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Radical transformation is the theme of 2020, with customers demanding personalized products, improved protection against fraud, and digital experiences that match every small shift in behavior. Banks, insurance companies, and institutional investors are even more reliant on big data and AI to meet these demands and to outmaneuver the competition.

For years, the Data + AI Summit has been the premier meeting place for organizations looking to build AI applications at scale with leading open-source technologies such as Apache SparkTM, Delta Lake and MLflow. In 2020, we’re continuing the tradition by taking the summit entirely virtual. Data scientists and engineers from anywhere in the world will be able to join June 22-26 to learn and share best practices for delivering the benefits of AI.

This year’s Summit features a full agenda of talks from Financial Services industry leaders, including CapitalOne, VISA, Credit Suisse and Intuit, among others. As usual, attendees can also take part in our Financial Services Experience to meet with their peers, participate in engaging discussions with industry thought leaders and participate in interactive demos on the hottest data and AI use cases. Register for Spark + AI Summit and visit the Financial Services Lounge to take advantage of all the sessions and events.

Financial Services Tech Talks

Here is an overview of some of our most highly anticipated financial services session talks at this year’s summit.


Disrupting Risk Management through Emerging Technologies
Optimally measuring risk is a critical function for many, but it’s especially so in financial services. Professionals need to understand the performance of products prior to investment in order to make strategic decisions. In this talk with CapitalOne, you’ll hear how senior members of their engineering are leveraging technology to provide modelers, analysts, and key stakeholders with end-to-end analytic experiences that enable loss forecasting, gaming analysis, result comparison of model runs, intelligent insights and outputs, and the creation of new features.


Cloud and Analytics—From Platforms to an Ecosystem
Data science powers Zurich’s insurance business like a central nervous system: 70 data scientists work on everything from optimizing claims-handling processes to protecting against the next risk, to revamping the suite of data and analytics for customers. In this talk, you’ll hear exactly how they implemented Zurich’s scalable, self-service data science ecosystem to optimize and scale the activities in the project lifecycle, as well as how they streamline machine learning and predictive analytics efforts with Azure data lake with analytical tools.


Using AI to Support Proliferating Merchant Changes
At VISA, merchants are a core entity in their payments network. Millions are observed to be added to the ecosystem every month, with a significant portion of them being merchants that have created a new identity and changed attributes. In this talk, learn how they use AI, big data, and a suite of tools to look at merchant patterns over regular intervals, detect these changes and, more importantly, track them with accuracy to prevent incorrect offers and delays in queries.


Using Machine Learning Algorithms to Construct All the Components of a Knowledge Graph
Machine learning algorithms drive product delivery at Reonomy. In this talk, you’ll learn the ins and outs directly from their chief data scientist as she walks through examples of critical code designs, cluster configuration, and the algorithms used for successfully building the components Reonomy’s knowledge graph. Takeaways include key points to consider when implementing production-quality models, as well as a logical framework for building knowledge graphs that are able to support a diverse set of property intelligence products.


How Intuit uses Apache Spark to Monitor in-production Machine Learning Models at Large-scale
In this presentation, Intuit will discuss their soon-to-be open source Model Monitoring Service (MMS). MMS is an in-house, Spark-based solution developed by Intuit AI to provide ongoing monitoring for both data and model metrics of in-production ML models. MMS aims to tackle multiple challenges of in-production ML model monitoring, including the integration of multiple data sources from different time ranges, and reusable and extendable metric and segmentation libraries.

Financial Services Industry Forum

Join us on Thursday, June 25, at 9:00 AM-10:30 AM EST for an interactive Financial Services Forum at Spark + AI Summit. In this capstone event at Spark + AI Summit, you’ll have the opportunity to engage in interactive discussions with leaders in the Financial Services industry on how data and machine learning are driving innovation across the entire sector. Here’s a snapshot of the presenter lineup for the Financial Services Forum:

How Credit Suisse is using data analytics and AI on Databricks to rapidly scale new product innovation
Despite the increasing embrace of big data and machine learning, most financial services companies still experience significant challenges around data types, privacy, and scale. Credit Suisse is overcoming these obstacles and leading the way in employing data analytics and AI for risk management and client focus to drive business growth and operational efficiency. How? Credit Suisse has brought together a core set of partnerships, people, processes and technology—with Databricks as its unified analytics platform— that enables them to collaborate and scale new product innovation, rapidly decreasing the time-to-market it takes from initial business idea to commercially viable product.

Nasdaq x AI, Dynamic Markets and the New Norm
AI has become a key differentiator for modern enterprises today. Technology innovations that were once only available to the top 1% of companies are now quickly becoming democratized. At Nasdaq, they are deploying advanced analytics to serve multiple use cases —  from protecting financial markets to enabling new digital markets. Specifically, we will explore how Nasdaq leverages advanced data science techniques like graph processing and deep learning to see relationships between unstructured data (such as images and text) to feed models that will protect, transform and unlock new opportunities in Capital Markets.

Demos on Popular Data + AI Use Case in Financial Services

Join live demos on the hottest use cases in the financial services industry including value at risk modeling, automating claims assessments with computer vision, credit risk analytics and more.

Modernizing Risk Management Practices
Traditional banks relying on on-premises infrastructure can no longer effectively manage risk. This demo highlights the value of an agile Modern Risk Management practice capable of rapidly responding to market and economic volatility. Using the value-at-risk use case, you will learn how Databricks is helping FSIs modernize their risk management practices, leverage Delta Lake, Apache Spark and MLFlow to adopt a more agile approach to risk management.

How to Build Models that Move Quickly Through Validation and Audit
Financial institutions and banks are increasingly using data and ML to drive competitive insights reliable enough for business to trust and act upon. In this demo focused on credit risk analytics, we show how a unified data analytics platform brings a more disciplined and structured approach to commercial data science, reducing the model lifecycle process from 12 months to a few weeks.

Accelerating Claim Assessment Through Computer Vision
With over 15 thousands car accidents in the US every day (10 accidents every minute), automotive insurers recognize the need to improve operational efficiency through the use of AI. In this session, we demonstrate how Databricks helps insurance companies kickstart their AI/Computer Vision journey towards claim assessment and damage estimation.

Financial Services Training

Practical Problem-Solving in Finance: Real-Time Fraud Detection with Apache Spark
In this half-day course, you’ll learn how Databricks and Spark can help solve real-world problems one faces when working with financial data. You’ll learn how to deal with dirty data and how to get started with Structured Streaming and Real-Time Fraud Detection. Students will also receive a longer take-home capstone exercise as bonus content to the class where they can apply all the concepts presented. This class is taught concurrently in Python and Scala.

AI Disruption of Quantitative Finance. From Forecasting, to Probability Density Estimation, to Generative Models, and to Optimisation with Reinforcement Learning
In this talk, Nima Nooshi, a customer success engineer at Databricks,  will showcase an end-to- end asset management pipeline based on recent AI developments. He’ll walk through the steps to build an autonomous portfolio manager, discuss how a predictive AI component, such as a nonlinear-dynamic Boltzmann machine, can improve the learning of the agent, as well as the possibility of using a data generating component to learn the conditional distribution of asset prices. You’ll walk away with a clear picture of an end-to-end data pipeline and how different components of a complex model work together in a unified platform architecture.

Sign-up for the Financial Services Experience at Summit!

To take advantage of the full Financial Services Experience at Spark + AI Summit, simply register for our free virtual conference and select Financial Services Forum during the registration process. If you’re already registered for the conference, log into your registration account, edit “Additional Events” and check the forum you would like to attend.

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