About the program
This course delivers a practical and structured understanding of AI evolution, with a strong focus on banking applications. It covers core AI paradigms, advanced algorithms, generative AI, and deployment practices, enabling participants to design and implement compliant, real-world AI solutions.
New Giza University CampusWhat You’ll Learn
Understand core AI
and machine learning paradigms and their banking applications
Build and apply AI algorithms
for credit, risk, fraud, and customer analytics
Design, Evaluate
and optimize AI models using banking-grade metrics
Implement AI automation
Best practices for integrating AI into legacy banking systems
Prepare AI systems
for production deployment, monitoring, and future scalability
Highlights
End-to-end coverage of AI evolution from core ML paradigms to advanced generative AI
Strong emphasis on real-world banking applications and regulatory compliance
Hands-on workshops, implementation exercises, and practical assessments
Focus on production deployment, monitoring, and performance optimization
Forward-looking perspective on emerging AI technologies and future banking landscapes
Modules
Machine Learning Paradigms in Banking
Introduces supervised, unsupervised, reinforcement, and hybrid learning paradigms. Focuses on selecting and applying appropriate ML approaches to banking operations, customer services, and decision-making within local regulatory contexts.
Algorithms and Applications in Banking
Covers decision trees, random forests, neural networks, and advanced algorithm optimization. Emphasizes building, integrating, and tuning AI models for core banking functions such as credit scoring, fraud detection, and risk assessment.
Automation & AI Agents
Focuses on evaluating AI models using classification and regression metrics, cross-validation techniques, and performance monitoring systems. Enables learners to design robust evaluation and reporting frameworks aligned with banking standards.
GAN Architecture and Implementation in Banking
Explores the design, training, and deployment of Generative Adversarial Networks for banking use cases. Includes synthetic data generation, financial pattern analysis, and production-level system integration and monitoring.
LLM Architecture and Banking Applications
Covers large language model architectures, prompt engineering, bilingual Arabic-English processing, and fine-tuning. Focuses on deploying LLMs securely within banking systems while optimizing performance and compliance.
Emerging Applications of Generative AI in Banking
Examines emerging generative AI use cases, future banking service models, and transformative technologies. Guides learners in assessing market readiness and developing strategic roadmaps for future AI adoption.
Ready to become an AI leader in banking?
Join the program and start transforming the future today.
For inquiries
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