Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Schedule: Financial Services sessions

13:30–17:00 Tuesday, 9 October 2018
Models and Methods
Location: Buckingham Room - Palace Suite
Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft)
Average rating: ***..
(3.67, 3 ratings)
Buisnesses use forecasting to make better decisions and allocate resources more effectively. Recurrent neural networks (RNNs) have achieved a lot of success in text, speech, and video analysis but are less used for time series forecasting. Join Yijing Chen, Dmitry Pechyoni, Angus Taylor, and Vanja Paunic to learn how to apply RNNs to time series forecasting. Read more.
11:05–11:45 Wednesday, 10 October 2018
AI Business Summit
Location: Blenheim Room - Palace Suite
Ashok Srivastava (Intuit)
Ashok Srivastava explains how to make your organization AI ready, determine the right AI applications for your business and products, and accelerate your AI efforts with speed and scale. Read more.
11:55–12:35 Wednesday, 10 October 2018
Christine Foster (The Alan Turing Institute), Rakshit Kapoor (HSBC)
In 2016, the Alan Turing Institute, the UK’s new national institute for data science and AI, announced a funded strategic multiyear research partnership with HSBC. Christine Foster and Rakshit Kapoor share insights and use cases that emerged while making this ambitious and innovative cross-sector partnership work. Read more.
13:45–14:25 Wednesday, 10 October 2018
Martin Goodson (Evolution AI), Mark Qualter (RBS)
Martin Goodson and Mark St. John Qualter share the results of a yearlong feasibility study on the introduction of AI into the onboarding process at the Royal Bank of Scotland (RBS). Along the way, Martin and Mark share their experiences in translating this complex business process into a high-performance computational system. Read more.
14:35–15:15 Wednesday, 10 October 2018
James Crawford (Orbital Insight)
By some estimates, soon it will require eight million people doing nothing but looking at satellite imagery 24/7 in order to ensure every photo taken on a daily basis is viewed. James Crawford explains how artificial intelligence solves this problem of scale, allowing us to accurately analyze reams of satellite imagery and detect patterns of socioeconomic change in a timely fashion. Read more.
16:00–16:40 Wednesday, 10 October 2018
AI in the Enterprise, Impact of AI on Business and Society
Location: Hilton Meeting Room 3-6
Giorgia Fortuna (Machine Learning Reply)
Many industries, including banking, financial sectors, and insurance, continuously face the problem of detecting fraudulent activities. Giorgia Fortuna explores state-of-the-art innovations in fraud detection and explains how unsupervised ML fits into the picture, focusing on signature checks and face recognition. Read more.
16:00–16:40 Wednesday, 10 October 2018
Implementing AI
Location: Windsor Suite
Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today. Read more.
14:35–15:15 Thursday, 11 October 2018
Models and Methods
Location: King's Suite - Balmoral
Amy Heineike (Primer)
Average rating: ***..
(3.67, 3 ratings)
When building natural language processing (NLP)-based applications, you quickly learn that no single NLP algorithm can handle the wide range of tasks required to turn text into value. Amy Heineike explains how she orchestrates natural language processing, understanding, and generation algorithms to build text-based AI applications for Fortune 500 companies. Read more.
16:50–17:30 Thursday, 11 October 2018
Impact of AI on Business and Society
Location: King's Suite - Balmoral
Johnnie Ball (Fluidly)
Average rating: *****
(5.00, 1 rating)
Cashflow is responsible for 80–90% of UK SME failure. Fluidly uses the wealth of financial data available through APIs to instantly predict cashflow. Johnnie Ball details how the company built an automated cashflow engine, explores the challenges faced in applying AI to financial data, and explains how machine learning can redefine how we think about established approaches to modeling. Read more.