Deep Learning meets Capital Markets.
Cutting-edge technology for understanding capital markets communications data.
In a competitive capital markets environment, digitally advanced services and architectures will be the determinant of future success. Artificial intelligence can deliver innovation that was previously hard to achieve.
Capital markets firms are sitting on a vast trove of largely un-mined and unstructured communications data containing uniquely valuable insights into client sentiment, market activity and operational processes.
These insights lie in the millions of emails, calls and chats happening externally in the market with clients or counterparties and internally in operations channels.
Today, capital markets firms are unable to extract value from these conversations and insights remain hidden. The data is unstructured, non-actionable and there is no capability to process it at scale.
re:infer’s deep learning platform unlocks the value in your communications.
re:infer converts unstructured communications (emails, calls, chats, notes) into structured data in real-time allowing for analytics and automation.
In the pre-trade environment, re:infer is automatically discovering client intents, sentiments, trends and hidden relationships that translate into trading signals for front-office teams.
In post-trade operations, re:infer is discovering manual processes, quantifying failures, highlighting root-cause errors, detecting incorrect reference data and providing a bridge to downstream automation.
Across the trade lifecycle, re:infer is revolutionising the way client data is processed, enabling new revenue streams and improved operating efficiency.
re:infer creates opportunities for growth
Automatic capture of management information
Minimising the sources of latency
Automation of manual processes
Every conversation counts.
COO was tasked with implementing a board-level change programme: cut the operational cost base by 50% within 5 years.
re:infer was selected to identify quality-of-service across all channels. Our cognitive intent platform was able to identify processes and workflows that are suitable for automation and transition to a digital operations infrastructure that is centered around real-time analytics, automation and self-service.
Projected Annual Savings
Employee Hours Saved
Process Automated End-to-End