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JUNE 27-30, 2022

June 27–30, 2022


Financial Services

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The future of Financial Services is open with data and AI at its core.

Welcome data teams and executives in Financial Services! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on how Financial Services leaders are harnessing the power of data and analytics to digitally transform, minimize risk, accelerate time to market and drive sustainable value creation

To help you take full advantage of the Financial Services industry experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

  • Financial Services Industry Forum: Our flagship event for Financial Services attendees at Summit featuring keynotes and panel discussions with ADP, Northwestern Mutual, Point72 Asset Management, S&P Global and EY, followed by networking. More details in the agenda below. 
  • Financial Services Lounge: Stop by our lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from our partners including Accenture, Avanade, Deloitte and others.
  • Session Talks: Over 15 technical talks and demos on topics including hyper-personalization, AI-fueled forecasting, enterprise analytics in cloud, scaling privacy and cybersecurity, MLOps in cryptocurrency, ethical credit scoring and more.

The full list of Financial Services sessions, talks and demos can be found in the agenda below

Featured Speakers

Headshot of Abigail Shriver

Abigail Shriver

Lead Cybersecurity Software Engineer


Curated Sessions


10:45 AM

10:45 AM-11:20 AM
FutureMetrics: Using Deep Learning to Create a Multivariate Time Series Forecasting Platform for Economic Strategic Planning
Liquidity forecasting is one of the most essential activities at any bank. TD bank, the largest of the big Five, has to provide liquidity for half a trillion dollars in products, and to forecast it to remain within a $5BN buffer. The use case was to predict liquidity growth over short to moderate time horizons: 90 days to 18 months. Model must perform reliably in a strict regulatory framework, and as such v...
Headshot of Matthew Wander
Matthew Wander
TD Bank
See Details

11:30 AM

11:30 AM-12:05 PM
Towards Dynamic Microstructure: The Role of Machine Learning in the Next Generation of Exchanges
What role will AI and machine learning play in ensuring the efficiency and transparency of the next generation of markets?

In this session, Douglas Hamilton (AVP, Machine Intelligence Lab) and Michael O’Rourke (SVP, Engineering & AI/ML) will show attendees how Nasdaq is building dynamic microstructures that reduce the inherent frictions associated with trading, and give insights into their app...
Headshot of Douglas Hamilton
Douglas Hamilton

2:05 PM

2:05 PM-2:40 PM
How Robinhood Built a Streaming Lakehouse to Bring Data Freshness from 24h to Less Than 15 Mins
Robinhood’s mission is to democratize finance for all. Continuous data analysis and data driven decision making are fundamental to achieving this. The data required for analysis comes from varied sources - OLTP databases, event streams and various 3rd party sources. A reliable lakehouse with an interoperable data ecosystem and fast data ingestion service is needed to power various reporting and business cr...
Headshot of Balaji Varadarajan
Balaji Varadarajan
Robinhood Markets
Headshot of Vikrant Goel
Vikrant Goel
2:05 PM-2:40 PM
Protecting Personally Identifiable Information (PII)/PHI Data in Data Lake via Column Level Encryption
Data breach is a concern for any data collection company including Northwestern mutual. Every measure is taken to avoid the identity theft and fraud for our customers; however they are still not sufficient if the security around it is not updated periodically. A multiple layer of encryption is the most common approach utilized to avoid breaches however unauthorized internal access to this sensitive data sti...
Headshot of Keyuri Shah
Keyuri Shah
Northwesternmutual Insurance
Headshot of Chandiprasad Chintalapati
Chandiprasad Chintalapati
Northwestern Mutual
2:05 PM-2:40 PM
Nixtla: Deep Learning for Time Series Forecasting

Time series forecasting has a wide range of applications: finance, retail, healthcare, IoT, etc. Recently deep learning models such as ESRNN or N-BEATS have proven to have state-of-the-art performance in these tasks. Nixtlats is a python library that we have developed to facilitate the use of these state-of-the-art models to data scientists and developers, so that they can use them...
Headshot of Max Mergenthaler
Max Mergenthaler

2:50 PM

2:50 PM-3:25 PM
Building an Operational Machine Learning Organization from Zero and Leveraging ML for Crypto Security
Introduction to BlockFi and what we do as a crypto company.
Our journey with Databricks:
- Building a cross functional ML team
- Scoping business problems to get executive buy in
- Conveying a strategic vision for ML at your organization
- Operationalizing ML & Data Science to solve business challenges
- Building clear business objectives for your ML Projects

Headshot of Anthony Tellez
Anthony Tellez

3:30 PM

Industry Forum
3:30 PM-5:45 PM
Financial Services Industry Forum: The Future of Financial Services is Open with Data and AI at Its Core
Join our capstone event for Financial Services attendees at Summit featuring keynotes and data transformation stories from TD Bank, Northwestern Mutual, S&P Global, Point72 Asset Management, ADP and EY, followed by networking.
Headshot of Jeffrey Parkinson
Jeffrey Parkinson
Northwestern Mutual
Headshot of Shraddha Shah
Shraddha Shah
And nine more

4:00 PM

4:00 PM-4:35 PM
Productionizing Ethical Credit Scoring Systems with Delta Lake, Feature Store and MLFlow
Fairness, Ethics, Accountability and Transparency (FEAT) are must-haves for high-stakes machine learning models. In particular, models within the Financial Services industry such as those that assign credit scores can impact people’s access to housing and utilities and even influence their social standing. Hence, model developers have a moral responsibility to ensure that models do not systematically disadv...
Headshot of Jeanne Choo
Jeanne Choo

5:30 PM

5:30 PM-6:05 PM
Scaling Salesforce In-Memory Streaming Analytics Platform for Trillion Events Per Day
In general , in-memory pipelines would scale quite well in Spark if we apply the same processing logic to all records. But for Salesforce the major challenge is, we need to apply custom logic specific to a Log Record Type (LRT). The custom logic includes applying different schemas while processing each event. So performing such custom logic specific to LRT , we need to have a mechanism to collect LRT specif...
Headshot of Dyno Fu
Dyno Fu
Headshot of Kishore Reddipalli
Kishore Reddipalli

5:45 PM

Industry Forum
5:45 PM-6:45 PM
Financial Services Industry Reception
Join us for a chance to network with other Financial Services industry executives and Databricks leaders. Enjoy some refreshments and learn about our key partners who have built solutions for tackling high impact use cases on the Lakehouse.


10:45 AM

10:45 AM-11:20 AM
Enabling BI in a Lakehouse Environment: How Spark and Delta Can Help With Automating a DWH Development
The traditional enterprise data warehouses typically struggle when it comes to handling large volumes of data and traffic, particularly when it comes to semi-structured and unstructured data. In contrast, data lakes manage to overcome such issues and have nowadays become the central hub for storing data. In this session we further outline how we can enable BI Kimball data modelling development in a Lakehous...
Headshot of Yoshi Coppens
Yoshi Coppens
Headshot of Ivana Pejeva
Ivana Pejeva

11:30 AM

11:30 AM-12:05 PM
Scaling Privacy: Practical Architectures and Experiences
At Spark Data & AI 2021, We presented the use case around Privacy in an Insurance Landscape using Privacera. Scaling Privacy in a Spark Ecosystem (https://www.youtube.com/watch?v=cjJEMlNcg5k). In one year, the concept of privacy and security have taken off as a major need to solve and the ability to embed this into business process to empower data democratization has become mandatory. The concept that data ...
Headshot of Mei Gui
Mei Gui
Headshot of Aaron Colcord
Aaron Colcord

2:05 PM

2:05 PM-2:40 PM
A Modern Approach to Big Data for Finance
• There are unique challenges associated with working with big data for finance (volume of data, disparate storage, variable sharing protocols etc...)

• Leveraging open source technologies, like Databricks' Delta Sharing, in combination with a flexible data management stack, can allow organizations to be more nimble in testing and deploying more strategies

• Live demonstration of Del...
Headshot of Bill Dague
Bill Dague
Headshot of Leonid Rosenfeld
Leonid Rosenfeld

2:50 PM

2:50 PM-3:25 PM
Enabling Learning on Confidential Data
Multiple organizations often wish to aggregate their confidential data and learn from it, but they cannot do so because they cannot share their data with each other. For example, banks wish to train models jointly over their aggregate transaction data to detect money launderers more efficiently because criminals hide their traces across different banks. To address such problems, we developed MC^2 at UC Berk...
Headshot of Rishabh Poddar
Rishabh Poddar
Opaque Systems

4:00 PM

4:00 PM-4:35 PM
Running a Low Cost, Versatile Data Management Ecosystem with Apache Spark at Core
Data is the key component of Analytics, AI or ML platform. Organizations may not be successful without having a Platform that can Source, Transform, Quality check and present data in a reportable format that can drive actionable insights.
This session will focus on how Capital One HR Team built a Low Cost Data movement Ecosystem that can source data, transform at scale and build the data storage (Red...
Headshot of Shariff Mohammed
Shariff Mohammed
Capital One


11:30 AM

11:30 AM-12:05 PM
Cutting the Edge in Fighting Cybercrime: Reverse-Engineering a Search Language to Cross-Compile it to PySpark
Traditional cybersecurity Security Information and Event Management (SIEM) ways do not scale well for data sources with 30TiB per day, leading HSBC to create a Cybersecurity Lakehouse with Delta and Spark. Creating a platform to overcome several conventional technical constraints, the limitation in the amount of data for long-term analytics available in traditional platforms and query languages being diffic...
Headshot of Abigail Shriver
Abigail Shriver
Headshot of Jude Ken-Kwofie
Jude Ken-Kwofie
And one more



Swedbank: Enterprise Analytics in Cloud
Swedbank is the largest bank in Sweden & third largest in Nordics. They have about 7-8M customers across retail, mortgage , and investment (pensions). One of the key drivers for the bank was to look at data across all silos and build analytics to drive their ML models - they couldn’t. That’s when Swedbank made a strategic decision to go to the cloud and make bets on Databricks, Immuta, and Azure.

Headshot of Vineeth Menon
Vineeth Menon
AI-Fueled Forecasting: The Next Generation of Financial Planning
Quarterly forecasts and annual planning submissions are all too familiar for financial analysts and senior leaders. Imagine an organization where the churn and extensive hours behind forecast submissions do not exist thanks to a continuous, real-time engine that incorporates continuous data feeds of financial, operational, and external macroeconomic data into AI-powered algorithms.

Headshot of Eric Merrill
Eric Merrill
Headshot of Arunima Gupta
Arunima Gupta

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