AI in Banking: Enhancing Operations and Customer Engagement

Course Info

Date: June-02-2025

Length: 1 Week

City: Dubai

Fees: 3,985

Type: In Classroom

Available Dates

  • Jan-13-2025

    Dubai

  • Apr-07-2025

    Dubai

  • June-02-2025

    Dubai

  • July-14-2025

    Dubai

  • Sep-15-2025

    Dubai

  • Nov-10-2025

    Dubai

Dates in Other Venues

  • Dec-23-2024

    Singapore

  • Dec-30-2024

    Paris

  • Dec-30-2024

    London

  • Dec-30-2024

    Amsterdam

  • Jan-06-2025

    Kuala Lumpur

  • Jan-06-2025

    Istanbul

  • Jan-06-2025

    Paris

  • Jan-06-2025

    Barcelona

  • Jan-06-2025

    Amsterdam

  • Jan-06-2025

    Singapore

  • Jan-13-2025

    London

  • Apr-07-2025

    Amsterdam

  • Apr-07-2025

    Barcelona

  • Apr-07-2025

    London

  • Apr-07-2025

    Paris

  • Apr-07-2025

    Istanbul

  • Apr-07-2025

    Kuala Lumpur

  • Apr-07-2025

    Singapore

  • June-02-2025

    London

  • July-07-2025

    Paris

  • July-07-2025

    Barcelona

  • July-07-2025

    Istanbul

  • July-07-2025

    Singapore

  • July-07-2025

    Kuala Lumpur

  • July-07-2025

    Amsterdam

  • July-14-2025

    London

  • Sep-15-2025

    London

  • Oct-06-2025

    Barcelona

  • Oct-06-2025

    Paris

  • Oct-06-2025

    Amsterdam

  • Oct-06-2025

    Istanbul

  • Oct-06-2025

    Kuala Lumpur

  • Oct-06-2025

    Singapore

  • Nov-10-2025

    London

Course Details

Course Outline

5 days course

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.

Python Basics & Data Exploration


  • Introduction to Python for banking data analysis, highlighting specialised libraries and tools.
  • Techniques for conducting exploratory data analysis (EDA) to extract insights from banking datasets.
  • Hands-on workshop: Participants engage in EDA exercises using real banking datasets.
  • Best practices in data management: Ensuring data quality, security, and regulatory compliance in banking.
  • Discussion on overcoming challenges in data analysis within the banking sector.

Machine Learning Applications in Banking


  • Building Predictive Models: Techniques for developing models to predict loan defaults, credit risks, and market opportunities.
  • Fraud Detection Techniques: Utilizing machine learning to identify and prevent potential fraud in real-time transactions.
  • Hands-On Workshop: Creating machine learning models to solve specific problems in banking, such as customer churn prediction.
  • Model Evaluation and Validation: Ensuring model reliability and accuracy within regulatory compliance frameworks.
  • interactive Q&A: Deep dive into the strategic application of ML in banking.

Advanced AI Applications and NLP


  • Implementing NLP in Customer Service: Enhancing digital customer interactions through chatbots and automated response systems.
  • Deep Learning for Financial Analysis: Using CNNs and RNNs for complex pattern recognition in financial time series data.
  • Hands-On Exercise: Developing an NLP system to handle customer queries and complaints.
  • Case Study Discussion: Review of a deep learning project implemented in transaction analysis or risk management.



Deployment & Future AI Trends

 

  • Best Practices for AI Deployment: Best Practices for AI Deployment: Discuss strategies for deploying AI solutions effectively in the banking sector.
  • Ethical Considerations: Managing bias and ensuring fairness in AI applications.
  • Future Trends in AI for Banking: Exploring emerging technologies and predicting future applications.
  • Final Project Presentations: Participants present their projects, discussing the potential impact and practical applications of their work.
  • Course Recap and Feedback: Summarizing key concepts and gathering participant feedback for continuous improvement.