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Available dates

Jan 29—31, 2020
3 days
London, United Kingdom
GBP 3490 ≈USD 4509
GBP 1163 per day
May 13—15, 2020
3 days
London, United Kingdom
GBP 3490 ≈USD 4509
GBP 1163 per day


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About the course

Machine Learning and AI have been with us for longer than most of us would imagine – and well before banking apps, PFMs and chat bots were in all the news, but it’s only recently that their application and importance to banking and financial services has come to the fore.

Whether it is back, middle or front office machine learning plays a key role across the financial services industry from fraud detection to the lending process, asset management to risk assessment, regulatory compliance and beyond.

The vast amount of highly accurate live and historical data held by financial institutions are valuable assets, but they are not being fully understood or exploited in decision making processes. As new fintech entrants enter the market focusing on customer experience and build out predictive capabilities, it is now more important than ever to understand where the potential threats are coming from and where the opportunities to partner, collaboration or compete lie.

We’ll explore these technologies, business use cases, case studies and key learnings in order to give you a solid grounding in AI, big data, and machine learning as well as help you understand the potential to apply them in your own organisation.

Some of the areas we’ll cover include:

  • Portfolio management
  • Algo trading/Robo advisory
  • Loan underwriting
  • Risk management
  • Fraud detection
  • Regulatory compliance

Course Learning Outcomes

  • Apply the course learning and AI/ML theory in your business or team.
  • Begin to develop a strategic and tactical plan for you and your teams, taking the learning from the possible to the practical, working in active learning groups.
  • Understand the thinking behind VC investment and the challenges in the relationship between commercial growth and engagement for FinTechs and getting a return for investors.

Programme content

Day 1

Morning – An overview of AI and Machine Learning in Finance

  • Intro to AI – What it is (and isn’t); a brief history; cutting through the jargon
  • Intro to Big Data – Collecting it, using it, managing it; using it to enhance performance
  • Intro to Machine Learning – What it is; An overview to structures and techniques
  • Types of Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning

Critical Use Cases in AI and Machine Learning in Financial Services

  • Trading: use of alternative data, sentiment analysis for alpha generation, optimal execution
  • Banking: on-boarding, credit scoring, recommender engines, bots
  • Payments: fraud prevention, reconciliation, processing
  • Cybersecurity

Case Studies

  • Trends in the Adoption and Deployment of Machine Learning and AI
  • Regulatory and Technology Constraints
  • AI Strategy
  • Supply and Demand Factors Influencing the Adoption of AI and Machine Learning in Financial Services


  • Technology (Computing power, data availability, algorithms, costs)
  • Financial (infrastructure and data to apply new techniques)


  • Profitability (cost reduction, revenue gains, better risk management)
  • Competition (one upmanship in FI’s)
  • Regulation (AML, KYC, reporting, execution)

Afternoon – Risk management and credit: How to use AI for efficiency in risk rating

  • Counterparty risk
  • Credit scoring
  • Anti-money laundering
  • Insurance

Day 2

Morning – Investors

Behavioural finance: understanding financial agents based on their (ir)rationality & emotions.

  • A description of the individuals’ behaviour as investors: theories & case studies
  • How to use data in order to help individuals make decisions
  • Case study: transposing the findings on Tesco to the financial industry

Afternoon – Investment

Understanding the new trends to generate performance

  • Making decisions of qualitative and quantitative signals
  • Techniques that improve performance and can help portfolio managers
  • From a classical to a big-data approach
  • Case study

Day 3

Morning – Active Learning Lab

Data Observatory

Afternoon – Reflection, consolidation, and application

  • Strategic and tactical review
  • Theory into practice
  • Group work and peer to peer learning
  • Action planning
  • The AI/Machine Learning Investor Panel

  • London is one of the global top spots for fintech and tech funding. The adoption of disruptive and innovative technologies such as the application of AI/Machine Learning has attracted the top tier investors and fintech founders. Discover where the smart money is going, meet active investors in this space.

Who should attend

Executives in the financial services industry, including members of the exchanges and regulatory agencies, and professionals who make business decisions that affect the firm’s financial results.

  • Decision makers
  • Portfolio managers
  • Risk managers
  • Wealth management
  • Pension fund managers
  • Insurance companies

Trust the experts

Enrico Biffis

Summary Enrico Biffis is Associate Professor of Actuarial Finance at Imperial College Business School, a fellow of the Pensions Institute in London, and a member of the Munich Risk and Insurance Centre at LMU Munich. His areas of expertise are risk analysis and asset-liability management, with a...


Marcin Kacperczyk

Summary Private Website


Paolo Zaffaroni

Summary   Paolo 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  ha...


Nikolai Hack

Prior to joining Exo as one of the founding team, Nikolai spent a number of years as a management and political consultant, gaining extensive experience across European bluechip clients and international organisations. He has spent the majority of his career focusing on operational excellence and...


Pierre Dangauthier

Pierre Dangauthier is the head of Quantitative Analytics at Smarkets, one of the leading betting exchanges. He is specialized in systematic market making and machine learning. He received a Ph.D. degree in statistical learning in 2007 from INRIA for a joint work with Microsoft Research Cambridge...


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AI & Machine Learning in Financial Services

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