HMRC SAS Innovation Day

Wednesday 15^th May 2019
The Royal Society, 6-9 Carlton House Terrace,
St James's, London, SW1Y 5AG

Adopting AI & Machine Learning for Government

In an era of data evolution, advanced analytics, Artificial Intelligence (AI) and Machine Learning (ML) empowers government departments like HRMC to address challenges of budget, efficiency and manpower.

HMRC produce, collect and store an unprecedented amount of increasingly diverse data that could be used to transform the face of the department, service delivery and the outcomes for citizens.

Please join us for the HMRC SAS Innovation day, to understand the potential of HMRC data and how the rapid emergence of AI & ML will enable the department to tap into bold new insights, make better decisions and do more with less.

Reserve Your Seat

Speakers

Iain Brown
Head of Data Science, SAS UKI

Roderick Crawford
Head of Public Sector, SAS UKI

Joseph Musolino
Government Fraud & Security Intelligence Strategy Lead

Matthew Stainer
Principal Data Scientist

Tuba Islam
Principal Data Scientist

Agenda

Wednesday 15^thMay
11.30 - 12.00:	Registration, Tea, Coffee & Pastries
12.00 - 12.10	Welcome to HMRC SAS Innovation Day	Roderick Crawford, Head of Public Sector
12:15 – 12:45	Five Lessons Learnt from AI & ML Deployment We will cover real-world applications of AI and Machine Learning deployments detailing both the results and more importantly the actionable lessons learnt. understand the critical factors that will make your AI & ML strategies successful have a feel for what AI could do to improve outcomes within HMRC.	Dr Iain Brown, Head of Data Science
12:50 – 13:50	Networking Lunch and hands-on AI & Analytics in Action Demos
13.50 - 14.20	How are computers becoming more image-conscious? How do you apply maths to photos, you may wonder? Computer vision is one the key AI applications for SAS; whether that be classifying images, detecting objects in images or simply assessing changes in images. Increasingly so, Public sector are seeking how to apply these techniques to previously unused data sources and drive innovation. This session will look at how they are achieving this and give real world examples of how Computer Vision is being leveraged within Governments to analyse customs cargo, scrape data from the dark web and more! enhance existing BAU processes and models, drive net-new innovation.	Tuba Islam, Data Scientist
14.25 - 14.55	Customer Case-study: Using Natural Language Processing to further improve customer satisfaction at Nationwide Nationwide has started to use Natural Language Processing (NLP) techniques, to further improve its already industry-leading customer satisfaction. Nationwide set out to investigate why their members get in touch and find better ways to resolve their enquiries. Successful projects to improve understanding of member contact has led to an increased demand for NLP based analytics across the societies broader text footprint. In addition, we will cover NLP success stories from other SAS customers including RBS and British Airways.	Matthew Stainer, Principal Data Scientist
15.00 - 15.30	Smarter Governments Powered by Data	Joseph Musolino, Government Fraud & Security Intelligence Strategy Lead
15.30 - 16.00	Panel Discussion: The Future of Analytics in Tax	Joseph Musolino, Government Fraud & Security Intelligence Strategy Lead Paul Jones, Chief Technology Officer, SAS
16.00 - 17.00	Networking & Drinks

The Royal Society
6-9 Carlton House Terrace, St James's, London, SW1Y 5AG

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Successful execution of an analytics, and thereby AI strategy, needs the right balance of choice and control.

Choice

Creativity and innovation flourish in open spaces. You need flexibility and freedom to attract the best analytical talent, use a wide variety of techniques and develop processes that work best. You need the flexibility to use multiple programming languages and analyze any data in any environment and to keep up with accelerating demands.

Control

Analytics chaos can creep up. Once you lose control of your data, you lose trust in the system and its outputs. Transparency, governance and security become essential for maintaining trust in models and analytical results. Becoming even more critical as you scale development, monitoring and refinement of analytics applications and their associated processes.

At the intersection of data, software, and ingenuity, the future is being redefined. After all, when curiosity meets capability, progress is inevitable.