
Financial Markets
Financial Markets are at a unique place and time in their evolution. They are faced with some of the biggest challenges in their history: full digitization of markets, technology-driven disruption, fee compression, and lower client switching costs, to name just a few. At the same time, they are embracing some of their biggest opportunities, many made possible by technology: the convergence of data ubiquity, high-speed processing, and the advanced technologies that comprise enterprise AI.
Discover how enterprise AI is driving the Financial Markets revolution.
Discover how enterprise AI is driving the Financial Markets revolution.
AI in Financial Markets
As Financial Market firms work to digitize and transform for growth and operational efficiencies, they are aggressively innovating and differentiating, as they compete to secure a larger share of assets and create a next generation client experience. From AI trading to AI fraud detection to the benefits of a machine learning stock market— artificial intelligence helps firms to reimagine their operations. By accelerating initiatives, leveraging their own data, and delivering bottom-line results, companies using machine learning for trading cannot just compete, but win.
Exceeding Client Expectations
- Identify client churn risk
- Retain the highest lifetime value clients
- Anticipate appropriate product offers (cross-sell/upsell strategies)
- Optimize new customer acquisition strategies
- Deepen client relationships
- Increase share of wallet
Risk Exposure
- Predict risk for ratings and outlooks
- Identify suspicious anti-money laundering activity with fewer false positives
- Detect and prevent account takeover and wire fraud
- Monitor trading and communication
- Detect and prevent cyber threats
Improving Operational Efficiency
- Understand channel effectiveness
- Predict customer profitability
- Asset allocation modeling
- Predict fund net inflow
- Reduce middle and back-office cost from process failures and error correction
- Optimize trade execution and routing for trading cost optimization

AI Use Cases in Financial Markets
The opportunity to apply AI across financial markets is expansive. There are thousands of applications for automated machine learning to help ensure that financial market research and investment firms are empowered to make faster and better decisions.
DataRobot Can Help:
DataRobot in the News
Explore the Most Popular Resources
-
On-Demand WebinarBest Practices for AI and ML in Financial Markets with Automated Machine LearningMachine learning techniques cannot magically make "signal" appear out of thin air, nor can they make unstable factors more stable. Data science typically succeeds where there are complex behaviors that can be described in data and where consistent inputs lead to consistent "predictable" outcomes.Watch the Webinar
-
EbookAI in Financial Markets: Beyond the Market-Predicting Magic BoxBefore you read a single word of this ebook, let’s get one thing out of the way: there’s no such thing as a market-predicting, magic AI box. After all, if there were, we’d be called DataRobot Capital Partners, be running a hedge fund, driving expensive cars to our beachside villas, and not selling our world-leading enterprise machine learning platform. That said, there is definite value to be had from machine learning (especially the automated variety) in the financial markets.Read More
-
Ebook6 AI Solutions Every Asset Manager NeedsAs the buy side faces the most challenging period in its history, AI and Machine Learning are being used today to counter the onslaught of obstacles – and help those same firms achieve significant ROIRead More
-
On-Demand WebinarDeploying AI for Success on the Buy SideIn this webcast, you will see AI in action, and learn how asset managers are using AI and machine learning (ML) to build and successfully deploy predictive models with measurable business impact.Watch the Webinar
-
On-Demand WebinarHow to Transform Your Business with Automated Machine LearningOn this webinar, you’ll see how automated machine learning enables more employees to take part in AI initiatives and makes existing data science teams more productive.Watch the Webinar