AML Solutions at Scale Using Databricks Lakehouse Platform
Anti-Money Laundering (AML) compliance has been undoubtedly one of the top agenda items for regulators providing oversight of financial institutions across the globe. As AML evolved and became more sophisticated over the decades, so have the regulatory requirements designed to counter modern money laundering and terrorist financing schemes. The Bank Secrecy Act of 1970 provided...
Strategies for Modernizing Investment Data Platforms
The appetite for investment was at a historic high in 2020 for both individual and institutional investors. One study showed that “retail traders make up nearly 25% of the stock market following COVID-driven volatility”. Moreover, institutional investors have piled on investments in cryptocurrency, with 36% invested in cryptocurrency, as outlined in Business Insider . As...
Combining Rules-based and AI Models to Combat Financial Fraud
The financial services industry (FSI) is rushing towards transformational change, delivering transactional features and facilitating payments through new digital channels to remain competitive. Unfortunately, the speed and convenience that these capabilities afford also benefit fraudsters. Fraud in financial services still remains the number one threat to organizations’ bottom line given the record-high increase in overall...
How to Extract Market Drivers at Scale Using Alternative Data
Watch the on-demand webinar Alternative Data Analytics with Python for a demonstration of the solution discussed in this blog and/or download the following notebooks to try it yourself. Stock Analysis - Plant-based Meat Historical Data GDELT News Source In Lakehouse Text Analytics on GDELT Alternative Data Time Series Foot Traffic Forecasting Introduction Why Alternative data...
Scaling Financial Time Series Analysis Beyond PCs and Pandas: On-Demand Webinar, Slides and FAQ Now Available!
On Oct 9th, 2019, we hosted a live webinar —Scaling Financial Time Series Analysis Beyond PCs and Pandas — with Junta Nakai, Industry Leader Financial Services at Databricks, and Ricardo Portilla, Solution Architect at Databricks. This was a live webinar showcasing the content in this blog- Democratizing Financial Time Series Analysis with Databricks. Please find...
Democratizing Financial Time Series Analysis with Databricks
Introduction The role of data scientists, data engineers, and analysts at financial institutions includes (but is not limited to) protecting hundreds of billions of dollars worth of assets and protecting investors from trillion-dollar impacts, say from a flash crash. One of the biggest technical challenges underlying these problems is scaling time series manipulation. Tick data,...
Understanding Dynamic Time Warping
This blog is part 1 of our two-part series Using Dynamic Time Warping and MLflow to Detect Sales Trends. To go to part 2, go to Using Dynamic Time Warping and MLflow to Detect Sales Trends. The phrase “dynamic time warping,” at first read, might evoke images of Marty McFly driving his DeLorean at 88...
Using Dynamic Time Warping and MLflow to Detect Sales Trends
This blog is part 2 of our two-part series Using Dynamic Time Warping and MLflow to Detect Sales Trends. The phrase “dynamic time warping,” at first read, might evoke images of Marty McFly driving his DeLorean at 88 MPH in the Back to the Future series. Alas, dynamic time warping does not involve time travel; instead, it’s...
