Key information

Duration: 3 days (on-campus)
Programme dates:
17 -
Location:
Address
Imperial College Business School, UK
Fees: £3,490

Key information

Duration: 4 weeks (virtual, part-time)
Programme dates:
11 May -
Location:
Address
Virtual, Study online
Fees: £2,200

AI & machine learning in financial services course overview

Imperial Artificial Intelligence (AI) & Machine Learning in Financial Services programme is a three-day course that explores the role of emerging algorithmic techniques on financial decisions. Drawing on knowledge from Imperial College Business School faculty, industry experts, case studies and your peers, you will apply key elements of artificial intelligence (AI) and machine learning to your organisation, increasing the efficiency and accuracy of decision making.

Knowledge of machine learning in finance

Through this immersive, hands-on training programme, you will gain an understanding of the fundamentals of AI and machine learning and how aspects such as big data apply to financial functions such as fraud detection, lending processes, asset management, risk assessment, regulatory compliance and beyond.

You will walk away prepared to implement what you’ve learnt, ensuring your organisation is maximising the value of its live and historic data.  

Who should attend?

This programme is designed for professionals wanting to learn about AI in finance and working in the financial services industry, including members of the exchanges and regulatory agencies, and executives who make decisions that affect financial results. To get the most from the three intensive days, you will need a good grounding in finance and statistical techniques. 

 

Learning outcomes

  • Gain a good understanding of the main concepts of AI and machine learning

  • Understand how to operationalise AI and machine learning

  • Be able to identify key areas to apply AI and machine learning techniques within your teams and workplace

  • Be able to appreciate the advantages that AI and machine learning techniques can add to various portfolio and risk management strategies

Become an Associate Alumni

Take your partnership with Imperial College Business School to the next level by becoming an Associate Alumni. Complete more than eight days (or 56 hours) of our programmes to claim 'Associate Alumni' status and join our active alumni community.

This on-campus programme accounts for 3 days worth of training time.
This virtual programme accounts for 28 hours worth of training time.

Administrative label
Machine Learning modules

Programme content

Before you arrive

Upon registration, you will be able to access our online hub. Here you will find a range of reading and activities to prepare you before you arrive with us on campus. The Hub will also provide you with the opportunity to meet fellow participants virtually, kickstarting your networking.

Day 1

Overview, Expectations & Introductions
Russell Miller, Director of Learning Solutions & Innovations at Imperial College Business School

Machine Learning Fundamentals
Marcin Kacperczyk, Professor of Finance at Imperial College Business School
Marcin will introduce some fundamental concepts and methodologies of machine learning. We start with the big picture before exploring in more depth some of the key concepts that will support your learning on Day 2.

Machine Learning Fundamentals
Marcin Kacperczyk, Professor of Finance at Imperial College Business School
Continuation of the morning session

Regulatory Risks of AI Products
Deeph Chana, Professor of Practice at Imperial College Business School
As the European Commission pledges to attempt to regulate AI, experts are sceptical about how to frame the AI legal framework. In this session, David will explain the impact of AI regulation for financial service firms.

Network Drinks
Join your colleagues at the end of day 1 for a chat over drinks and canapés

Day 2

Operationalising AI & ML (spread over two sessions)
Dr Alex Ribeiro-Castro, Data Scientist and Senior Teaching Fellow at Imperial College Business School
The next two sessions give you the opportunity to experience engaging an organisation in an AI & ML initiative. What works best when it comes to getting buy in? What are some of the rocks in the road you’ll need to avoid? You will be working with your peers and our panel of experts in an experiential activity that will give you the confidence to operationalise what you learn on the programme

Understanding the challenges and opportunities of implementation
Dr Alex Ribeiro-Castro, Data Scientist and Senior Teaching Fellow at Imperial College Business School
In this session you will learn how major companies and financial services organisations are operationalising AI & ML. Through case studies and peer group discussion, understand where the opportunities and challenges might lie for your organisation.

AI & ML: The Big Idea
Dr Alex Ribeiro-Castro, Data Scientist and Senior Teaching Fellow at Imperial College Business School
This session builds on your understanding of the fundamentals and focuses on the strategic aspects of AI and ML and current applications of AI & ML in financial services. Understand the directional momentum of AI in financial services and which AI applications are currently driving the most impact. The session ends by signposting how all of this is relevant for you in your own role, right now.

Day 3

Deep Learning Algorithms
Pierre Dangauthier, Head of Quantitative Analytics at Smarkets
We explore the importance of using available data in Machine Learning and make the case for the role of human guidance and intuition in its application.

The IT Organisation: Operationalising AI & ML
Pierre Dangauthier, Head of Quantitative Analytics at Smarkets
The barriers to entry are much lower than assumed (and getting lower). What are the main requirements your technology and IT teams need to consider?

Investing in AI – The next big thing?
London is one of the global hot spots for FinTech and tech funding. The adoption of disruptive and innovative technologies such as the application of AI/ML has attracted the top tier investors and FinTech founders. Discover where the smart money is going and meet active investors in this space.

Reflection & Consolidation & Certificates
Russell Miller, Director of Learning Solutions & Innovations at Imperial College Business School

Programmes content

Orientation Week

Teaching sessions begin on 20th October 2020. You will have access to the learning platform from 13th October, 2020. There will be no formal teaching between these dates, though you will have the opportunity to familiarise yourself with the learning platform and prepare for the programme.

Week 1

Maching Learning Fundamentals

Explore fundamental concepts and methodologies of machine learning. Get a big picture before going in more depth some of the key concepts that will support your future learning.

Regulatory Implications of AI

As the European Commission pledges to attempt to regulate AI, experts are sceptical about how to frame the AI legal framework. This session explains the impact of AI regulation for financial service firms.

PD Drop in Session

In this session there will be Q&A with the Programme Director, giving you the opportunity to discuss topics explored in the programme and consolidate your learning.

AI & ML: The Big Idea

This session builds on your understanding of the fundamentals and focuses on the strategic aspects of AI and ML and current applications of AI & ML in financial services. Understand the directional momentum of AI in financial services and which AI applications are currently driving the most impact. The session ends by signposting how all of this is relevant for you in your own role, right now.

Week 2

Understanding the challenges and opportunities of implementation

In this session you will learn how major companies and financial services organisations are operationalising AI & ML. Through case studies and peer group discussion, understand where the opportunities and challenges might lie for your organisation.

Operationalising AI & ML (spread over two sessions)

The next two sessions give you the opportunity to experience engaging an organisation in an AI & ML initiative. What works best when it comes to getting buy-in? What are some of the rocks in the road you’ll need to avoid? You will be working with your peers and our panel of experts in an experiential activity that will give you the confidence to operationalise what you learn on the programme.

The IT Organisation: Operationalising AI & ML

The barriers to entry are much lower than assumed (and getting lower). What are the main requirements your technology and IT teams need to consider? We also explore the importance of using available data in Machine Learning and make the case for the role of human guidance and intuition in its application.

PD Drop in Session

In this session there will be Q&A with the Programme Director, giving you the opportunity to discuss topics explored in the programme and consolidate your learning.

Investor Panel

The adoption of disruptive and innovative technologies such as the application of AI/ML has attracted the top tier investors and FinTech founders to London. Discover where the smart money is going, and meet active investors in this space.

Programme faculty

Deeph Chana

Deeph Chana

Deeph has extensive experience of working on world leading STEM in academia, industry and government and brings a wealth of knowledge and expertise to Imperial College London's executive education programmes. He is the Programme Director of Imperial AI & Machine Learning in Financial Services Programme and Professor of Practice within Imperial College Business School. Deeph is also Deputy Director of the Institute for Security Science Technology, co-founder of the UK-Government funded Research Institute in Trustworthy Industrial Control Systems and Imperial's FinTech Network of Excellence.

 

Marcin Kacperczyk

Marcin Kacperczyk

Marcin Kacperczyk is a Professor of Finance at Imperial College Business School. Marcin has extensive experience in the field of machine learning and has recently been appointed as research consultant to the European Central Bank. His research interests include artificial intelligence, machine learning, money markets, mutual funds, social responsibility, empirical asset pricing, behavioural finance and decision theory.

Pierre

Pierre Dangauthier

Pierre is a portfolio manager at Tower Research Capital. He was previously the head of trading at Smarkets, a leading betting exchange. He is specialized in systematic trading and machine learning. Pierre also worked as a vice president on Credit Suisse’s bond e-trading desk, and as a quant at Barclays. His domain of expertise is a combination of modern applied statistics, software development and financial engineering. Pierre holds a Ph.D. in Bayesian machine learning from INRIA and a computer science and applied mathematics MSc. from Ensimag (France).

Alex Ribeiro-Castro

Dr Alex Ribeiro-Castro

Dr Ribeiro-Castro holds a MA and Ph.D. in Mathematics from the University of California (Santa Cruz), and held a professorship in Mathematics from the Pontifical Catholic University (PUC-Rio) in Rio de Janeiro. He was previously a Visiting Lecturer in the Applied Mathematics Department at Imperial College London and has accumulated over 10 years of teaching experience across the globe, in diverse subjects in pure and applied mathematics. With over five years of consultancy experience, Dr Ribeiro-Castro has made contributions in various industries, including machine learning and optimisation problems in FinTech, health, energy, and more recently in retail. Some of his most recent industry partners include KPMG, Quantum Black/McKinsey, Ion Trading (finance), Ovo Energy/Kaluza (energy), DoctorLink (health), and more recently Otravo BV (retail). He is currently working as a technical adviser for a pioneering data science project in maritime insurance.

Key information

Duration: 3 days (on-campus)
Programme dates:
17 -
Location:
Address
Imperial College Business School, UK
Fees: £3,490

Key information

Duration: 4 weeks (virtual, part-time)
Programme dates:
11 May -
Location:
Address
Virtual, Study online
Fees: £2,200

Contact us

Get in touch

Jose Rosario
José Rosário
Programme Advisor

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