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 suc...

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 t...

Description:

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...

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 ...

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 ...

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...

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.

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 dis...

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 spe...

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 Lakeh...

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 tha...

• 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 demonstrati...

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 B...

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 (...

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 dif...

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.

Precisi...

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.