AI in Banking: Enhancing Operations and Customer Engagement

Available Dates

Dec-30-2024

Paris
Jan-27-2025

Paris
Feb-24-2025

Paris
Mar-24-2025

Paris
Apr-28-2025

Paris
May-26-2025

Paris
June-23-2025

Paris
Aug-25-2025

Paris
Sep-22-2025

Paris
Oct-27-2025

Paris
Nov-24-2025

Paris
Dec-22-2025

Paris

Dates in Other Venues

Dec-09-2024

Barcelona
Dec-09-2024

Dubai
Dec-16-2024

Istanbul
Dec-16-2024

Kuala Lumpur
Dec-23-2024

Singapore
Dec-30-2024

Amsterdam
Dec-30-2024

London
Jan-06-2025

London
Jan-06-2025

Amsterdam
Jan-13-2025

Dubai
Jan-13-2025

Barcelona
Jan-20-2025

Istanbul
Jan-20-2025

Kuala Lumpur
Jan-27-2025

Singapore
Feb-03-2025

Amsterdam
Feb-03-2025

London
Feb-10-2025

Dubai
Feb-10-2025

Barcelona
Feb-17-2025

Istanbul
Feb-17-2025

Kuala Lumpur
Feb-24-2025

Singapore
Mar-03-2025

London
Mar-03-2025

Amsterdam
Mar-10-2025

Barcelona
Mar-10-2025

Dubai
Mar-17-2025

Kuala Lumpur
Mar-17-2025

Istanbul
Mar-24-2025

Singapore
Apr-07-2025

Amsterdam
Apr-07-2025

London
Apr-14-2025

Barcelona
Apr-14-2025

Dubai
Apr-21-2025

Kuala Lumpur
Apr-21-2025

Istanbul
Apr-28-2025

Singapore
May-05-2025

London
May-05-2025

Amsterdam
May-12-2025

Dubai
May-12-2025

Barcelona
May-19-2025

Kuala Lumpur
May-19-2025

Istanbul
May-26-2025

Singapore
June-02-2025

Amsterdam
June-02-2025

London
June-09-2025

Dubai
June-09-2025

Barcelona
June-16-2025

Istanbul
June-16-2025

Kuala Lumpur
June-30-2025

Amsterdam
June-30-2025

London
June-30-2025

Singapore
July-07-2025

Amsterdam
July-07-2025

London
July-14-2025

Dubai
July-14-2025

Barcelona
July-21-2025

Kuala Lumpur
July-21-2025

Istanbul
July-28-2025

Singapore
Aug-04-2025

Amsterdam
Aug-04-2025

London
Aug-11-2025

Dubai
Aug-11-2025

Barcelona
Aug-18-2025

Istanbul
Aug-18-2025

Kuala Lumpur
Aug-25-2025

Singapore
Sep-01-2025

London
Sep-01-2025

Amsterdam
Sep-08-2025

Barcelona
Sep-08-2025

Dubai
Sep-15-2025

Istanbul
Sep-15-2025

Kuala Lumpur
Sep-29-2025

London
Sep-29-2025

Amsterdam
Sep-29-2025

Singapore
Oct-06-2025

London
Oct-06-2025

Amsterdam
Oct-13-2025

Dubai
Oct-13-2025

Barcelona
Oct-20-2025

Kuala Lumpur
Oct-20-2025

Istanbul
Oct-27-2025

Singapore
Nov-03-2025

London
Nov-03-2025

Amsterdam
Nov-10-2025

Dubai
Nov-10-2025

Barcelona
Nov-17-2025

Istanbul
Nov-17-2025

Kuala Lumpur
Nov-24-2025

Singapore
Dec-01-2025

London
Dec-01-2025

Amsterdam
Dec-08-2025

Barcelona
Dec-08-2025

Dubai
Dec-15-2025

Istanbul
Dec-15-2025

Kuala Lumpur
Dec-29-2025

Singapore
Dec-29-2025

London
Dec-29-2025

Amsterdam

Course Details

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.

In today's banking landscape, AI stands as a transformative force, revolutionising core functions such as risk management, fraud detection, customer service enhancement, and financial advising. By the end of this course, participants should be able to:

Understand the key applications of AI in various aspects of banking, including risk management, fraud detection, customer service, and financial advising.
Implement machine learning algorithms to analyze banking data for insights that drive business decisions.
Develop predictive models to forecast market trends and customer behaviour.
Navigate the ethical and regulatory landscape specific to AI applications in banking.
Leverage natural language processing (NLP) to enhance digital customer interactions.

This course caters to a diverse range of professionals within the banking sector eager to deepen their understanding and proficiency in AI applications:

Bank executives and managers seeking to innovate and improve their bank’s services
Financial analysts interested in AI-driven market prediction models
IT professionals in the banking sector
Data scientists working within financial institutions
Regulatory compliance officers focusing on technology applications
Any professional curious about the intersection of AI and banking

Our courses in Paris are held at the LPC office located at:

75 BD Haussmann ,75 Boulevard Haussmann, Paris, 75008

Once you register, we will subsequently send you the course details, including the location, trainer, and other logistical information.

Pay Attention, Please! The course location at our offices is subject to availability. Should our office be unavailable, we will secure an alternative nearby venue and promptly inform you of the change. The exact time and location will be confirmed one week prior to the course commencement.

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

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.

AI in Banking: Enhancing Operations and Customer Engagement

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