?? Project Title: Building AI Models for Real-Time Credit Risk Assessment in Fintech

?? 1. Framework: Scrum Ban

? - Scrum Ban combines the structure and iterative nature of Scrum with the flexibility of Kanban.

? - Scrum focuses on predefined sprints and ceremonies, while Kanban ensures smooth, continuous flow of work.

? - Key Framework Elements:

• Backlog Management: Organized backlog with priorities established by product owner and development teams.
• Work-In-Progress Limits (WIP): Ensuring no task overload in each sprint cycle.
• Daily Stand-Ups: 15-minute daily meetings to sync teams and address blockers.
• Sprint Reviews and Retrospectives: After each sprint, review progress and improve processes.
• Task Flow: Visualized on a Kanban board, tracking tasks from “To-Do” through “In Progress” to “Done.”
• Continuous Delivery: Release of updates and features whenever they are ready.

?? 2. Project Overview

? - Objective: To create a real-time AI-driven credit risk assessment tool, enabling fintech companies to instantly evaluate the risk of extending credit to individuals and businesses based on multiple data sources (e.g., transactional, behavioral, historical credit data).

? **- Scope:

• In-Scope: Data ingestion pipelines, AI model development, API integrations, UI dashboard for decision-makers, and deployment to cloud environments.
• Out-of-Scope: Non-credit-related financial models, long-term maintenance beyond initial product deployment.

? **- Key Deliverables:

1. AI-powered credit risk assessment engine: Capable of real-time credit scoring.
2. Data pipelines: Handling large volumes of data, including transactional, historical, and behavioral data.
3. User-friendly dashboard: Providing instant credit risk insights for decision-makers.
4. Integration API: Allowing the credit engine to be plugged into various fintech platforms.

? **- Stakeholders:

• Internal: Data science team, development team, project managers, QA engineers.
• External: Fintech companies, credit agencies, data vendors, regulatory bodies.

? **- Teams:

• Data Science Team (Model development).
• DevOps and Cloud Infrastructure Team.
• UI/UX Design Team (Dashboard development).
• QA/Test Automation Team.
• Project Management (Delivery Lead Dimitris Souris).**

?? 3. Timeline

? Phase 1: Initiation and Planning (Month 1)

• Project charter creation.
• Stakeholder identification.
• High-level architecture discussion.
• Team onboarding.

? Phase 2: Data Collection and Preparation (Months 2-3)

• Identify key data sources (e.g., financial transactions, credit histories).
• Build ETL pipelines.
• Clean and preprocess data.

? Phase 3: AI Model Development and Training (Months 4-6)

• Feature engineering and selection.
• Develop machine learning algorithms (e.g., Random Forest, Neural Networks).
• Train models using historical data.
• Regular testing and validation of model performance.

? Phase 4: Testing and Validation (Months 7-8)

• Run the models against test data.
• Fine-tune models for accuracy, precision, recall.
• Conduct A/B testing with different models and datasets.

? Phase 5: Deployment and Integration (Months 9-10)

• Deploy AI models in production environments.
• Integrate with fintech platforms via RESTful APIs.
• Ensure end-to-end testing from data ingestion to output.

? Phase 6: Monitoring and Maintenance (Ongoing)

• Continuous monitoring of model performance.
• Regular updates to the models based on changing credit behaviors.
• Bug fixes, patches, and incremental updates.

?? 4. Objectives

? - Build scalable AI models to accurately assess credit risk in real time.

? - Integrate with existing fintech platforms, ensuring seamless data flow and accessibility.

? - Provide reliable decision-making tools to fintech companies for better credit risk management.

? - Optimize model accuracy, precision, and real-time data processing.

?? 5. Scope Statement

? In-Scope:

• Data pipelines.
• AI model training, validation, and testing.
• User interface for fintech users.
• API integrations.

? Out-of-Scope:

• Long-term maintenance beyond one year.
• Features unrelated to credit risk (e.g., loan optimization, portfolio management).

?? 6. Feasibility Analysis

? Economic Feasibility:

• ROI: Significant reduction in default rates expected due to early detection of high-risk borrowers.
• Revenue Streams: Licensing AI models to fintech companies and financial institutions.
• Cost: Major costs include development, cloud hosting, and ongoing maintenance.

? Technical Feasibility:

• Data Availability: Adequate data sources are available for training models.
• Infrastructure: Scalable cloud infrastructure using AWS/Azure to manage real-time data processing.
• Expertise: Skilled AI, data science, and DevOps teams available to execute the project.

?? 7. Market Analysis

? Industry Trends:

• AI and machine learning adoption is increasing in fintech, with a strong focus on real-time data analytics.
• Fintechs and banks are increasingly moving to AI models to assess creditworthiness due to faster, data-driven decisions.

? Target Market:

• Fintech companies that offer credit products (loans, credit cards, BNPL).
• Banks and other financial institutions.

? Competitive Landscape:

• Major competitors include traditional credit scoring agencies (e.g., Experian, Equifax) and AI-first credit companies.

? SWOT Analysis:

• Strengths: Real-time assessment, AI-powered accuracy, scalable architecture.
• Weaknesses: High initial investment in infrastructure.
• Opportunities: Growing fintech sector and demand for real-time decision-making tools.
• Threats: Competition from large, established players; regulatory hurdles.

?? 8. Budget (Detailed Breakdown)

?? 9. Forecasting Metrics for Project Management

? Cost Performance Index (CPI): Measures cost efficiency—CPI = Earned Value / Actual Cost.

? Schedule Performance Index (SPI): Measures schedule efficiency—SPI = Earned Value / Planned Value.

? Burn-Down/Burn-Up Charts: Track completed work versus remaining work.

? Earned Value (EV): Evaluates project performance in terms of scope, schedule, and cost.

? Planned Value (PV): Baseline budget for the work that should have been completed.

? Actual Cost (AC): Actual expenses incurred for the work performed.

?? 10. Development Phases

? Phase 1: Data Ingestion and Preparation:

• Create data pipelines for collecting real-time transactional data from different sources.
• Clean and preprocess data to ensure accuracy and consistency.

? Phase 2: AI Model Development:

• Select key features (financial behaviors, credit history, transactions).
• Develop machine learning models (e.g., Gradient Boosting, Neural Networks).

? Phase 3: Model Training and Evaluation:

• Train models using supervised learning algorithms.
• Test models for precision, recall, and F1 score.

? Phase 4: Integration with Fintech Platforms:

• Develop APIs to integrate the models with fintech systems.
• Build a real-time dashboard for users.

?? 11. Sprint Planning

? Sprint Duration: 2-week sprints.

? Sprint 1: Data Collection Setup:

• ETL process, clean data.

? Sprint 2: Initial Model Development:

• Baseline models.

? Sprint 3-4: Model Training:

• Hyperparameter tuning, training.

? Sprint 5: Testing:

• Test results and optimize models.

?? 12. KPIs for Monitoring Progress

? Velocity: Average story points completed per sprint.

? Lead Time: Time from when a story is picked up to when it’s completed.

? Cycle Time: Time taken to complete tasks within a sprint.

? Burn-down Chart: Shows remaining work versus time.

? Model KPIs: Accuracy, precision, recall, F1 score, ROC-AUC.

?? 13. Risk Management

? Risks:

• Data Quality: Inaccurate or incomplete data may hinder model performance.
• Model Inaccuracy: Poor model performance leading to wrong decisions.

? Mitigation:

• Regular data audits.
• Rigorous model validation processes.

?? 14. Architecture

? System Components:

• Data Ingestion: Kafka for streaming data.
• Processing: Spark for large-scale data processing.
• Modeling: TensorFlow and PyTorch.
• Storage: AWS S3 for storing raw data; PostgreSQL for structured data.
• API Integration: REST APIs for system communication.

?? 15. Data Flow

? Data Source: Transactional and financial data.

? ETL Process: Extract, transform, and load raw data.

? Model Training: Data fed into AI models.

? Prediction: Real-time credit score prediction via API.

? Output: Results displayed on fintech dashboards.

?? 16. Tech Stack

? Languages: Python, R, SQL.

? ML Frameworks: TensorFlow, Scikit-learn.

? Data Storage: AWS S3, PostgreSQL.

? Cloud Infrastructure: AWS, Azure.

? DevOps Tools: Docker, Kubernetes.

System architecture and data flow:

?? 17. Conclusion

? This project, led by Dimitris Souris, aims to deliver a scalable AI credit risk solution with real-time decision-making capabilities, leveraging modern tools and methodologies. Through Scrum Ban’s flexibility and continuous improvement, this project is well-positioned to drive innovation in fintech.