EC2, AWS RDS, and AWS Lambda for scalability and resilience.
• Frontend: Developed using Angular, providing dynamic, responsive dashboards and user-friendly interfaces.
• Backend: Built with Java microservices architecture, ensuring modularity, maintainability, and independent scalability.
• AI/ML Components: Incorporated machine learning models for trend analysis, forecasting, and recommendation generation.

Features

1. Account Management:
   • Consolidated view of all financial accounts.
   • Real-time transaction tracking and categorization.
   • Secure authentication and role-based access control.
2. Market Analysis Based on Expense Trends:
   • AI algorithms analyze historical expense data to identify patterns.
   • Categorization of expenses by type, frequency, and merchant.
   • Visual dashboards showing monthly, quarterly, and annual trends.
   • Alerts for unusual spending patterns or deviations.
3. Projections and Predictions:
   • AI-powered forecasting models predicting cash flow based on historical inflow and outflow.
   • Scenario analysis to simulate future financial outcomes under various conditions.
   • Market trend insights based on aggregated anonymized data for benchmarking.
4. Recommendations:
   • Personalized suggestions for reducing unnecessary expenses.
   • Investment and savings advice based on financial goals and predicted trends.
   • Alerts for upcoming bills, potential overdrafts, and budgeting tips.

Security and Compliance

• Data encrypted in transit and at rest using AWS KMS.
• Use of AWS Identity and Access Management (IAM) for fine-grained permissions.
• Compliance with industry standards such as GDPR and PCI DSS.
• Regular vulnerability assessments and penetration testing.

Outcomes

• Improved financial visibility and control for users.
• Enhanced decision-making with AI-driven insights.
• Scalable and reliable system architecture supporting growing user base.
• High user satisfaction due to intuitive UI and valuable recommendations.