Peter Ross heads the data science practice at DataRobot and works with asset management, fintech, and banking clients. He has over 25 years of experience in quantitative roles, including at Morgan Stanley, Warburg Pincus, and Goldman Sachs. Prior to joining DataRobot, he was a partner at a start-up global equities hedge fund. Peter holds an M.Sc. in data science from the City University of London, an MBA from Cranfield University School of Management, and a B.Sc. in Mathematics from Cranfield University.
Datarobot’s AI for Financial Markets
FactSet has announced a new AI platform that integrates automated machine learning technology from DataRobot into its workstations. With this platform, clients can easily create, monitor and manage automated machine learning models to power their investment workflows. This unified platform also enables clients to scale data-driven investment strategies.
Among the new features DataRobot is adding to its AI Cloud platform is Automated Time Series. This new feature will help organizations make better predictions and better match the changing market landscape. Additionally, the new software supports prediction explanation and segmented modeling, which allows developers to rapidly create AI applications with minimal code.
Feature Is Available For Banking Firms
The new feature is available for banking firms that are interested in applying AI in their daily operations. It can be used to predict whether a loan will default or not, which can help save time and money. DataRobot also enables alternative lending companies to predict loan defaults by leveraging data from multiple sources.
AI can help financial institutions make better investment decisions by eliminating the risk and complexity of traditional methods. By de-risking AI investments, this solution allows financial institutions to implement AI applications quickly and easily. The platform also enables companies to quickly scale their AI solutions to almost every part of their operations.
Kx’s Automated Time Series Solution
With increased regulatory scrutiny, banks need sophisticated time series analytics for their automated trading and pricing activities . For this industry, Kx has been the time series analytics engine of choice. This solution helps banks automate and monitor their trading, pricing and hedging businesses. Kx’s automation capabilities also allow financial institutions to monitor the behavior of their customers. While use-cases vary, they all share common requirements for high-speed analytics, data integrity, and scalability.
Kx technology incorporates Kdb+, a time-series database that is built on a proprietary platform. This software platform is fast and scales to massive datasets. It also provides high-performance analytics, operational intelligence, and streaming analysis. The Kx technology platform is available for enterprise clients in 15 countries and supports real-time data analysis.
How to Improve Customer Experience?
Kx’s Automated Time Series solutions are backed by kdb+, the world’s fastest in-memory time series database. This solution empowers clients to build and deploy AI applications across all asset classes. This technology also supports advanced analytics, risk management, and sales.
KX has partnered with Microsoft to develop new applications and services that help businesses analyze market data and make better decisions. The partnership will expand KX’s service portfolio and improve customer experience. Microsoft believes KX’s capabilities can help accelerate the evolution of financial services.
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