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Duration 14 hours
Course Outline
Introduction to Edge AI in the Financial Sector
- Overview of Edge AI concepts and their specific applications in finance
- Analyzing the benefits and challenges of adopting Edge AI in banking operations
- Examining case studies of successful Edge AI integrations in the financial industry
Establishing the Edge AI Environment
- Installation and configuration of essential Edge AI tools
- Integration of financial data sources and collection infrastructure
- Introduction to key Edge AI frameworks and libraries
- Practical exercises focused on setting up the development environment
Fraud Detection Utilizing Edge AI
- Fundamental concepts of fraud detection in financial contexts
- Development of AI models capable of real-time fraud identification
- Implementation of advanced anomaly detection systems
- Hands-on exercises dedicated to fraud detection mechanisms
Improving Customer Service Through Edge AI
- Overview of customer service dynamics within financial services
- Application of AI techniques for personalized customer engagement
- Deployment of AI-driven chatbots and virtual assistants
- Practical exercises for building customer service applications
Risk Management with Edge AI
- Introduction to modern risk management strategies
- Leveraging AI for real-time risk assessment and mitigation efforts
- Implementation of AI-powered decision support systems
- Hands-on exercises to practice risk management applications
Deployment and Management of Edge AI Solutions
- Deploying AI models onto financial edge devices
- Ongoing monitoring and maintenance of Edge AI systems
- Troubleshooting and performance optimization of deployed models
- Practical exercises covering deployment and system management
Tools and Frameworks for Financial Edge AI
- Survey of available tools and frameworks (including TensorFlow Lite, OpenVINO)
- Application of TensorFlow Lite for financial AI use cases
- Practical exercises involving optimization tools
Real-World Applications and Case Studies
- Review of successful financial Edge AI projects
- Discussion of industry-specific use cases and scenarios
- Hands-on project to build and optimize a real-world financial AI application
Summary and Recommended Next Steps
Requirements
- A solid grasp of fundamental AI and machine learning principles
- Familiarity with financial services landscapes and fintech application development
- Foundational programming proficiency, with Python being the recommended language
Target Audience
- Finance professionals
- Fintech developers
- AI specialists
Testimonials (1)
That we can cover advance topic and work with real-life example