Download the Future is Now - Machine Learning X

Machine Learning: the Future is Now
Anthony de Fazekas
Partner, IP strategist, IP lawyer, patent agent, trademarks agent
October 26th, 2016
Machine Learning
Fintech Conference
Connecting Industry Problems With Machine Learning Start Up Innovations
Overview
• Where we are in the FinTech/AI innovation curve?
• How do the AI milestones map to FinTech innovation
opportunities?
• Key trends in applying AI to FinTech
• Best practices for AI related innovations for financial institutions
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Most popular AI applications in FinTech
1. Risk assessment
2. Financial analysis
3. Investment/portfolio management
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From BBC News on Monday, October 24th
Could AI replace judges and lawyers?
24 October 2016 Last updated at 09:46 BST
An artificial intelligence system has correctly predicted the verdicts
of cases heard at the European Court of Human Rights, with a 79%
accuracy.
Lead researcher Dr Nikolaos Aletras, from University College
London, told the Today programme the system would not spell the
end of judges but could be used to prioritise cases most likely to
involve human rights violations.
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Where to begin?
Where do you channel your resources?
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Our perspective on AI/FinTech
At the nexus of AI innovations
• Research institutions
• AI emerging businesses
• Innovative FIs
• VCs investing in AI
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AI/FinTech: an exponential innovation
October, 2016
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Worry less about what AI/FinTech project you are working on
or that you pick
It is more important that you are working on, or start working
on soon, a couple of AI/FinTech projects
Let me explain why
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“Computers are useless.
They can only give you answers.”
Pablo Picasso
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Precepts of AI based FinTech innovations
1. There is no obvious short list, AI opportunities are evolving
2. AI systems are costly and complex
3. Modern underlying systems are required
4. “Open collaboration” is required
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Key best practices for AI based FinTech innovation
• Build up your innovation capacity
• Build up your team’s applied, AI innovation capacity
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Identifying potentially meaningful AI applications
Knowing how to identify requirements, and express these to machine
learning experts for example
Distilling subject matter expertise to enable development of specific AI
functionality
Configuring, training and/or testing an AI application
Formulate standard AI development criteria
Follow possible regulatory developments
Consider cyber risk implications of AI applications
Watch for technical standards
Thank you!
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