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Registration & breakfast


Registration & breakfast

Chair's opening remarks

Rodrigo Salvaterra Global Business Engineer Officer JP Morgan Chase

Scaling the Data Science Mountain

As organizations become more mature in their use of data science, the need to increase both the number and complexity of data science projects also increases. In this talk, will discuss how organizations can scale their tools, methods, and people in order to meet these growing needs.

Grant Case Customer-Facing Data Scientist and Analytics Architect Dataiku

The Future of Digital Economics & Global Impact of Automation

Hari Eppanapally Co-Founder GITA Fund

An Exciting New Era in Banking: Artificial Intelligence and Machine Learning

Morgan Stanley Research is leveraging Machine Learning, Natural Language Processing, Artificial Intelligence (AI), and Big Data techniques to help businesses discover new information, make better decisions, and work in a more efficient manner. While predictive models built with structured data have a longer history and can be quite effective, there is an enormous amount of information, in unstructured format, previously ignored. With the evolution of AI and Big Data techniques such as deep learning, word2vec, sentiment analysis, and knowledge graph/ontology, we can now extract insights from research reports, emails, earning notes, news, and social media, and apply them to identify new opportunities and further improve business performance.

Kenneth Yu Zhang VP, Lead Data Scientist Morgan Stanley

Networking coffee break

Deliver Analytics On-Demand with Search and AI-Powered Insights

We are rapidly entering the third era of business intelligence, one augmented by machine learning and artificial intelligence. This introduction will cover how to deliver analytics on-demand from your enterprise data with search-driven analytics, automated discovery of insights, and predictive modeling on a scalable high-performance data platform.

Ajay Khanna Founder & CEO Tellius

The Economics of AI today and the Future of Markets

The job market is rapidly changing because of technology and automation. People are concerned about losing their jobs and their sources of income. At the same time, US unemployment rates are at historic low levels not seen in almost 50 years. In this talk, we discuss how AI will transform our economy, jobs, markets, and lifestyle through automation.

Rodrigo Salvaterra Global Business Engineer Officer JP Morgan Chase

Paving the Way to Data-Driven Transformation with 3rd Generation BI

Data has always held tremendous value. But for decades, most of that value has been left on the table. Complexity, technical limits, bottlenecks, and skill gaps have kept insights hidden and analytics out of the hands of business users. With the 3rd generation of BI, that’s about to change.

Paul Van Siclen Sr. Director of Industry Solutions, Financial Services (Global) Qlik

Big Data, Big Risk: Role of Risk Management in the Regulated environment

In a world of increasing complexity and demand, the ability to capture, access and utilize Big Data determines business as well as risk management success. The financial and banking sectors are some of the most heavily regulated industries therefore introducing innovative data solutions, even with the promise of cutting costs and increasing profits is difficult. The variety of possibilities using Big Data is available to grow the banking business but can this be achieved without compromising on transparency and risk associated with it. Hence, what is the expectation from risk management groups in the banks and what does the Big Data evolution mean for risk management?

Neha Gupta Vice President, Credit Risk HSBC

Networking lunch

Panel: How Data Analytics is Driving the Future of Banking

See how financial institutions are utilizing new analytics such as predictive analytics, artificial intelligence (AI), and machine learning to empower employees at all levels to better understand clients’ needs and provide even more personalized services and solutions than bank managers of the past.

Eric Thornton Data Science & Machine Learning Expert Chameleon Metadata
Martin Caupin Data Scientist BNP Paribas
Devanshu Mehrotra VP, Audit Data Analytics MUFG

Driving Holistic Data Driven Business Decisions with Machine Learning & Data Analytics

Join us as we dive into the broad understanding of process flow, techniques and challenges surrounding what it takes to implement a Machine Learning solution within the Financial Industry. We will look at various lines of business where machine learning can bring immediate results and help automate as well as potentially grow the business. We will also take a look at the various implementation phases required as well as common techniques used within the implementation.

Victor Tewari Sr. Technology Officer, Capital Markets Technology, Data Sciences & Data Analytics BMO Financial Group

Networking coffee break

10 Lessons Learned From AI Initiatives in the Financial Services Sector

Data-driven use cases are paving the way for next-generation work streams like artificial intelligence (AI) across the business landscape, and the Financial Services industry is no exception.
To deliver AI @ scale, organizations must consider several major dependencies and challenges, which require them to have a high-level understanding of the technical requirements that an AI project will place on the infrastructure within their organization.
This session shares the 10 lessons learned from AI initiatives in the financial services sector.

Andy Price Financial Services Lead Pure Storage

Building a Data Science Information Architecture

• Components to add to traditional (data/information) architecture to transform it into a Data Science Architecture

Eric Thornton Data Science & Machine Learning Expert Chameleon Metadata

Chair's closing remarks

Rodrigo Salvaterra Global Business Engineer Officer JP Morgan Chase

Networking drinks reception

Networking drinks reception ends at 6.00pm.

Registration & breakfast


Registration & breakfast

Chair's opening remarks

Rodrigo Salvaterra Global Business Engineer Officer JP Morgan Chase

5 Lessons Learned From Teaching Machine Learning in Finance

Meninder(Mike) Purewal Director, Data Scientist & Adjunct Professor New York University

An Application of Graph-based Machine Learning in Anti–Money Laundering

Different approaches have been heavily discussed in Anti–Money Laundering (AML) since the financial crisis, from risk-based approaches to the sophisticated deep learning algorithms; from random forest to anomaly detection. They all focus on the transaction behaviors from unassuming entity relationships since AML programs have historically focused on entity behavior. Graph-based machine learning explores those relationship among the nodes in a graph in a more rigorous and comprehensive way. Let’s explore the possibilities of detecting AML risk by uncovering networks discovered through hidden relationship. Please join us for an introduction of the process and challenges as we explore this new wave of disruption in AML.

Yibei Chen McDermott Data Scientist, Threat Analytics Deutsche Bank

Accelerating Innovation through Responsible AI:

Artificial intelligence (AI) is emerging as the defining technology of our age, with many industries already utilising AI in some form. Unleash the full potential of AI to transform your business model requires deep understanding of the customer, embedding societal and ethical implications in AI design and a structured approach for model development. How can you ensure that you are in control of your AI strategy and how do you develop AI capabilities that are in tune with a responsive, positive view of human society?

Jay Chakraborty Director & Adjunct Professor PwC

Networking coffee break

Small Data – Big Gains: Innovating in an Increasingly Complex Data Landscape

Applying big data, analytics and AI in finance is the direction most enterprises are moving towards. However, the integration of data across systems is increasingly challenging with long development horizons. Meanwhile companies are fighting to stay relevant and innovate today. Learn how Northwestern Mutual leveraged both big and small data at scale to deliver game changing results in record time.

Roman Geyzer Senior Director of Product Northwestern Mutual

Industry Roundtable Discussion


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