Abhi Sidhu & Ritvik Shrivastava – In this post, we’ll take a look at our newest blueprint — a conversational assistant for common personal banking use cases.
MindMeld blueprints come with a pre-configured application structure and pre-built set of code samples and datasets. In this post, we’ll take a look at our newest blueprint — a conversational assistant for common personal banking use cases.
MindMeld provides example applications for common conversational use cases, called MindMeld blueprints, that comes with a pre-configured application structure and pre-built set of code samples and datasets. A blueprint allows you to quickly build and test a fully working conversational app without writing code or collecting training data. If desired, you can then treat the blueprint app as a baseline for improvement and customization by adding data and logic specific to your business or application needs.
In this post, we’ll take a look at our newest blueprint — a conversational assistant for common personal banking use cases.
Before diving into the details of development, let’s talk about some key contributing factors behind the idea of this app.
With the growing popularity of FinTech, major financial institutions are looking at smarter solutions for providing their services to clients — conversational IVR, or virtual assistants, is one of the prominent targets.
The MindMeld platform, widely used for developing robust assistant applications, is ideal for the same. This serves as motivation for our new Banking Assistant blueprint: a virtual bank teller that shows off some of our amazing functionalities.
Virtual assistants are efficient in terms of time spent by employees. Targeting lower customer interaction times by reducing the human-hours spent on solving previously seen issues is one of the major benefits. Also, AI-powered solutions are data-driven and can be improved with time and continuous training. This requires less time than training and re-training employees for the same.
For enterprises like banks, customers’ personal data is extremely sensitive. The MindMeld platform offers a significant advantage over cloud-based conversational AI platforms by allowing for data storage entirely on an organization’s local servers. This makes it advantageous for enterprise applications that are concerned about data privacy and security as data is never shared.
Now that we have our motivation, let’s take a look at the development steps.
The Banking Assistant allows users to securely access their banking information and complete tasks as if they’re conversing with a teller. Below are some sample conversations for common banking tasks:
As part of the NLP component of any MindMeld app, we define a set of key use case domains or more fine-grained intents. The Banking Assistant intents include:
For the complete description of the app’s architecture and a detailed breakdown of domains, intents, and entities, visit our documentation and refer to the illustration below:
There are a few unique challenges to building a conversational app for a banking firm, which we overcome through some of the MindMeld’s impressive built-in functionalities.
To give a glimpse of both the dialogue management functionalities of the app and the slot-filling feature, here’s a snippet of a dialogue handler code. The logic in this function (check_balances_handler) is fairly simple as you are only expecting one entity — an account type for which the user is checking the balance for. If the account type entity is not specified by the user the slot filling logic will be invoked. You can find an example of a more complex handler function for the Banking Assistant here
That covers a brief overview of our new Banking Assistant blueprint application! If you would like to try it out, you can find more information here. For help developing your own application, take a look at our documentation.
Ritvik Shrivastava is a Machine Learning Engineer at Cisco’s MindMeld Conversational AI team. He holds his MS in Computer Science at Columbia University, specializing in Machine Learning and Natural Language Processing.
Abhi Sidhu is a Software Engineer at Cisco who specializes in providing practical solutions to emerging technological problems. He holds a BS in Computer Science from Cal Poly San Luis Obispo.
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