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Course Outline
Introduction to Edge AI in the Financial Sector
- Overview of Edge AI capabilities and their application in finance
- Advantages and obstacles associated with adopting Edge AI in banking
- Analysis of successful Edge AI implementations in the financial industry
Configuring the Edge AI Environment
- Installation and configuration of Edge AI tools
- Integration of financial data sources and collection mechanisms
- Introduction to key Edge AI frameworks and libraries
- Practical exercises for setting up the environment
Fraud Detection via Edge AI
- Foundamentals of fraud detection processes
- Creating AI models for real-time fraud identification
- Building and implementing anomaly detection systems
- Practical exercises focused on fraud detection
Improving Customer Service with Edge AI
- Current state of customer service in financial services
- AI methods for facilitating personalized customer interactions
- Deployment of AI-powered chatbots and virtual assistants
- Practical exercises for customer service applications
Risk Management with Edge AI
- Core concepts of risk management
- Leveraging AI for immediate risk assessment and mitigation
- Implementing AI-driven decision support mechanisms
- Practical exercises for risk management workflows
Deployment and Administration of Edge AI Solutions
- Deploying AI models on financial edge devices
- Monitoring and maintaining Edge AI infrastructure
- Diagnosing issues and optimizing deployed models
- Practical exercises for deployment and management tasks
Tools and Frameworks for Financial Edge AI
- Overview of relevant tools and frameworks (e.g., TensorFlow Lite, OpenVINO)
- Applying TensorFlow Lite to financial AI use cases
- Practical exercises utilizing optimization tools
Real-World Applications and Case Studies
- Review of high-profile financial Edge AI projects
- Discussion of use cases specific to the industry
- Capstone project: building and optimizing a real-world financial AI application
Conclusion and Future Steps
Requirements
- Foundational knowledge of AI and machine learning principles.
- Practical experience with financial services and fintech applications.
- Basic programming proficiency (Python is preferred).
Intended Audience
- Finance industry professionals
- Fintech software developers
- AI technical specialists
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
