Introduction

Artificial intelligence is reshaping the banking sector by enabling faster decisions, deeper customer insight, and more effective risk management. From fraud detection and credit default prediction to customer engagement through chatbots and personalised recommendations, AI has become a strategic capability rather than a technical experiment. Banks are increasingly expected to leverage data, analytics, and automation to remain competitive while meeting regulatory and governance expectations.

This Artificial Intelligence (AI) in Banking training course provides a practical understanding of how AI technologies are applied within banking environments. It explains how machine learning, natural language processing, and data visualisation can be used to extract value from large data sets, improve operational efficiency, and strengthen customer-focused services. The course bridges analytical concepts with real banking use cases to support informed, responsible adoption of AI.

Key focus areas include:

Key Learning Outcomes

At the end of this training course, participants will be able to:

Training Methodology

This training course follows a practical, hands-on learning approach combining structured explanations with applied exercises and guided demonstrations. Participants engage with real-world banking scenarios to build conceptual clarity and practical confidence in using AI-driven solutions without requiring advanced technical backgrounds.

Artificial Intelligence (AI) in Banking

Who Should Attend?

This training course is ideal for professionals seeking to:

  • Banking and financial services professionals
  • Risk management and compliance specialists
  • Digital transformation and innovation leaders
  • Data, analytics, and business intelligence professionals
  • IT and systems professionals supporting banking operations
  • Managers involved in customer experience and process optimisation

Course Outline

Day 1

Artificial Intelligence Basics

  • Artificial Intelligence and Machine Learning
  • Typical applications
  • The architecture of a system
  • Software tools: Python
  • Software tools: R
  • Software tools: WEKA
Day 2

Data Analytics and Visualization

  • Data gathering
  • Feature engineering
  • Statistical analysis
  • Data visualization
  • Dimensionality reduction
Day 3

Unsupervised and Supervised Learning

  • Similarity estimation
  • Clustering
  • Association rules
  • Recommender systems
  • K-Nearest Neighbors
  • Decision Trees
  • Naïve Bayes
  • Artificial Neural Networks
Day 4

Natural Language Processing

  • Extracting structure from raw text
  • Regular expressions
  • Word features and semantics
  • Text classification
  • Information extraction
  • Question answering systems
Day 5

Building a Chatbot

  • Extracting information from conversations
  • Chatbot as a search engine
  • Natural Language Understanding
  • Natural Language Generation
  • Building a system

Ready to Take the Next Step?

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FAQs

The course focuses on applying AI and machine learning to improve banking decision-making, risk management, and customer engagement.

No, it is designed to be accessible and focuses on understanding, application, and interpretation rather than coding.

Yes, the course addresses predictive analytics for fraud detection and credit default assessment.

Yes, it covers chatbots, recommender systems, and natural language processing in banking contexts.

Yes, it supports informed oversight of AI adoption within regulated banking environments.

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