An Interactive 5-Day Training Course

Artificial Intelligence (AI) in Banking

Using AI for Credit Default Prediction, Fraud Detection, and Smart Customer Assistants

22 - 26 Dec 2025
Dubai
| $5950
30 Mar - 03 Apr 2026
London
| $5950
27 - 31 Jul 2026
Barcelona
| $5950
05 - 09 Oct 2026
London
| $5950
21 - 25 Dec 2026
Dubai
| $5950
27 - 31 Jul 2026
Online
| $3950
05 - 09 Oct 2026
Online
| $3950

Introduction

Artificial intelligence has been successfully applied to various fields to create quantum-leap improvements across the entire supply and value chains. Artificial intelligence applications include recommender systems, smart assistants, chatbots, classifiers, and predictive engines. Recommender systems are currently able to recommend the right product to the right person at the right time. Smart assistants are part of everyday life. Chatbots are becoming more and more common in customer service applications. Classifiers can detect fraud, and predictive engines can predict credit defaults. 

With the ever-increasing use of social networks by customers, organizations are more inclined to analyze trends and sentiments. Natural Language Processing provides suitable solutions to this problem. Moreover, data visualization tools are becoming crucial to gain insights from the huge amount of data available to organizations.

The participant to this training course will learn how to apply artificial intelligence to the banking sector. In particular, the participant will learn how recommender systems, chatbots, classifiers, and predictive engines can provide value in the banking sector.

This GLOMACS Artificial Intelligence (AI) in Banking training course will highlight:

Key Learning Outcomes

By the end of this Artificial Intelligence in Banking training course, participants will learn to:

Training Methodology

Participants in this Artificial Intelligence in Banking training course will receive thorough training on the subjects covered by the course outline with the Tutor utilising a variety of proven adult learning teaching and facilitation techniques. Training methodology includes combining a presentation of the main concepts and hands-on practical exercises to be completed by the participant.

Artificial Intelligence (AI) in Banking

Who Should Attend?

The organization will ensure that the participant employees know the latest developments in Artificial Intelligence and how it can solve business problems.

Organisations will:

  • Learn to identify opportunities for applying Machine Learning to solve business problems
  • Learn to use the most commonly employed software in AI and data analytics
  • Learn to build prediction systems
  • Learn to build customer segmentation and recommender systems
  • Learn to build and effectively use chatbots
  • Learn how Natural Language Processing could be used to solve business problems

Learning Journey Breakdown

  • Artificial Intelligence and Machine Learning
  • Typical applications
  • The architecture of a system
  • Software tools: Python
  • Software tools: R
  • Software tools: WEKA
  • Data gathering
  • Feature engineering
  • Statistical analysis
  • Data visualization
  • Dimensionality reduction
  • Similarity estimation
  • Clustering
  • Association rules
  • Recommender systems
  • K-Nearest Neighbors
  • Decision Trees
  • Naïve Bayes
  • Artificial Neural Networks
  • Extracting structure from raw text
  • Regular expressions
  • Word features and semantics
  • Text classification
  • Information extraction
  • Question answering systems
  • Extracting information from conversations
  • Chatbot as a search engine
  • Natural Language Understanding
  • Natural Language Generation
  • Building a system

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