? AI-Driven Loan Approval and Credit Risk Management System

? AI-Driven Loan Approval and Credit Risk Management System Project Plan using the SAFe Framework Scrum Master: Dimitris Souris Teams Involved: 5

? 1. Project Overview This project aims to develop an AI-based system that automates loan approvals and enhances credit risk management. It will streamline the decision-making process for financial institutions, using machine learning models to assess borrower creditworthiness, fraud detection, and risk exposure.

? 2. Development Phases

■ Discovery Phase

• Define scope, key requirements, stakeholders.
• Perform initial feasibility study.
• Outline high-level system architecture.

■ Design Phase

• UX/UI design for dashboards and loan processing portals.
• AI model design and data flow architecture for credit scoring.
• Design API integrations for loan processing, risk assessments.

■ Development Phase

• Implement front-end, back-end, AI model integration.
• Build API services for real-time data processing.
• Develop iterative features during sprints.

■ Testing Phase

• Conduct unit, integration, and system testing.
• Validate AI model accuracy and performance metrics.

■ Deployment and Maintenance Phase

• Launch system for real-time processing.
• Monitor system, train models continuously for risk evaluation.

? 3. Tech Stack

■ Front-End: ReactJS, HTML5, CSS3

■ Back-End: Python (Flask, Django), Node.js

■ Machine Learning: Python (TensorFlow, PyTorch, Scikit-Learn)

■ Data Storage: PostgreSQL, MongoDB

■ Cloud Infrastructure: AWS (EC2, S3, Lambda)

■ CI/CD Tools: Jenkins, Docker, Kubernetes

■ Security: Vault, SSL, OAuth2

■ Monitoring: Prometheus, Grafana

? 4. Budget Estimation (in Euros)

■ Development Costs: €600,000 ■ Cloud Hosting: €100,000/year ■ AI and Data Tools: €50,000 ■ Security & Compliance: €30,000

■ Total Budget: €780,000

? 5. Feasibility Study

■ Economic Feasibility This AI-based solution will result in significant cost savings, reducing manual review time and lowering operational costs. The ROI is expected in 18 months due to improved loan processing efficiency and more accurate credit risk evaluations.

■ Technical Feasibility The project leverages modern AI technologies, cloud services, and scalable infrastructure to handle increasing volumes of loan applications. Using AWS, the system can quickly scale with growing demand.

? 6. Market Analysis

■ Target Market: Medium to large-sized financial institutions, lending companies. The AI FinTech sector is expected to grow 19% annually by 2025, creating a huge demand for automated credit risk systems.

? 7. SWOT Analysis

• Strengths: Automation of loan approvals and risk management.Faster decision-making with AI-backed scoring.
• Weaknesses: Initial high cost for infrastructure and AI model training.
• Opportunities: Expansion into international markets.Integrating fraud detection to widen service offerings.
• Threats: Regulatory challenges in credit lending standards.Competition in AI-based financial services.

? 8. Sprint Planning

■ Sprint 1-2

• Set up project repository, design initial data pipeline.
• Identify key user stories for credit scoring model.

■ Sprint 3-5

• Develop machine learning models for credit risk evaluation.
• Set up APIs for integration with external data sources.

■ Sprint 6-8

• Implement loan approval UI and dashboard.
• Integrate AI engine for real-time loan decision processing.

■ Sprint 9-12

• Testing and optimization of the system’s response time.
• Perform feedback loop and refine AI models.

? 9. Risk Management

■ Technical Risks

• Data privacy concerns and AI model reliability. Mitigation: Implement strict GDPR compliance, create backup validation strategies.

■ Market Risks

• Low adoption by financial institutions due to trust issues. Mitigation: Focus on market education, early customer feedback, and success stories.

? 10. Data Flow

■ Data Input Layer

• Ingest borrower financial histories, external credit reports, and customer application data.

■ Processing Layer

• Apply AI models to analyze data in real-time, generating risk scores.

■ Output Layer

• Display credit risk scores on a dashboard for loan officers and provide automated loan approval recommendations.

? 11. Architecture Design

■ Data Source Layer

• Collect data from multiple sources like banks, credit agencies, customer profiles.

■ AI Processing Layer

• Centralize machine learning models for predictive analytics and risk scoring.

■ Output & Decision Layer

• Connect to dashboards and reporting systems for decision-making and loan processing approval.

Layered Architecture Design:

Here is the UML class diagram illustrating the system's structure: