Machine Learning – Dive into Banking

By focusing on banking-oriented applications, this training emphasises practical, actionable insights rather than theoretical knowledge alone. Participants will engage in hands-on exercises and case studies that mirror real challenges in the financial industry, bridging the gap between learning and doing. This approach ensures that you can apply the skills learned immediately to your professional role, driving growth and innovation in your organisation.

Tailored for busy professionals and driven entrepreneurs, this course is structured to maximise learning within a minimal timeframe. It is an ideal opportunity for those looking to enhance their expertise in machine learning, leverage big data analytics, and gain a competitive edge in the rapidly evolving financial sector.

During this course, participants will

• Develop a fundamental understanding of machine learning concepts, algorithms, and tools, including supervised, unsupervised, and reinforcement learning

• Build skills in data handling, cleaning, and exploratory analysis to prepare banking datasets for machine learning applications

• Apply machine learning to predictive modelling in finance (risk assessment, investment optimisation, customer segmentation)

• Gain exposure to advanced methods such as neural networks and ensemble learning for tackling complex problems

• Consolidate knowledge through hands-on projects and real-world financial case studies.

Module 1 – Introduction to Machine Learning in Finance

• What machine learning is and why it matters in financial services

• Key applications: fraud detection, credit scoring, algorithmic trading, and personalisation

Module 2 – Data Preparation and Exploratory Analysis

• Techniques for data cleaning, handling missing values, and managing outliers

• Using descriptive statistics and visualisation to detect patterns

• Mini exercise: exploring a financial dataset

Module 3 – Core Machine Learning Techniques

• Supervised learning: regression and classification models

• Unsupervised learning: clustering and dimensionality reduction (PCA)

• Avoiding overfitting and ensuring model validation

• Applications in risk modelling and customer segmentation

Module 4 – Decision Trees, Ensembles, and Neural Networks

• Introduction to decision trees, random forests, and boosting

• Basics of neural networks and their role in financial applications

• Practical insights: when to use advanced methods vs. simple models

Module 5 – Tools and Hands-On Implementation

• Overview of key tools: Python, R, scikit-learn, TensorFlow

• Guided exercise: building a simple predictive model with Python

Module 6 – Real-World Case Study & Wrap-Up

• Case study: applying machine learning to solve a banking problem

• Reflections and practical takeaways for immediate workplace application

This course is suitable for Financial Analysts, Start-up Entrepreneurs, Data Scientists and Engineers in Finance, Risk Management Professionals, and IT Professionals in Financial Institutions.

Preliminary knowledge on data analysis and data mining. Having attended the training “Data Mining - Business Oriented Methods for Exploring Big Data” is recommended.