Beyond CRM: AI Enables Capital Markets Firms to Reinvent Client Engagement & More Latest News Here – Up Jobs

By Stuart Berwick, CEO and co-Founder, Singletrack

Digitisation is becoming increasingly critical, and while those capital markets firms that have already digitised to some degree are seeing increased internal efficiencies and productivity gains as a result, there’s much more that can be done. 

There are familiar and valuable concepts for the sell-side that centre around, ‘Who do I call to get the trade?’, or ‘Which of my clients are at risk?’. Yet the answers will only typically be found in either a one-off spreadsheet that has taken a week to put together, or a quarterly report. In this era of artificial intelligence and machine learning, it is striking that legacy client relationship tools have not kept up with the times. 

Imagine what firms could achieve, however, if they break the bounds of possibilities? Not only by empowering employees to be more efficient, productive and better informed (which will ultimately increase and retain revenue, and drive profitability), but also by providing a better client experience. In uncertain times such as these, swift, data-driven decisions can have a positive effect on client retention and acquisition.

Preventing progress

Today, legacy CRM systems plagued by poor user experience and unreliability, are standing in the way of empowering employee productivity. Lacking investment, these legacy tools have not progressed. They are also highly inflexible, meaning they can’t be configured to meet the needs of a rapidly changing and volatile marketplace. 

Further, a growing number of younger “digital natives” are beginning to come through the industry, and along with them a much more sophisticated expectation of the technological tools aiding their day-to-day work.

Introducing ‘ambient’ and ‘guided’ systems

Changes are afoot, however, with the introduction of new AI & ML-led tools to CRM systems. Two important concepts in this area are ambient data collection and guided actions. 

Ambient data collection is based around a concept of “ambient zero”. This means that a user can sit down at a business IT system and simply do their daily work, without having to store or copy information onto another system. In other words, the system can automatically, or ‘ambiently’, gather information from its surrounding environment. 

An example of an ambient CRM feature is ‘data capture’. A virtuous circle is created whereby the system can ambiently identify data from the environment (such as Instant Bloomberg (IB) chat, email, phone conversations, calendar events, etc.) and intelligently categorise and capture it. The result is used to guide the system going forward. By implementing feeds from financial markets messaging systems (such as IB chat), the transcripts can be mined using artificial intelligence to identify high-value conversations (for example, the ticker/sector mentioned or directional comment). The user will confirm if the relevant fragment of data is indeed valuable, which will in turn teach the system to better recognise useful data. In this respect the system will ultimately become completely automated. 

Guided actions, on the other hand, relate to where the system guides the user to certain actions or outcomes to achieve a benefit, for example, to grow and maintain revenue and profitability. It achieves this by using a wide dataset and knowledge of previous outcomes.

Guided actions doesn’t only tell users what the problem is, it tells them what they should do about it by using an array of data points and algorithms to form a prediction. For instance, the system can tell an investment bank which of its institutional clients might be at risk of either terminating their relationship or a significant decline in activity (such as attending events) and revenue over the next six to 12 months. Another example of a guided feature is a “Call Manager” application, whereby an institution’s salespeople and research analysts have dynamic client lists that can be organised and prioritised by sector, buy-side client or other segmentation. These lists are then easily prioritised, in seconds, by who to call when, depending on what is happening in the market, how traders are reacting, and even statistically determining when people are most likely to respond and pick up a call.

A competitive edge in good times and bad

The ability to ingest interaction data across all channels and identify meaningful interactions through natural language processing, and to ambiently capture client data that would otherwise be laborious and costly to process, gives firms an indubitable edge.

In today’s fast-moving and unpredictable world, simply updating a CRM system is not good enough – the only option now is to move to nothing less than a data-driven advisory system, because this is what will give the insights needed to excel in all market conditions. By thinking bigger and more ambitiously than current systems permit, new technology can break the barriers and for the first time truly automate and systemise a client strategy process. 

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