Artificial intelligence (AI) and machine learning are changing the face of finance and are being rapidly adopted for a range of applications in the financial services industry. Many applications, or “use cases”, of AI and machine learning already exist. The adoption of these use cases has been driven by both supply factors, such as technological advances and the availability of financial sector data and infrastructure, and by demand factors, such as profitability needs, competition with other firms, and the demands of financial regulation. Some of the current and potential use cases of AI and machine learning include:
- Financial institutions and vendors are using AI and machine learning methods to assess credit quality, to price and market insurance contracts, and to automate client interaction.
- Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions.
- Hedge funds, broker-dealers, and other firms are using AI and machine learning to find signals for higher (and uncorrelated) returns and optimise trading execution.
- Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment, and fraud detection.
The Asian Banker is pleased to present during The Future of Finance Summit 2019, a series of workshops, which will create and inspire leaders from all over the world. In this workshop, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. In a world full of uncertainty, the only thing that is certain is the need to evolve.
Delegates attending this one-day workshop will gain complimentary access to the two-day – Future of Finance Summit 2019 – the most influential annual forum that brings together a wide range of players – banks, internet finance companies, asset managers, insurance companies, investors and customers.
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Artificial Intelligence for Everyone - Workshop
Wednesday May 29, 2019
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08:00 - 09:00
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Registration
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09:00 - 10:30
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Fundamentals of AI
Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. During this session you’ll learn:
- Artificial Intelligence for everyone – Past and present
- Definitions: AI, machine learning (including deep learning, reinforcement learning), data analytics, customer science, etc.
- Present-day usage
- AI within the corporate context
- Significance and urgency: Why now, and why you?
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10:30 - 11:00
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Coffee/tea break
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11:00 - 12:30
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AI in the industry – Practical applications and benchmark leaders
- A selection of practical applications
- Statistics, uncertainty and graphical models
- Strategic approaches: Commissioning versus procurement (build or buy?)
- Augmented reality market worth $80bn by 2025
- AI maturity models and the enabling preconditions within your organization to use AI well. By looking at data, skills, tools, metrics, and others.
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12:30 - 13:30
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Lunch
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13:30 - 14:30
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Assessing raw data in your organization to be used for AI projects
- What data assets do you have, where does it live, who owns those systems, where are the gaps, and how could you fill them by (e.g., buying data or installing sensors)?
- What skills do you have access to from your part of the business, and what gaps might there be?
- What tools do you use, where are the gaps, what other options are there?
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14:30 - 15:30
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Selection of projects and assessment for AI use
- Overview of the four key criteria for successful project selection and how to assess them
- Group exercise: Create a project longlist (often categorized broadly within marketing, operational efficiency, operational effectiveness, and commercial optimization)
- Scoring longlist against key criteria and group discussion
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15:30 - 16:00
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Coffee/tea break
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16:00 - 17:00
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How to manage an AI project in your organization
- Similarities and differences
- Project lifecycle and timescales
- Project delivery methodology
- Typical staffing profiles
- Cost estimates for external suppliers (for data science as a service and for consulting)
- Performance metrics
- Operations and maintenance
- Governance and risk management
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17:00
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End of workshop
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