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Can Natural Language Processing Bring Us Back to the Future of Banking?

  • Alex Jimenez
  • April 16, 2020
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In the late 1990s, many financial service organizations began adopting Customer Relationship Management (CRM) systems. Originally, CRMs were focused on sales management tracking and automation. However, the promise of CRMs being true relationship management tools that cut across sales, marketing, operations, and customer service was too tantalizing to ignore. Alas, very few financial service companies have been able to truly leverage CRM in that fashion.

In the early ’00s, I build out a private banking call center. One of the keys that made a call center possible for private banking clients was the promise of allowing the call center agents to have a full view of the client’s relationship; including up to the minute marketing campaigns, sales notes, and operational updates.

Somehow, we never harnessed such capabilities from the CRM system. There were many obstacles in realizing that vision — but the hardest piece was to properly gather all the elements of conversations the clients had had with the bank.

I was reminded of that vision when reading about Frame AI‘s Series A funding in TechCrunch. The article described Frame AI’s use of natural language processing (NLP) to address a similar idea thus:

“Frame is basically an early warning system and continuous monitoring tool for your customer voice,” Frame CEO and co-founder George Davis told TechCrunch. What that means, in practice, is the tool plugs into help desk software, call center tooling, CRM systems and anywhere else in a company that communicates with a customer.

At Extractable, we work with many financial service companies that share the same vision.

We help them with tactics that pull data together to gather as much of the voice of the customer (VOC) in a single place, often a CRM.  However, the challenge remains that there are conversations beyond accessible data.

The problem isn’t getting any easier.

As more and more interactions between organizations and their clients surface,having a centralized VOC seems farther and farther away.  For instance, social media conversations are often not captured in an organization’s data warehouse.

With companies moving to unstructured data repositories, such as data lakes, some have begun to pull these interactions.  AI is the latest technology that offers us the possibility of attaining a full picture of a customer’s relationship.  If Frame AI and their competitors can indeed apply NLP to unstructured data from all types of interactions with customers in all channels—this could be the keys to the kingdom.

Those of us old enough to remember bankers that were fully in the know of a customer’s life can’t help but wonder — Is AI technology finally here to deliver us back to that reality?

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