Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Financial Services sessions

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9:0012:30 Tuesday, 30 April 2019
Data Engineering and Architecture
Location: Capital Suite 10
Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
The enterprise data management space has changed dramatically in recent years, and this had led to new challenges for organizations in creating successful data practices. In this presentation we’ll provide guidance and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects. Read more.
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9:0017:00 Tuesday, 30 April 2019
Location: Capital Suite 13
Nicolette Bullivant (Santander UK Technology), Ivan Danesi (UniCredit Services S.C.p.A.), Charlotte Werger (Van Lanschot Kempen), Yoav Einav (GigaSpaces), Yiannis Kanellopoulos (Code4Thought), Romi Mahajan (KKM Group), Rashed Iqbal (Investment and Development Office)
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.
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13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15
Francesca Lazzeri (Microsoft), Aashish Bhateja (Microsoft)
Time series modeling and forecasting has fundamental importance to various practical domains and, during the past few decades, machine learning model-based forecasting has become very popular in the private and the public decision-making process. In this tutorial, we will walk you through the core steps for using Azure Machine Learning to build and deploy your time series forecasting models. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Sami Niemi (Barclays)
Predicting transaction fraud of debit and credit card payments in real-time is an important challenge, which state-of-art supervised machine learning models can help to solve. Barclays has been developing and testing different solutions and will show how well different models perform in variety of situations like card present and card not present debit and credit card transactions. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Alun Biffin (Van Lanschot Kempen), David Dogon (Van Lanschot Kempen)
In this talk we describe how machine learning revolutionized the stock picking process for portfolio managers at Kempen Capital Management by filtering the vast small-cap, investment universe down to a handful of optimal stocks. Read more.
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14:5515:35 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Eitan Anzenberg (Flowcast AI)
Machine learning applications balance interpretability and performance. Linear models provide formulas to directly compare the influence of the input variables, while non-linear algorithms produce more accurate models. We utilize "what-if" scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as LIME. Read more.
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16:3517:15 Wednesday, 1 May 2019
Case studies, Strata Business Summit
Location: Capital Suite 12
Maurício Lins (everis consultancy UK), Lidia Crespo (Santander UK)
Big data is usually regarded as a menace for data privacy. However, with the right principles and mind-set, it can be a game changer to put customers first and consider data privacy an inalienable right. Santander UK applied this model to comply with GDPR by using graph technology, Hadoop, Spark, Kudu to drive data obscuring and data portability, and driving machine learning exploration. Read more.
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17:2518:05 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 7
Ted Malaska (Capital One)
In the world of data it is all about building the best path to support time/quality to value. 80% to 90% of the work is getting the data into the hands and tools that can create value. This talk will take us on a journey of different patterns and solution that can work at the largest of companies. Read more.
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17:2518:05 Wednesday, 1 May 2019
Teresa Tung (Accenture Labs), Jean-Luc Chatelain (Accenture)
How do enterprises scale moving beyond one-off AI projects to making it re-usable? Teresa Tung and Jean-Luc Chatelain explain how domain knowledge graphs—the same technology behind today's Internet search—can bring the same democratized experience to enterprise AI. Beyond search applications, we show other applications of knowledge graphs in oil & gas, financial services, and enterprise IT. Read more.
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11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Charlotte Werger (Van Lanschot Kempen)
This talk discusses a best practice use case for detecting fraud at a financial institution. Where traditional systems fall short, machine learning models can provide a solution. Sifting through large amounts of transaction data, external hit lists, and unstructured text data we managed to build a dynamic and robust monitoring system that successfully detects unwanted client behavior. Read more.
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11:1511:55 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 10/11
Eoin O'Flanagan (Newday), Darragh McConville (Kainos)
In this session you will learn how we have built a high-performance contemporary data processing platform, from the ground up, on AWS. We will discuss our journey from legacy, onsite, traditional data estate to an entirely cloud-based, PCI DSS-compliant platform. Read more.
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11:1511:55 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Sandeep U (Intuit)
Teams today rely on tribal data dictionaries which is a mixed bag w.r.t. correctness -- some datasets have accurate attribute details, while others are incorrect & outdated. This significantly impacts productivity of analysts & scientists. Existing tools for data dictionary are manually updated and difficult to maintain. This talk covers 3 patterns we have deployed to manage data dictionaries. Read more.
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16:3517:15 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Brennan Lodge (Goldman Sachs), Jay Kesavan (Bowery Analytics LLC)
Cyber security analysts are under siege to keep pace with the ever-changing threat landscape. The analysts are overworked, burnout and bombarded with the sheer number of alerts that they must carefully investigate. To empower our cyber security analysts we can use a data science model for alert evaluations. Read more.