Module 1: Machine 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.

Module 2: 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.

Module 3: 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.

Module 4: 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.

Module 5: Operationalising AI & ML

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.

Module 6: The IT Organisation - How to Enter the World of 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.

Module 7: AI/ML Challenges and Opportunities 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.

Foundations of Risk Finance Theory
Robert Kosowski, Associate Professor of Finance at Imperial College Business School

• Risk and Risk Aversion
• Portfolio Theory and Basic Portfolio Mathematics
• Efficient Frontier
• Introduction to CAPM
• Multi-factor models
• Basic Capital Structure

This module deals with two fundamental problems in finance and risk management:
Robert Kosowski, Associate Professor of Finance at Imperial College Business School

• How to allocate capital across multiple investments
• How to evaluate and monitor portfolio risks
• Portfolio Allocation across multiple assets: Risk/return tradeoffs
• Optimal portfolio allocation: Expected utility maximization, efficient frontier
• Asset liability management and liability driven investing (LDI)
• Evaluating and monitoring risks
• The capital asset pricing model (CAPM): A model of risks and expected returns. Performance measures and risk-adjustments. Multi-factor models.

Financial Markets and Instruments
Robert Kosowski, Associate Professor of Finance at Imperial College Business School

• Bond Pricing
• Futures and Forwards
• Options and Swaps
• Recent developments in OTC and Exchange-traded Derivatives
• How to manage interest rate risk

Bond pricing and interest rates
Robert Kosowski, Associate Professor of Finance at Imperial College Business School

• Markets and pricing methods
• Start with the simplest securities: bonds
• Default-free bonds: Spot rates, coupon bonds, zeros,
• Yields and yield curves
• Forward rates, No-arbitrage pricing
  Motivation: bonds and interest rates are the simplest products and are the basis for all derivative pricing. Develop no-arbitrage methods for pricing, which will be extensively used to price more complicated products.

Introduction to Option and Swap Markets
Robert Kosowski, Associate Professor of Finance at Imperial College Business School

• Options contracts and markets, – Option pricing models
• Swaps: Interest Rate Swaps
• Case: Expand on ALM in context on Bank One Corp case study and interest rate risk management
• How to manage currency risk (forwards, futures, currency and fx swaps)

Market Risk Management
Enrico Biffis, Associate Professor of Actuarial Finance at Imperial College Business School

• Overview of Market Risk Management
• Risk measures for different asset classes
• Portfolio Risk Measures
• Value at Risk (VaR) and Expected Shortfall
• Analytical VaR Models
• Decision criteria and preferences for risk
• Stylized features of asset returns
• Risk measures: Value at Risk and Expected Shortfall examples

Analytical VaR case studies
Enrico Biffis, Associate Professor of Actuarial Finance at Imperial College Business School

• Modeling the conditional distribution
• Portfolio VaR limits
• Risk limits in the carbon capture lab
• Re-set provisions in agricultural insurance programs

Portfolio VaR case studies
Enrico Biffis, Associate Professor of Actuarial Finance at Imperial College Business School

• Modelling dynamic correlations
• Market and credit risk co-movement
• Capital requirements in insurance

Market Risk Management 
Enrico Biffis, Associate Professor of Actuarial Finance at Imperial College Business School

• Nonparametric VaR Models
• Monte Carlo VaR Models
• Modeling complex portfolios
• Backtesting and stress testing
• Risk measures beyond market risk

Other approaches to the computation of risk measures
Enrico Biffis, Associate Professor of Actuarial Finance at Imperial College Business School

• Standard, weighted, and filtered Historical Simulation
• Monte Carlo simulation
• Scenario analysis and stress testing
• Modeling a “perfect storm” in asset management

Practical insights from a Chief Risk Officer in the non-financial sector
John Ludlow, CEO of AIRMIC (Association of Insurance and Risk Managers in Industry and Commerce)

• Managing and measuring key risks in the corporate sector
• Quantitative risk management tools for daily decision making
• The CRO explains how to manage and measure the key risks in the corporate sector
• Managing reputation risk
• How to use quantitative risk management tools for daily decision making
• Explanation of how the risk management function informs board level decision making

Credit Risk Management
Damiano Brigo, Chair in Mathematical Finance at Imperial College London

•  Single name Credit products: Bonds, CDS
• Market implied default probabilities and correlations
• Firm Value Models: Merton, Black Cox and AT1P models.
• Case study of Calibration on Lehman Brothers CDS with firm value models
• Intensity Models: Constant, time inhomogeneous and stochastic spreads
• Case study of Calibration of Lehman with intensity models
• Multi-name credit products: Default Baskets and CDOs
• Correlation and Dynamic loss models
• CDOs and the credit crisis of 2007-2008
• Defaultable Bonds and Credit Default Swaps (CDS)
• Market implied default probabilities and recovery rates.

Firm Value Models
Damiano Brigo, Chair in Mathematical Finance at Imperial College London

• Merton Model and Black Cox Model.
• AT1P model and Bond/CDS Calibration
• Case study with Lehman Brothers CDS in 2007-2008
• Extension to random default barrier and fraud risk

Intensity Models
Damiano Brigo, Chair in Mathematical Finance at Imperial College London

• Intensity as local default probability or credit spread
• Analogies between survival probabilities and bonds
• Constant, time-varying or stochastic credit spreads
• The JCIR++ model and CDS calibration
• Case study with Lehman Brothers CDS in 2007-2008

Multi-name Products and Models
Damiano Brigo, Chair in Mathematical Finance at Imperial College London

• Default Baskets, Credit Indices and CDOs. iTraxx and CDX pools.
• Hints at Copula models, implied correlation and dynamic loss models
• CDOs and the credit crisis 2007-2008. Where now?

Counter-Party and Funding Risk
Damiano Brigo, Chair in Mathematical Finance at Imperial College London

• Credit valuation adjustments (CVA)
• Debt valuation adjustments (DVA)
• New Regulatory requirements in CVA and centralized counter-parties
• Re-hypothecation and closeout rules
• Funding Valuation Adjustment
• Lehman Brothers Case Study
• Hedging Counterparty risk
• Hints at Capital Valuation Adjustment

Counterparty and Funding Risk
Damiano Brigo, Chair in Mathematical Finance at Imperial College London

Introduce CVA, DVA, FVA, KVA, and XVA

• A consistent and comprehensive framework for counterparty credit and funding risk
• Credit valuation adjustments (CVA), Incorporating CVA into Pricing
• Debit Valuation Adjustment (DVA) and regulatory conflict. DVA as funding benefit?
• Closeout. First to default risk. Payout risk for CVA/DVA.
• Value at Risk of CVA?

• CVA for interest rate swaps, commodities, credit and equity products
• Hints at Funding valuation Adjustments and global valuation problems
• Funding as aggregation dependent and nonlinear. No “law of one price”
• Capital valuation adjustment (KVA)
• Toward XVA
• Lessons from Historical Case Studies: Lehman Brothers and other examples
• Implementation of counter-party and funding risk: Hedging counterparty Risk