Just before the end of 2019, we shared with you our ranking of last year's best talks from EGG — the human-centered AI conference which brought together over 3,000 people across 6 cities around the hot topics of machine learning interpretability, bias, fairness, and more.
Since we're in the middle of awards season anyway, we decided to take it a step further and break down our top EGG talk picks by industry. We're starting with the banking and finance sector, where we've had a number of great presentations from industry leaders and experts, on topics and use cases ranging from AI ethics and stakeholder management to cyber risk analytics and GANs for finance. So while you're waiting for the Oscars, why not check them out?
Here is our take on the 7 best 2019 EGG talks for banking and finance:
#7 Organisation + Humans + Data — Building a Highly Effective People Analytics Team at Barclays
In this talk, Jesús Rogel covers the journey of bringing together data science and behavioral science to an organization such as Barclays. During the past 12+ months, Barclays have enhanced their HR function by bringing new skills to the mix, ranging from reporting through to analytics and data science. The creation of a People Analytics team has allowed the business to make decisions combining their expertise with cutting edge machine learning techniques.
#6 Ethical Enterprise AI — A Guideline or Compass?
In his insightful speech, Martin Leijen gives a comprehensive account of how Rabobank determines and protects their privacy and ethical standards, as well as how financial institutions can we effectively maintain a firm commitment to moral and ethical standards while at the same time encouraging a strong drive to optimize business opportunities and profitability.
#5 How to Build a Data Science Service Center of Excellence
What does it really take to build and run a functioning Center of Excellence for self-service analytics in a global financial services firm? In his talk, Nicholas Bignell, the Director of Data Science at UBS, shared his real-life insights on how he set out to create a Center of Excellence that comprises both self-service data preparation and machine learning.
#4 Generative Adversarial Networks for Finance
Today, Generative Adversarial Networks (GANs) are the new golden standard for simulation. It has worked wonders in image generation, but can it be applied to option pricing? In this talk, Alexandre Hubert — Dataiku's lead data scientist, tells the story of how two data scientists (including a former trader) deployed a GAN for option pricing in real-time, in just 10 days.
#3 Stakeholder Management: A Data Scientist's Perspective
Data science today entails much more than building a prototype model and hoping it proves useful. Making the business case for additional data sources, prioritizing against other projects and convincing stakeholders of the long term value provided are often equally if not more important than the actual technical work. Denis Fragkakis, Enterprise Data Science Lead at IG, showcases examples of disruptive projects where successful stakeholder management was the key to realizing the business value of data, and discusses how the lessons from those can be applied to any project.
#2 A Glance at ADA: Aviva’s Algorithmic Decision Agent
Aviva looks to leverage its vast data resources to drive world class customer data science into action. Dr Rumble's works has been involved in the development of ADA, Aviva’s Algorithmic decision agent, a customer first AI that is powering omni-channel hyper personalised marketing. ADA is a supervised machine learning model ensemble that utilises Aviva’s Customer data and XGBoosting methods to provide predictions of our customer’s next best actions. Not only does this project showcase the power of big data to provide predictions of the future behaviour of our customers, it also demonstrates the full life cycle of a fintech project, from inception, design, delivery of a mvp.
#1 Formula for Success: AI + Human Intuition = Differentiated Insights
Jeff McMillan is responsible for all aspects of Morgan Stanley Wealth Management’s strategy to leverage analytics, data, and artificial intelligence to drive growth and efficiencies across the wealth management business. In his talk, he makes a compelling case for extracting the most value out of augmenting decision making rather than simply automating it, relying on human insights and intuition, and fostering a culture of collaboration, introspection, and innovation.