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Duration 14 hours
Course Outline
Introduction to Edge AI in Financial Services
- Overview of Edge AI concepts and their specific applications in finance.
- Exploring the advantages and potential challenges of Edge AI in banking.
- Analysis of case studies highlighting successful Edge AI implementations in the financial industry.
Configuring the Edge AI Environment
- Installation and configuration of necessary Edge AI tools.
- Integration of financial data sources and collection systems.
- An introduction to key Edge AI frameworks and libraries.
- Practical exercises focused on setting up the environment.
Fraud Detection Leveraging Edge AI
- Foundational concepts of fraud detection.
- Building AI models for real-time fraud identification.
- Implementing effective anomaly detection systems.
- Hands-on exercises dedicated to fraud detection tasks.
Improving Customer Service with Edge AI
- Overview of customer service standards in financial services.
- AI techniques for delivering personalized customer interactions.
- Developing and deploying AI-driven chatbots and virtual assistants.
- Practical exercises focused on customer service applications.
Risk Management through Edge AI
- Introduction to risk management principles.
- Utilizing AI for real-time risk assessment and mitigation strategies.
- Implementing AI-based decision support systems.
- Hands-on exercises applied to risk management scenarios.
Deploying and Overseeing Edge AI Solutions
- Deploying AI models onto financial edge devices.
- Strategies for monitoring and maintaining Edge AI systems.
- Techniques for troubleshooting and optimizing deployed models.
- Practical exercises on deployment and management tasks.
Key Tools and Frameworks for Financial Edge AI
- Overview of essential tools and frameworks (e.g., TensorFlow Lite, OpenVINO).
- Application of TensorFlow Lite in financial AI projects.
- Hands-on exercises involving optimization tools.
Real-World Applications and Case Studies
- Review of successful financial Edge AI projects.
- Discussion of use cases specific to the industry.
- A capstone project involving the building and optimization of a real-world financial AI application.
Conclusion and Future Directions
Requirements
- A foundational understanding of AI and machine learning principles.
- Practical experience with financial services and fintech applications.
- Basic programming proficiency (Python is recommended).
Target Audience
- Finance professionals.
- Fintech developers.
- AI specialists.
Testimonials (1)
That we can cover advance topic and work with real-life example
