?? AI-Based Credit Scoring Models for Decentralized Finance (DeFi) Platforms: Project Plan Overview

?? 1. Project Overview

The project focuses on developing AI-based credit scoring models specifically designed for decentralized finance (DeFi) platforms. The goal is to provide accurate, transparent, and scalable credit scoring solutions using blockchain technology and machine learning algorithms.

?? 15. Framework: Scrum of Scrums

The project will use the Scrum of Scrums framework with five independent teams working on different project components, coordinated by Scrum of Scrums Master Dimitris Souris.

?? 2. Project Timeline

• Phase 1: Initial Feasibility Study and Planning – 1 Month
• Phase 2: Tech Stack Setup and Model Development – 2 Months
• Phase 3: Integration with DeFi Platforms and Testing – 3 Months
• Phase 4: Full Deployment and Optimization – 1 Month

Total project duration: 7 months

?? 3. Feasibility Analysis

?? 3.1 Technical Feasibility

The project leverages a robust AI and blockchain tech stack, ensuring that the credit scoring model can process massive datasets securely and efficiently on decentralized platforms. The key technical components include:

• Machine Learning Algorithms: Used for credit scoring based on user transaction history, social profiles, etc.
• Blockchain Integration: Ensuring decentralized, tamper-proof data validation.
• Oracles: For fetching off-chain data securely.

?? 3.2 Economic Feasibility

Given the rising demand for DeFi, AI-based credit scoring models can disrupt traditional banking systems by providing credit assessments to the unbanked. The total budget estimate is €500,000, with high potential ROI due to scalability across multiple DeFi platforms.

?? 3.3 Market Analysis

• Demand: Growing interest in DeFi solutions and increased adoption of crypto-financial products.
• Competitors: Limited direct competitors offering AI-based decentralized credit scoring solutions, making this project a first-mover.
• Target Users: DeFi platforms, unbanked individuals, and institutions looking for decentralized credit risk analysis.

?? 4. SWOT Analysis

?? 4.1 Strengths

• Innovative AI integration: Strong differentiation in the DeFi space.
• Scalability: Ability to serve a global decentralized market.

?? 4.2 Weaknesses

• Technical complexity: Integration of AI and blockchain can introduce challenges.

?? 4.3 Opportunities

• Global Market Penetration: High scalability potential within the DeFi sector.

?? 4.4 Threats

• Regulatory Risks: DeFi remains an unregulated space, and new regulations could impact the model’s adoption.

?? 5. ROI and Other Metrics

• ROI Estimate: 30% return within the first two years, based on projected user growth and platform fees.
• KPIs for ROI:Cost per integration: Keep under €20,000 per DeFi platform.
• User Growth: Increase by 50% in year two.
• Revenue from Platform Fees: Achieve €200,000 in platform fees within the first year.

?? 6. Budget

• Development Costs: €300,000
• Cloud Infrastructure: €80,000
• Marketing and Legal: €70,000
• Contingency: €50,000

Total Budget: €500,000

?? 7. Risk Management

• Technical Risks: Challenges with blockchain integration and ML model accuracy. Mitigation: Rigorous testing and fallback systems.
• Economic Risks: Fluctuations in DeFi market demand. Mitigation: Diversified DeFi platform partnerships.
• Regulatory Risks: Regulatory changes may slow adoption. Mitigation: Build compliance features for future adaptability.

?? 8. Tech Stack

• Machine Learning: Python (TensorFlow, Scikit-Learn)
• Blockchain: Ethereum, Polkadot
• Database: BigQuery for off-chain data
• APIs: Oracles for off-chain data integration
• Platform: AWS and Azure for cloud infrastructure

?? 9. Stakeholders

• Project Sponsor: DS Inc. Executive Team
• Product Owner: DeFi platform representatives
• Scrum of Scrums Master: Dimitris Souris
• Tech Leads: AI & Blockchain specialists
• Development Teams: 5 Scrum teams focusing on different layers of the project

?? 10. Development

?? 10.1 Scrum Teams Breakdown

• Team 1: Machine learning model development.
• Team 2: Blockchain and Oracle integration.
• Team 3: Frontend development for DeFi platform integration.
• Team 4: Backend system and APIs.
• Team 5: Testing and deployment.

?? 11. Sprint Planning

• Sprint Duration: 2 weeks per sprint
• Total Sprints: 14 sprints over the course of 7 months
• Sprint 1-4: Feasibility study, tech stack setup
• Sprint 5-8: ML model development and training
• Sprint 9-12: Blockchain integration and testing
• Sprint 13-14: Final testing, optimization, and deployment

?? 12. KPIs for Monitoring of Project and Teams

?? 12.1 Project-Level KPIs

• Sprint Burndown Rate: Measure how quickly tasks are being completed during each sprint.
• Velocity: Track the amount of work completed by teams per sprint.
• Defect Rate: Number of defects found and resolved during development.
• Project Completion Rate: Measure the percentage of project milestones completed on time.

?? 12.2 Team-Level KPIs

Team 1 (Machine Learning):

• Model Training Time: Target under 2 hours for each iteration of model training.
• Accuracy of Credit Score Models: Above 85% accuracy.

Team 2 (Blockchain Integration):

• Blockchain Latency: Ensure data validation occurs in under 5 seconds.
• Oracle Integration Speed: Each oracle setup should take less than 1 week.

Team 3 (Frontend Development):

• User Interface Load Time: Less than 2 seconds.
• User Acceptance Testing Success Rate: Aim for 95% success rate in each sprint.

Team 4 (Backend and API Development):

• API Response Time: Ensure responses under 200 milliseconds.
• API Uptime: Target 99.9% availability.

Team 5 (Testing and Deployment):

• Automated Test Coverage: Ensure 90% of the codebase is covered by automated tests.
• Deployment Success Rate: Target 100% success for deployment to staging environments.

?? 12.3 Scrum of Scrums Master (Dimitris Souris) KPIs:

• Cross-Team Communication Efficiency: Track the number of resolved inter-team blockers per sprint.
• Dependency Management: Measure the time taken to resolve cross-team dependencies.
• Sprint Alignment: Ensure all teams meet their sprint goals, with a target alignment rate of 90%.

?? 13. Architecture Design

The project will follow a modular architecture with clear separation between the machine learning models, blockchain components, and user interface layers.

• Layer 1: Machine learning models for credit scoring.
• Layer 2: Blockchain for transaction validation and scoring transparency.
• Layer 3: API layer for communication between ML models and DeFi platforms.

?? 14. Data Flow

1. User data from DeFi platforms is collected via oracles.
2. Machine learning models process data to assess credit scores.
3. Blockchain stores validated credit scores, ensuring decentralized integrity.
4. APIs send scores back to DeFi platforms for real-time decision-making.

Layered Architecture Design and Data Flow Diagram (DFD):