Key information

Duration: 3 days
Programme dates:
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to
Location:
Address
Imperial College Business School, London, United Kingdom
Fees: £3,490

Course overview

AI and Machine Learning for Financial Services is a three-day programme 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.

Through this immersive, hands-on programme you will gain an understanding of the fundamentals of AI and machine learning and how they 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 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

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Find out more about this course and if it is right for you.
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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

The IT Organisation: Operationalising AI & ML
Deeph Chana, Professor of Practice at Imperial College Business School
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?

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

Day 2

Machine Learning for Portfolio Management
Paolo Zaffaroni, Professor in Financial Econometrics at Imperial College Business School
Understand how AI and Machine Learning solutions are improving investment decisions, allowing organisations to manage their financial assets, reducing costs and ultimately increasing revenues.

Machine Learning for Risk Management
Enrico Biffis, Associate Professor of Actuarial Finance at Imperial College Business School
Risk management is a complex system. We will be looking at dependency patterns and the importance of capturing their sequential evolution.

Machine Learning for Systematic Strategies
Andrea Buraschi, Professor of Finance at Imperial College Business School
We answer the question of how Big Data and Commodity Trading Advisor (CTA) are shaping the investment landscape.

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.

Regulatory Risks of AI Products: A pensions example
David Miles, Professor of Financial Economics 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 using a pensions example.

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

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 AI and Machine Learning in Financial Services 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.

 

Professor Paolo Zaffaroni

Paolo Zaffaroni

Peolo Zaffaroni is Professor in Financial Econometrics at Imperial College Business School. He has a summa cum laude degree in economic statistics from Roma and holds a PhD in Econometrics from the London School of Economics. He is also teaching at the University of Rome La Sapienza and has previously taught at the London School of Economics and at the University of Cambridge.

Enrico-Biffis

Enrico Biffis

Enrico Biffis is an Associate Professor of Actuarial Finance at Imperial College Business School, a fellow of the Pensions Institute London, a member of the Munich Risk and Insurance Center at LMU Munich, and an editor of ASTIN Bulletin – The Journal of the International Actuarial Association.

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.

Andrea-Buraschi

Andrea Buraschi

Andrea Buraschi is a Professor of Finance at Imperial College Business School. Andrea’s research interests are in the fields of financial economics, asset pricing and derivatives, and financial econometrics. With an extensive career in academia and advising organisations, Andrea has received many awards in different areas of research and teaching excellence.

David Miles

David Miles

David Miles is a Professor of Financial Economics at Imperial College Business School, and was Chief UK Economist of Morgan Stanley bank from October 2004 to May 2009. He was appointed to the Bank of England's Monetary Policy Committee (MPC) from 2009 to 2015. In 2016 he was appointed by Her Majesty's Treasury to advise on the measurement and reporting of yields on UK government debt. His report was completed in October 2016 and is being implemented. He is chair of the board of trustees of the Institute for Fiscal Studies and a trustee of the Centre for Economic Policy Research. He is a member of Council of Economic Advisors to Her Majesty's Treasury.

Dr.-Pierre-Dangauthier

Pierre Dangauthier

Pierre is the head of Quantitative Analytics at Smarkets, one of the leading betting exchanges. He is specialised in systematic market-making and machine learning. Pierre was previously a VP on Credit Suisse’s bond e-trading desk and a member of Barclays Wealth’s quantitative analytics team. Pierre designs statistical models and machine learning algorithms with application to financial markets. His domain of expertise is a combination of modern applied statistics, computer sciences and financial engineering.

Key information

Duration: 3 days
Programme dates:
to
to
Location:
Address
Imperial College Business School, London, United Kingdom
Fees: £3,490
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Jose Rosario
José Rosário
Director of Business Development

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