Artificial Intelligence (AI) in Banking
A practical introduction to investment principles, financial instruments, and portfolio strategies for beginners and intermediate learners.
Course Overview
Artificial Intelligence (AI) has been widely adopted across diverse industries, driving transformative advancements throughout supply and value chains. Its applications span from recommender systems and smart assistants to chatbots, classifiers, and predictive engines. Today, recommender systems effectively deliver the right product to the right customer at the optimal time, while smart assistants have become integral to daily life. Similarly, chatbots are revolutionizing customer service, classifiers are detecting fraudulent activities, and predictive engines are anticipating credit defaults with remarkable accuracy.
In parallel, the rapid growth of social media usage has heightened the need for organizations to analyze trends and customer sentiments. Natural Language Processing (NLP) offers powerful solutions in this domain, while data visualization tools play a critical role in extracting meaningful insights from vast organizational datasets.
This training program equips participants with the knowledge and skills to harness AI technologies within the banking sector. Participants will explore how recommender systems, chatbots, classifiers, and predictive engines can create substantial value for financial institutions.
Key areas of focus in the Artificial Intelligence in Banking training course include:
Data analysis and visualization
Customer clustering and segmentation
Machine learning for credit default prediction and fraud detection
Natural Language Processing (NLP)
Chatbots and smart assistants
Course Objectives
By the end of this Artificial Intelligence in Banking training course, participants will learn to:
Develop a credit default predictor
Develop a fraud detection system
Develop a recommender system
Develop a customer segmentation system
Build a chatbot that assists customers
Course Audience
his training course is intended for professionals interested in solving problems in the Banking sector using Artificial Intelligence.
This Artificial Intelligence (AI) in Banking training course is suitable for a wide range of professionals but will greatly benefit:
Risk managers
Marketing managers and professionals in the Banking sector
Computer programmers who intend to understand the applications of Artificial Intelligence in Banking
Technologists and researchers interested in Banking and Artificial Intelligence
Customer service managers and professionals in the Banking sector
Senior corporate Leaders, Managers, and Department Heads in the Banking sector
Course 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
Course Outline
Day 1: Foundations of Artificial Intelligence
Introduction to Artificial Intelligence
Artificial Intelligence and Machine Learning concepts
Typical applications across industries
System architecture overview
Software tools for AI development: Python, R, WEKA
Day 2: Data Analytics and Visualization
Data collection and preparation
Feature engineering techniques
Statistical analysis methods
Data visualization for insights
Dimensionality reduction approaches
Day 3: Supervised and Unsupervised Learning
Principles of similarity estimation
Clustering methods and customer segmentation
Association rules for pattern discovery
Recommender systems in practice
Classification models: K-Nearest Neighbors, Decision Trees, Naïve Bayes
Introduction to Artificial Neural Networks
Day 4: Natural Language Processing (NLP)
Structuring information from raw text
Regular expressions for text processing
Word features and semantic analysis
Text classification techniques
Information extraction methods
Question-answering systems
Day 5: Building Intelligent Chatbots
Extracting meaningful information from conversations
Chatbots as interactive search engines
Natural Language Understanding (NLU)
Natural Language Generation (NLG)
Developing and deploying a complete chatbot system