Visualize AI to spot new trading opportunities

The use of artificial intelligence (AI) and deep learning to generate and execute trading strategies is becoming increasingly common. As more and more business decisions are based on AI and advanced data analytics, it’s critical to provide transparency to the inner workings of an algorithmic black box. In fact, regulators and investors demand it.

The financial services sector is a leading adopter of AI and has the most ambitious AI investment plans. Adoption will center on AI technologies like neural-based machine learning and natural language processing because those are the ones that are beginning to mature and prove their value. However, the rapid adoption of these technologies by hedge funds and asset managers comes with a major challenge: lack of transparency.

The decision-making processes of machine learning based technologies are opaque and don’t provide reasoning behind the results. This is concerning because many machine-based decisions have wide-ranging compliance and business development implications. This is not much different from working with people, because their decision-making processes aren’t fully captured. AI, however, creates data and that is a major opportunity. That data can be audited and visualized to expose lineage and correlation that offers fund managers deeper insight into what makes one strategy better than another. The demand by regulators and investors for transparency into investment decisions will drive new innovations in visual data analytics.

Paul Lashmet, a veteran of the financial services industry, demonstrates how the right visualization tools can be used to visualize the decisions behind market simulations and automated trade execution. Along the way, he shows how data visualization can enable fund managers to learn from the decisions made by machines and spot new trading opportunities.