AI in Retail Finance
Introduction to key Machine Learning methods in finance
Scope of the Course
Get a grip on the disruptive forces that are enabling new, customer centric and lower-cost financial services.
Who is the intended audience for the course?
People in finance and allied industries who want to understand, implement or build new technology-driven financial services.
What is the need for this course, and why will participants benefit from it?
According to CapGemini, FinTech could add $512 billion to the global revenues of financial services firms by 2020.At the core of the FinTech revolution is Machine Learning - the ‘engine room’ of AI.
The aim of Machine Learning for Finance is to enable you to understand what kinds of Machine Learning algorithms can be applied to different kinds of practical financial scenarios.
The scenarios that we'll explore are:
- Insurance claims partitioning
- Customer segmentation
- Predicting interest rate rises
- Predicting interest rate values
- Predicting the value of stocks
- Portfolio development
- Finding market drivers
- Predicting market movements
The main Machine Learning methods that we'll use are:
- Random Forest Regression and ClassificationARIM
- LSTM Neural Network Regression
- K-means Clustering
- Principle Component Analysis
- Mean-Variance Portfolios
What are the learning outcomes?
At the end of this course, you will have acquired the following:
Skills
- Spot opportunities for AI and Machine Learning in your organisation
- Use key Machine Learning methods
- Apply Machine Learning to practical problems
Knowledge
- Understand the benefits and limitations of Machine Learning
- Key mathematical and coding concepts
- How Machine Learning works within a finance context
Capabilities
- Analyse and evaluate key Machine Learning capabilities
- Be able to frame Machine Learning opportunities
- Guide the development of a Machine Learning-based solution
Tools
- A development ‘sandbox' that you can use and customise after the course
- Starter code and data
- Data and model visualisation
Your Instructor
Mike’s passion is to simplify Deep Tech to enable organisations grow, and help people to learn.
In September 2013, after a long and successful career at Microsoft, Mike founded what was to become learn-tech.io - a company dedicated to democratising Deep Tech, and helping people develop technical, scientific, mathematics, engineering and business knowledge and skills. learn-tech.io now has customers for its products and services in UK, Singapore, China and Australia.
Mike started his career in automotive engineering, spent 10 years teaching and at Secondary and University levels, then worked at Microsoft for 13 years before founding his current business. His experience spans the full spectrum of technology - from AI to Zero Emissions solutions.
Learn-tech.io provides custom learning and technology solutions to organisations such as Pearson, Intel, Microsoft, ARM, BBC, Singapore Science Centre, RM Plc, Box Hill Institute, Burges Salmon and the Bristol Technology and Engineering Academy.
The learn-tech.io organisation includes partners in Asia, and “Deep Tech Task Force” - a team of interns focussed on simplifying a wide range of aspects of Deep Tech from Neural Networks, to FinTech, to Law and Public Policy.
Mike is the author of the “AI Demystified” and “How to Make a Mind?” courses. He has delivered keynote speeches across the world on a range of technology related subjects and has worked at senior level in over 30 countries.