We are only at the beginning of what is possible
Artificial intelligence and automation technologies are beginning to transform the Asset Management world.
Most asset managers suffer from siloed data.
Client interactions and counterparty conversations remain opaque. There is no oversight or control and rich information about sentiment, performance and product appetite remain hidden.
Across the middle and back office trades fail to settle, grey processes remain unchanged and email is used as the primary workflow tool to manage exceptions.
The inability to analyse or automate unstructured communications data prevents client fulfilment, creates latency in operations and brings operational risk.
Re:infer's Deep Learning Platform transforms the value Asset Managers drive from their communications data.
In client analytics, Re:infer is providing a new window into the fundamentals of brand value, customer loyalty and trade appetite.
In operations, Re:infer is providing a new era for front, middle and back office performance, driven by real-time actionable structured data feeds.
Re:infer creates opportunities for growth
Re:infer helps Asset Managers become information advantaged. By enabling a real-time understanding and optimization of client interactions, shifts in sentiment, product and pricing signals can be actioned to drive growth.
Discovering the causes of failure in operations and the existence of grey processes allows for effective process redesign, accurate automation strategies and effective digitization.
Re:infer converts unstructured communications into structured data enabling actions to be triggered in downstream robotic process automation (RPA) installations.
Anomalous behaviour in conversational threads and failure to enforce appropriate practices are automatically alerted to compliance teams.
Advanced Client Analytics
- Monitor Client sentiment: real-time analytics on clients wants and needs
- Discover Client Demand: detect the drivers of value and failure demand
- Discover and act on product, pricing or research feedback
- Detect Operational Risk
- Monitor adherence to regulatory requirements from KYC to AML & GDPR
- Alerts to low frequency high risk events
- Perform surveillance at scale
- Discover and quantify manual processes
- Detect root-cause of exceptions and process, systems and people failure
- Monitor and improve settlements
- Make all communications data structured and actionable in the wider IT real-estate (RPA, CRM, CMS, SCV, ERP)
- Deliver straight-through processing of requests
- Cognitive + RPA: amplify existing robotics installations with a cognitive front-end that can trigger processes