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Personalized Content Generation for Bank of Baroda Hackathon 2024

Team: Coach_Sahab

"Driving the technology through the leaps of Himalayas"

Project Overview

This project aims to solve the problem of personalized content generation for the banking sector, enhancing customer satisfaction and engagement through tailored content. Our solution leverages advanced AI and machine learning models to deliver personalized marketing materials, financial reports, and educational content.

App Download

Link: Click Here

Demo Video

Link: Click Here

Screenshots

Financial Advisor
AI Financial Advisor 		Dashboard Home Screen
Dashboard Home Screen 		Financial Report
AI Generated Financial Report
AI Prompts 1
Personalized AI Bots 		AI Prompts 2
Personalized AI Bots 		AI Loan Recommender
Bot Detector
Transaction
AI Based Smart Notification 		Profile Screen
Profile Screen 		Graph and Goals
Graph and Goals
Login Screen
Login Screen 		Splash Screen
Splash Screen 		AI Policy Recommender
AI Policy Recommender
AI Loan Recommender
AI Loan Recommender 		Transaction
Transaction 		Transaction Screen
Payment Screen

Repository

Architecture

Overall Architecture

  1. Data Ingestion Layer

    • Components: Azure Data Factory, Azure Data Lake Storage
    • Function: Collects and processes customer data from multiple sources, ensuring data quality and consistency.

  2. AI Model Layer

    • Components: Azure Machine Learning, Azure Kubernetes Service (AKS), Azure Batch
    • Function: Utilizes AI models to generate personalized content based on customer data analysis.

  3. Distribution Layer

    • Components: Azure API Management, Azure Functions, Azure Logic Apps
    • Function: Distributes personalized content through appropriate channels (email, SMS, mobile app).

  4. Feedback Loop

    • Components: Azure Application Insights, Power BI
    • Function: Gathers feedback on content effectiveness to refine and improve personalization algorithms.

Overall Architecture (For the final production ready application)

image

Backend Architecture

  1. Source Code Management

    • Tools: GitHub, Jenkins/GitHub Actions
    • Function: Version control, continuous integration, and deployment.

  2. Deployment

    • Components: Cloud-based virtual machines, Docker
    • Function: Hosting the application and running it in containers.

  3. AI System

    • Components: AI models for processing complex queries
    • Function: Provide advanced functionalities.

  4. API

    • Function: Interfaces for communication between software components, handling client requests.

  5. Database Management

    • Components: MongoDB
    • Function: Storing and retrieving application data efficiently.

Backend Architecture Diagram

image

Generative AI Architecture

  1. Langchain Agent

    • Function: Uses a language model to choose a sequence of actions to take.

  2. Langchain Tools

    • Function: Interfaces that the agent can use to interact with the world.

  3. OpenAI LLM (Azure)

    • Components: OpenAI ChatGPT 3.5/4.0, Langchain Framework
    • Function: Generative AI for creating personalized content.

Generative AI Architecture Diagram

image

Frontend Architecture

  1. Retrofit Service

    • Function: Simplifies network operations, manages API calls, and converts responses into data models.

  2. DI Class for Dagger

    • Function: Manages dependencies, ensuring clean and reusable components.

  3. ViewModel

    • Function: Manages UI data and business logic.

  4. Repository

    • Function: Provides a single source of truth for data, handles caching and synchronization.

  5. Activity/Fragment

    • Function: Displays data, handles user interactions, and delegates logic to ViewModel.

Android Architecture Diagram

image

Azure Resources Required

  • Azure AI Studio & Azure OpenAI: For advanced language models.
  • **Azure App Services **: For Deployement of the Backend for core and generative AI services

Methodology

  1. Pilot Testing
  2. Feedback and Refinement
  3. Gradual Rollout
  4. Marketing and Awareness Campaigns
  5. Customer Support and Training
  6. Continuous Improvement

Key Differentiators

  • Highly Personalized Content
  • Real-Time Content Generation
  • Comprehensive Integration
  • Data-Driven Insights
  • Continuous Improvement

Adoption Plan

  1. Pilot Testing
  2. Feedback and Refinement
  3. Gradual Rollout
  4. Marketing and Awareness Campaigns
  5. Customer Support and Training
  6. Continuous Improvement

Scalability

  • Cloud Infrastructure
  • Microservices Architecture
  • AI Model Deployment
  • Data Processing Efficiency
  • Distribution Channels
  • Continuous Monitoring and Optimization

Security Considerations

  • Data Encryption
  • Access Control
  • Compliance Certifications
  • Network Security
  • Threat Detection
  • Data Residency
  • Audits and Assessments
  • Disaster Recovery

Android App Releases

Version History

v1.0.0 - July 31, 2024

  • Initial release with core features
  • Includes functionalities like user registration, login, GenAI Features etc.

Contributors

  • Yash Kamal Saxena (Generative AI Infra Developer)
  • Tushar Garg (Backend Core Infra Developer)
  • Uphar Gaur (Android Infra Developer)

Thank you for considering our project!