kurhula / financial-demo

A demo for a financial services bot

Financial Services Example Bot

This is an example chatbot demonstrating how to build AI assistants for financial services and banking. This starter pack can be used as a base for your own development or as a reference guide for implementing common banking-industry features with Rasa. It includes pre-built intents, actions, and stories for handling conversation flows like checking spending history and transferring money to another account.

Table of Contents

• Install dependencies
• Run the bot
• Overview of the files
• Things you can ask the bot
• Testing the bot
• Rasa X Deployment
• Action Server Image

Install dependencies

Run:

pip install -r requirements.txt

To install development dependencies:

pip install -r requirements-dev.txt
pre-commit install

Run the bot

Use rasa train to train a model.

Then, to run, first set up your action server in one terminal window:

rasa run actions

In another window, run the duckling server (for entity extraction):

docker run -p 8000:8000 rasa/duckling

Then to talk to the bot, run:

rasa shell --debug

Note that --debug mode will produce a lot of output meant to help you understand how the bot is working under the hood. To simply talk to the bot, you can remove this flag.

Overview of the files

data/core.md - contains stories

data/nlu.md - contains NLU training data

actions.py - contains custom action/api code

domain.yml - the domain file, including bot response templates

config.yml - training configurations for the NLU pipeline and policy ensemble

tests/e2e.md - end-to-end test stories

Things you can ask the bot

The bot currently has five skills. You can ask it to:

1. Transfer money to another person
2. Check your earning or spending history (with a specific vendor or overall)
3. Answer a question about transfer charges
4. Pay a credit card bill
5. Tell you your account balance

It also has a limited ability to switch skills mid-transaction and then return to the transaction at hand.

For the purposes of illustration, the bot recognises the following fictional credit card accounts:

• gringots
• justice bank
• credit all
• iron bank

It recognises the following payment amounts (besides actual currency amounts):

• minimum balance
• current balance

It recognises the following vendors (for spending history):

• Starbucks
• Amazon
• Target

You can change any of these by modifying actions.py and the corresponding NLU data.

Testing the bot

You can test the bot on the test conversations by running rasa test. This will run end-to-end testing on the conversations in tests/conversation_tests.md.

Note that if duckling is running when you do this, you'll probably see some "failures" because of entities; that's ok! Since duckling entity extraction is not influenced by NLU training data, and since the values of time entities depend on when the tests are being run, these have been left unannotated in the conversation tests.

Rasa X Deployment

To deploy financial-demo, it is highly recommended to make use of the one line deploy script for Rasa X. As part of the deployment, you'll need to set up git integration to pull in your data and configurations, and build or pull an action server image.

Action Server Image

You will need to have docker installed in order to build the action server image. If you haven't made any changes to the action code, you can also use the public image on Dockerhub instead of building it yourself.

See the Dockerfile for what is included in the action server image,

To build the image:

docker build . -t <name of your custom image>:<tag of your custom image>

To test the container locally, you can then run the action server container with:

docker run -p 5055:5055 <name of your custom image>:<tag of your custom image>

Once you have confirmed that the container works as it should, you can push the container image to a registry with docker push

It is recommended to use an automated CI/CD process to keep your action server up to date in a production environment.