Hackathon submission
template
Introduction
Name
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Anuritha L
2
Anushka Singh
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Shreya Sindhu Tumuluru
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S G Navya
Phone Numbers
Qualification
(+91)-9844846404
3rd year Student (Artificial
Intelligence and Data
Science)
(+91)-89798 37191
3rd year Student (Artificial
Intelligence and Data
Science)
(+91)-91085 65437
3rd year Student (Artificial
Intelligence and Data
Science)
(+91)-73537 24272
3rd year Student (Artificial
Intelligence and Data
Science)
Expertise
Elevated pitch
• Pitch your proposed solution for the given problem statement.
• Include a video clip, not exceeding 3 minutes in duration, in which
you elucidate your proposed solution, its relevance, and the
compelling reasons for the bank to embrace it.
• Guidelines for Video Presentation
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Prepare a video lasting no more than 3 minutes.
Upload the video to YouTube, setting it as 'unlisted.'
Include the link to access the unlisted video in the slide.
Refer to the provided link for instructions on creating an unlisted video.
(https://www.guidingtech.com/upload-private-and-unlisted-videos-on-youtube/)
Problem Statement
Objective: Enhance the banking app experience through hyperpersonalization techniques.
● Develop a middleware platform that efficiently consumes, processes,
and manages bank data.
● Create algorithms within the middleware for tailored services, hyperpersonalized recommendations and proactive notifications.
● Seamlessly integrate the middleware with existing bank systems for
real-time data synchronization.
● Develop a user-friendly app that showcases hyper-personalized
recommendations and tailored services.
● Focus on an intuitive and visually appealing user interface with
customization options.
● Implement robust security measures to protect users' financial data
and adhere to relevant guidelines.
Solution Overview
The Hyper-personalized Banking Middleware presented as a mobile app is a
transformative solution designed to individualize the banking experience, accommodating
the distinct requirements of each customer. Our solution redefines user-centric banking
by offering customers tailor-made financial solutions and a profound understanding of
their financial situation. This creates an exceptionally sophisticated and personalized
banking experience, combining personalization, advanced data insights, and cutting-edge
technology to empower users.
This app has the following features
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Auto-Adaptive UI: The UI and dashboard is adjusted based on the most used features
for a user-centric experience
Analytics Visualizations: Deep insights through intuitive charts and graphs.
Real-time Notifications: Timely and personalized transaction alerts.
AI Chatbot: Instant and intelligent responses to user queries.
Personalized Recommendations of schemes: AI-driven scheme suggestions.
Personal & Business Views: Tailored experiences for diverse customer segments.
Summarized Terms: Simplified Terms and Conditions for quick understanding.
Technological Stack
Data Processing and Storage:
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Backend (Flask)
Database (MongoDB)
Geolocation and Mapping:
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Geolocation and Maps (OpenStreetMap (OSM) and OverpassAPI)
Data Visualization:
User Interface Development:
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Frontend (Flutter)
User Interface Customization (Flutter)
Machine Learning and AI Integration:
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Federated Learning (Tensorflow Federated)
NLP (Hugging Face API)
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Data analytics (Plotly)
Mapping Libraries for Visualization( flutter_map or
google_maps_flutter)
Security and Authentication:
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Biometric Security (local_auth for Flutter)
Deployment and Hosting:
User Behavior Tracking and Analytics:
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Analytics (Firebase Analytics)
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Backend Deployment (Heroku )
ML Model API Deployment (Cloud service or Docker)
Security Standards Followed
Identity
Management
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'local_auth' Package: Securely
implements biometric
authentication in Flutter.
KYC Regulations: Enforces
identity verification for KYC
compliance.
Two-Factor Authentication
(2FA): Adds extra account
security.
Biometric Authentication:
BEnhances user security with
fingerprint and facial
recognition
Data Security
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Federated Learning:
Preserves data privacy for
personalized
recommendations.
TFF: Ensures secure model
sharing and updates.
Data Encryption: Safeguards
sensitive data during
transmission and storage.
Regular Model Updates:
Complies with data protection
standards.
Consent
Management
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User Consent: Obtains explicit
user consent for data
processing.
User-Friendly Messages:
Provides clear and transparent
consent explanations.
User Control: Allows
customization of data sharing
and personalization.
Review and Modification:
Supports user data rights and
privacy regulations.
Personalization techniques
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Personalized Scheme Recommendations: Use machine learning algorithms to analyze a user's
transaction history and preferences. Provide tailored suggestions for banking schemes, investments,
and services that align with their financial goals
Personalized Financial Goals with Federated Learning: Federated learning allows the model to learn
from user data without directly accessing it. This ensures privacy. Enable users to set and track
personalized financial goals, and use federated learning to improve goal recommendations based on
collective user data.
Adaptive User Interface: Customize the app interface based on user behavior. Frequently used
features, like checking account balance, should be prominently displayed. This ensures a user-centric
design, making the app more intuitive and efficient.
Visual Expenditure Summaries with Graphs: Create visual representations, like graphs, to illustrate
monthly and yearly expenditures. This provides users with a clear overview of where their money is
going, helping them make informed financial decisions.
Text Summarization for Terms and Conditions: Implement a text summarization tool to condense
lengthy terms and conditions. This ensures users can grasp essential information without the need to
read through lengthy documents.
Separate Interfaces for Personal and Business Accounts: Design distinct interfaces for personal and
business accounts to streamline user experience.
Notifications for EMI and Bills: Implement timely notifications for upcoming EMI payments and bills.
Users can customize notification preferences to stay on top of their financial commitments without
missing deadlines.
Data Utilization and Sources
Data Sources
Provided dataset: Includes time
stamps for various customer events,
such as account openings, KYC
updates, transactions, and app
interactions.
Additional sources:
Location Data: Collected with user
consent, it includes geographic
coordinates and addresses, facilitating
location-based services and nearby
recommendations.
User Profile Data: Gathered through
user input during registration,
containing demographic details and
financial preferences for personalized
recommendations.
Device and App Usage Data: Captures
technical information like device type
and app interaction patterns, allowing
for performance optimization and user
interface improvements.
Data Utilization
Customer Behavior Analysis: Analyze time stamps of events like FD
openings, KYC updates, transactions, and app interactions to understand
individual customer behavior.
KYC Updates: Track KYC updates such as PAN, voters ID, driving license,
and Aadhar address to ensure regulatory compliance and improve user
verification processes.
Transaction Insights: Examine data related to transactions like NEFT and
IMPS to gain insights into user payment habits and financial activities.
App Interaction Patterns: Analyze app login and user engagement
patterns to identify user preferences & optimize the app's user interface.
Location-based services: Leverage location data to offer services, such
as finding nearby ATMs or branch offices for users on the go.
Personalization through User Profile Data: Used to personalize
recommendations and services by considering user demographics,
financial goals, and preferences.
Enhancing User Experience: Optimizes app performance and user
interface for a smoother experience, taking into account device
specifications and usage patterns.
User Feedback and Iteration
Enhancing the banking app through personalization is our focus. We collect and enrich data,
employ advanced machine learning, and prioritize ethical AI practices. A/B testing, in-app
feedback loops, predictive modeling, and data partnerships drive iterative improvements.
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Enhance User Profiling through Data Enrichment: Collect and enrich data to understand your users'
preferences and behaviors.
Implement Advanced Machine Learning Techniques: Usage of deep learning and neural networks to
provide more accurate and relevant content recommendations.
Compare Different Algorithm Versions with A/B Testing: Run live experiments where users experience
different algorithm versions to determine which one performs better.
Incorporate In-App User Feedback Loops: Include in app feedback loops that gather user-generated
data to iteratively improve algorithms.
Leverage Predictive Modeling for Proactive Engagement: Develop predictive models based on
recurrent neural networks to anticipate user preferences and pre-emptively suggest relevant content.
Prioritize Ethical AI Practices and Data Privacy: Ensure fairness and transparency through ethical AI
practices and compliance with data privacy regulations, such as GDPR.
Iteratively Apply Feedback-Driven Updates: Continuously gather user feedback and apply it to refine
algorithms, following agile development methodologies.
Collaborate for External Data Enrichment: Establish strategic data partnerships to access third-party
data sources, facilitating data enrichment and diversified insights.
Project Plan and Timelines
Future Development and Expansion
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Advanced Security Measures: Implement cutting-edge security technologies,
such as blockchain-based encryption and multi-factor authentication, to
fortify data protection and enhance user trust in the app's security.
Integration with Financial Ecosystem: Expand integrations with third-party
financial apps and services to provide a holistic view of a user's financial life,
including investments, loans, and insurance.
Improvement of AI Assistant: Enhance the AI assistant's natural language
understanding and contextual intelligence to deliver even more precise and
relevant financial guidance and recommendations, making it an indispensable
companion for users' financial journeys.
Cross-Platform Compatibility: Develop versions of the app for various
platforms, including wearable devices and smart speakers, to offer a
seamless experience across devices.
Data Monetization: Explore opportunities to monetize data in a user-centric
and privacy-compliant way, such as by providing anonymized, aggregated
financial insights to researchers or businesses.
Regulatory Compliance
Conclusion
• Summarize the key points of solution concept and its potential
impact.
Visuals and Prototype
• Include any visuals, diagrams, or a clickable prototype that
demonstrate your concept.