How Conversational Artificial Intelligence Can Improve the Member Experience

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Andrew Casson Photo
Vice President
Content Guru

4 minutes

Augment your contact center with conversational machine agents.

In the credit union world, artificial intelligence is often used for credit scoring and credit card fraud detection. More recently, conversational machine agents have come into common use to boost the member experience.  

This application of AI to the member experience in the call center through conversational machine agents is a growing trend. Questions we’ll answer here about these intelligent chatbots or “conversational AI” include the following:

  • How do conversational machine agents compare to chatbots?
  • Why are conversational machine agents are great for credit union members and employees alike?
  • How can a cloud contact center with embedded conversational machine agents improve your credit union budget and operations?

Both Chatbots and Conversational Machine Agents Communicate Like People

On the face of it, chatbots and conversational machine agents seem the same because both mimic human conversation. The key difference is that conversational machine agents are powered by AI, while chatbot interactions follow a rules-based computer program. This requires chatbots to follow a pre-determined interaction flow.

Chatbots operate like an auto-attendant that directs members to press a number for high-volume, simple requests. So chatbots are very useful for these kinds of requests. However, some requests are more complex. And this is where conversational machine agents stand out.

Resolve A Bigger Range of Issues with Conversational Machine Agents

A conversational machine agent powered by AI enables:

  • The ability to understand intent based on speech or written text
  • Perceive language sentiment and subtle nuance
  • Reply as a person would in a human conversation

Conversational machine agents are also omnichannel. This means that humans can interact with any digital channel, like a phone, website or social media, just as they do with other humans.

To replace pre-defined chatbot conversations, conversational AI uses various artificial intelligence methods like machine learning and natural language processing. And then using proprietary artificial intelligence algorithms, the conversational machine agent system is trained.

Training occurs when a conversational machine agent is fed questions and answers from humans. As it gets more of both, over time, it becomes smarter. In addition, it learns about a broader range of issues and solutions, becoming increasingly accurate. In time, a conversational machine agent can engage in a more natural, dynamic conversation with credit union members.

At some point, as members increasingly engage with conversational machine agents, a member won’t be able to tell the difference between an interaction with a machine from an interaction with another person.

The big benefits conversational machine agents can offer are cost-savings, productivity gains and more satisfied members. 

Why? Fully trained conversational machine agents can easily resolve many issues. As such, they reduce higher-cost interactions with employees, while delivering quick answers to members. The impact is higher operational efficiency and lower direct costs. 

Here's an example of the economics. Assume the average contact center agent costs your credit union $52,000 a year or around $4,300 monthly. We’ll also assume you need to add capacity to your existing contact center agents to meet call demand.

One conversational machine agent removes a monthly 20 hours of human time. Instead of hiring another agent for $4,300 per month, you can add a conversational machine agent for $150 a month. 

This leaves your human agents to work more efficiently on more complex queries. The result is that human agents have more interesting jobs while your credit union saves money.

With conversational machine agents, your credit union can concentrate on keeping and adding members. In addition, you can engage with a limitless number of members in a personal way and scale up contact center capacity to handle sudden demand spikes.

Finally, credit unions can personalize interactions and become proactive—to create differentiated and modern member experiences.

How to Evaluate Conversational Machine Agents 

There are options for deploying conversational machine agents in your credit union’s cloud contact center. You can:

  1. Integrate another SaaS app into the cloud contact center
  2. Build and train your own conversational machine agent using an artificial intelligence provider that you must integrate into your infrastructure
  3. Use an end-to-end cloud contact center that embeds AI for in-built conversational machine agent functionality

Approach 1 brings data silos and another vendor to manage and has the disadvantages of a standalone app. Approach 2 requires a big budget and specialized skills. Approach 3 can be the best way to go, and here’s why.

  • It is pre-trained in financial service and credit union language, making instantly productive conversational machine agents without lengthy initial model training.
  • Reporting is more accurate and actionable in real-time. As opposed to the data silos in each separate SaaS app, a cloud contact center with built-in conversational machine agents keeps all interaction data in a centralized place. The result is a 360-degree view of member activity, which is always up-to-date across all channels, which is crucial to agile operations.
  • You can save on AI integration and vendor fees. Savings drop to the bottom line. Since the cloud contact center manages the maintenance of AI integrations, you won’t have to do that either. 
  • Preferred, high-volume AI provider rates get passed on to your credit union. A direct relationship with an AI provider would cost you much more.

Taking it all together, it’s easy to conclude that for credit unions seeking efficiency, savings and superior member experiences, a cloud contact center with embedded AI for in-built conversational machine agent functionality is a solid choice. 

Andrew Casson is a longtime network engineer and telecommunications and contact center architect. He’s currently a VP for CUESolutions provider Content Guru, Campbell, California, maker of the highly-acclaimed storm®, an all-in-one contact center-as-a-service solution with industry-leading functionality, performance, reliability and flexibility. Learn how your credit union can grow happy members with conversational machine agents here.

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