AI in Banking: Enhancing Operations and Customer Engagement
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Course Details
- Introduction
- Objective
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AI in Banking is fundamentally reshaping financial services through technological innovation and data-driven insights. This comprehensive training course explores how AI transforms critical banking functions such as risk assessment, credit scoring, fraud detection, and customer relationship management.
Participants will engage in practical workshops using Python for data analysis, conducting exploratory data analysis (EDA), and building predictive models tailored to banking applications like loan defaults and customer churn prediction.
The course delves into advanced AI applications including natural language processing (NLP) for automating customer interactions and deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for intricate financial analysis tasks. Case studies and discussions underscore successful AI implementations in banking, reinforcing best practices in data management, regulatory compliance, and ethical AI deployment.
By empowering participants with these skills, the course equips them to drive innovation, improve operational efficiencies, and meet evolving customer demands within the dynamic landscape of the banking industry.
Course Outline
Introduction to AI in Banking
- Overview of AI in Banking: Impact and evolution of AI technologies in the Banking field.
- Key Areas of Application: Exploring how AI is transforming risk assessment, credit scoring, fraud detection, and customer relationship management.
- AI Tools & Technologies: Introduction to the essential tools & technologies behind AI in Banking.
- Challenges and Opportunities: Discussion on data privacy, ethical considerations, and the potential of AI to transform Banking.
- Case Studies: Real-world examples of AI positively impacting Banking outcomes.