The Irony of Automation: Using Artificial Intelligence to Provide Personalized Customer Service

November 24, 2015 Kris Hammond

irony of automation

As originally seen on Professional Wealth Management.

It is undeniable. Your customers want more. More information, more conversations, more of your time and more of your attention. And if you cannot give them more, you risk losing them to companies and services that can give it to them. But how can you provide personal attention and communication to everyone all the time and at scale?

This is a question that is looming on everyone’s mind. For wealth management in particular, the issue of scaling client services and satisfaction is emerging as one of the strongest drivers in setting IT priorities.[1] Driven by the growing world of automation and cognitive computing solutions, traditional firms are being forced to change their models and expand personalized communications to all clients, not just the highest-wealth individuals, before clients that have felt ignored decide to pick another option such as a robo-advisor firm like Betterment and WealthFront. While robo-advisors have only begun to draw away the long-tail of clients, their continued growth seems inevitable and in turn, traditional firms must adapt.

Personalization through Automation

Fortunately, the technological advances that have contributed to this shift also contain the solution: personalization through automation. In other words, artificial intelligence now makes it possible to craft personalized automated communications at scale - exactly the partner that the wealth management sector needs to deliver the cost-effective, high quality personalized services that all clients are seeking.

For example, many fund managers are already turning to AI-powered natural language generation (NLG) to generate Portfolio Commentary, saving literally weeks of time, while wealth managers are using the same technologies to prepare for client meetings by automating the generation of talking points regarding investment performance. Another firm is using the technology to auto-generate custom letters to their clients, explaining their progress against goals with pointers as to how to better achieve them.

From a compliance perspective, since the analysis and reporting are done by machine, an audit trail that captures every step of the process can be generated along side the report. Issues of validation, quality and consistency need only be established once and can be relied on upon to be an unchanging part of the system’s performance.

As the advisory technologies that are now driving the world of robo-advisors is brought into larger firms, they could be tied to language generation systems as well. This sort of communication layer could even be used to provide insight, explain where the advice is coming from and the reasoning that went into it. Rather than dealing with hyper-intelligent “black boxes”, both advisors and customers will be able to understand what these systems are “thinking” and what is the basis for the advice they are giving.

Future Applications of Natural Language Generation

Over the next few years, other machine learning and predictive analytics technologies will certainly be brought to bear on long term investment decisions in the same way that they now dominate the short term trading landscape. As this happens, the need for language generation technologies will become even more important for both advisors and customers so that they can understand the advice and actions of the systems they are using.

The reality is that NLG technology can be used anywhere that data exists and that data contains information that people need to know. The irony is, that this technology can provide a level of communication at scale, communication that is genuinely personal, that is possible only because it is being done by machine.

[1] Aite Group's survey of 19 North American Wealth Management IT executives, Q2 2014

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