AI & Machine Learning in Financial Services
Machine learning and artificial intelligence are radically going to change the decision making processes in financial institutions. They will impact investment signals in asset and wealth management. They will influence how people analyse concentration, scenario and operational risks. In addition, we will know clients much better than we currently do, leading to a more bespoke, though industrialised service. Our machine learning and AI in finance training course builds a strong foundation in AI, big data, and machine learning to allow you to make better decisions using these evolving techniques. This short course is intended to provide you with an overview of the plethora of techniques of Machine Learning and Deep Learning specifically designed and implemented in a portfolio and risk management context.
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
- Machine Learning
- Neural Networks
- Predictive Analysis
- Probabilistic reasoning
Who should attend this course?
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
Course Learning Outcomes
At the end of this programme you will:
- Have a good understanding of the main concepts of Machine Learning and Big Data
- Understand the investment in hardware needed in your work place as well as the type of profiles that your institution needs to hire in order to be able to implement AI&ML methodologies
- Be able to identify key areas to apply AI & ML techniques within your teams/work place
- Be able to appreciate the advantages that AI&ML techniques can add to various portfolio and risk management strategies
Machine Learning & AI in Finance Content
Machine Learning for Risk Management
Machine Learning for Risk Management (Continued)
Regulatory Implications of Artificial Intelligence
Machine Learning for Systematic Strategies
Morning – Learning goals:
- Credit scoring and loan underwriting (Europe case study)
- Remote sensing and supply chain risk management (China case study)
- Blended finance solutions in agribusiness (East-Africa case study)
Afternoon – Risk management and credit: How to use AI for efficiency in risk rating
- Compliance in Corporate Governance
- Systematic Investing
Machine Learning for Portfolio Management
Group photo & Lunch
Machine Learning for Financial Economics
Morning – Risk Management
Objectives: To understand risk management is part of a community that is a complex system. We will be looking at complex dependency patterns and how important in order to capture their sequential evolution in the case of Machine Learning.
- We will use graphical models to better understand dependencies
- A new approach on systemic risk and counterparty risk
- Case study: the CDS market & contagion
- Other applications of financial analytics to different risk management topics
- Improving forecasts: Feature and observation generation
- Anti-money laundering: Binary Classification Algorithm
- Data versus methods
- Assessing impact of suppliers and buyers on CDS spreads
Afternoon – The IT organisation
How to enter in the world of Artificial Intelligence/Machine Learning? The most important message is that the barrier to entry is much lower than assumed.
The IT organisation required to handle projects efficiently
- Data security and interaction with the cloud
- Bespoke programming versus open architecture code
- Different challenges in the financial world as opposed to elsewhere
- Understand basic trade-offs governing machine learning
- Discuss the most common models of machine learning applied in financial economics
- Discuss the strengths and weaknesses of the approach in the context of return predictability, default risk, and portfolio management
- ML and robot advisory
Deep Learning Algorithms
The AI/ML Investor Panel – Investing in AI: the next big thing?
Programme Wrap Up
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.
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