Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Schedule: Financial Services sessions

9:00–17:00 Tuesday, 22 May 2018
Findata Day
Location: Capital Suite 4
Paul Lashmet (Arcadia Data), Anthony Culligan (SETL), Konrad Sippel (Deutsche Börse), Paul Lynn (Nordea), Mikheil Nadareishvili (TBC Bank), Olaf Hein (ORDIX AG), Robert Passarella (Alpha Features), Louise Beaumont (Publicis Groupe | techUK | NPSO), Alistair Croll (Solve For Interesting), Robert Passarella (Alpha Features), Christina Erlwein-Sayer (OptiRisk Systems), Angelique Mohring (GainX), Saeed Amen (Cuemacro), Gisele Frederick (Zingr.io)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.
11:15–11:55 Wednesday, 23 May 2018
Executive Briefing: GDPR—Getting your data ready for heavy, new EU privacy regulations
Executive Briefing, Law, ethics, and governance, Strata Business Summit
 Location: Capital Suite 17 Level: Intermediate
Mark Donsky (Okera), Syed Rafice (Cloudera)
Average rating: ****.
(4.00, 1 rating)
In May 2018, the General Data Protection Regulation (GDPR) goes into effect for firms doing business in the EU, but many companies aren't prepared for the strict regulation or fines for noncompliance (up to €20 million or 4% of global annual revenue). Mark Donsky and Syed Rafice outline the capabilities your data environment needs to simplify compliance with GDPR and future regulations. Read more.
11:15–11:55 Wednesday, 23 May 2018
Leveraging public-private partnerships using data analytics for economic insights
Strata Business Summit
 Location: Capital Suite 15/16 Level: Non-technical
Audrey Lobo-Pulo (Phoensight), Nicholas O'Donnell (LinkedIn)
In October 2017, LinkedIn and the Australian Treasury teamed up to gain a deeper understanding of the Australian labor market through new data insights, which may inform economic policy and directly benefit society. Audrey Lobo-Pulo and Nick O'Donnell share some of the discoveries from this collaboration as well as the practicalities of working in a public-private partnership. Read more.
12:05–12:45 Wednesday, 23 May 2018
Risk-sharing pools: Winning zero-sum games through machine learning
Data science and machine learning
 Location: Capital Suite 10/11 Level: Intermediate
Baiju Devani (Aviva Canada), Etienne Chasse St-Laurent (Aviva Canada)
Average rating: ***..
(3.33, 3 ratings)
Risk-sharing pools allow insurers to get rid of risks they are forced to insure in highly regulated markets. Insurers thus cede both the risk and its premium. But are they ceding the right risk or simply giving up premium? Baiju Devani and Étienne Chassé St-Laurent share an applied machine learning approach that leverages an ensemble of models to gain a distinctive market advantage. Read more.
14:55–15:35 Wednesday, 23 May 2018
Machine learning for time series: What works and what doesn't
Data science and machine learning
 Location: Capital Suite 12 Level: Intermediate
Mikio Braun (Zalando)
Average rating: ****.
(4.40, 15 ratings)
Time series data has many applications in industry, in particular predicting the future based on historical data. Mikio Braun offers an overview of time series analysis with a focus on modern machine learning approaches and practical considerations, including recommendations for what works and what doesn't. Read more.
12:05–12:45 Thursday, 24 May 2018
Machine learning at Intuit: Five delightful use cases
Data science and machine learning, Streaming systems and real-time applications
 Location: Capital Suite 12 Level: Intermediate
Calum Murray (Intuit)
Average rating: *....
(1.50, 2 ratings)
Machine learning-based applications are becoming the new norm. Calum Murray shares five use cases at Intuit that use the data of over 60 million users to create delightful experiences for customers by solving repetitive tasks, freeing them up to spend time more productively or solving very complex tasks with simplicity and elegance. Read more.
12:05–12:45 Thursday, 24 May 2018
Interpretable machine learning products
Data science and machine learning
 Location: Capital Suite 10/11 Level: Intermediate
Mike Lee Williams (Cloudera Fast Forward Labs)
Average rating: *****
(5.00, 2 ratings)
Interpretable models result in more accurate, safer, and more profitable machine learning products, but interpretability can be hard to ensure. Michael Lee Williams examines the growing business case for interpretability, explores concrete applications including churn, finance, and healthcare, and demonstrates the use of LIME, an open source, model-agnostic tool you can apply to your models today. Read more.
14:05–14:45 Thursday, 24 May 2018
Spark NLP in action: Intelligent, high-accuracy fact extraction from long financial documents
Data science and machine learning, Expo Hall
 Location: Expo Hall Level: Intermediate
David Talby (Pacific AI), Saif Addin Ellafi (John Snow Labs), Paul Parau (UiPath)
Average rating: ****.
(4.50, 4 ratings)
Spark NLP natively extends Spark ML to provide natural language understanding capabilities with performance and scale that was not possible to date. David Talby, Saif Addin Ellafi, and Paul Parau explain how Spark NLP was used to augment the Recognos smart data extraction platform in order to automatically infer fuzzy, implied, and complex facts from long financial documents. Read more.
14:55–15:35 Thursday, 24 May 2018
Machine learning platform lifecycle management
Data engineering and architecture, Data-driven business management
 Location: Capital Suite 7 Level: Intermediate
Hope Wang (Intuit)
Average rating: ****.
(4.00, 3 ratings)
A machine learning platform is not just the sum of its parts; the key is how it supports the model lifecycle end to end. Hope Wang explains how to manage various artifacts and their associations, automate deployment to support the lifecycle of a model, and build a cohesive machine learning platform. Read more.
14:55–15:35 Thursday, 24 May 2018
Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench
Data science and machine learning
 Location: Capital Suite 13 Level: Intermediate
Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft)
Average rating: ****.
(4.00, 2 ratings)
Advancements in computing technologies and ecommerce platforms have amplified the risk of online fraud, which results in billions of dollars of loss for the financial industry. This trend has urged companies to consider AI techniques, including deep learning, for fraud detection. Francesca Lazzeri and Jaya Mathew explain how to operationalize deep learning models with Azure ML to prevent fraud. Read more.
16:35–17:15 Thursday, 24 May 2018
Predicting rent arrears: Leveraging data science in the public sector
Data science and machine learning, Emerging technologies and case studies
 Location: Capital Suite 10/11 Level: Beginner
Jonathan Leslie (Pivigo), Tom Harrison (Hackney Council), Maryam Qurashi (Pivigo)
Average rating: *****
(5.00, 5 ratings)
One major challenge to social housing is determining how best to target interventions when tenants fall behind on rent payments. Jonathan Leslie, Maryam Qurashi, and Tom Harrison discuss a recent project in which a team of data scientist trainees helped Hackney Council devise a more efficient, targeted strategy to detect and prioritize such situations. Read more.