Build conversational experiences for auto insurance using Amazon Lex

Auto insurance coverage business are focusing on digital developments to fulfill consumer needs. Digital-first engagements offer customized coverage, transparent details, and smooth experiences. The shift to virtual channels for customer support that occurred throughout the pandemic is unlikely to revert to traditional channels for many customers. The modification in consumer habits continues to accelerate due to a growing population of tech-savvy customers who desire to interact with organizations online. Throughout the lifecycle, consumers anticipate a quick turn-around as they evaluate policy options (pre-purchase), make a premium payment (purchase), or report a claim (post-purchase). A faster and tailored service improves policy holder satisfaction and enables insurance coverage representatives to focus on building much deeper relationships.
In this post, we evaluate how you can use a pre-built option with Amazon Lex to allow rich, tailored interactions so insurance companies can offer faster handling of payments processing, declares reports, policy updates, and policy quotes.
Service introduction
Amazon Lex supplies the innovative deep learning performances of automated speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to construct applications with highly engaging user experiences and lifelike conversational interactions. Amazon Lex for Insurance uses pre-built services so you can allow more conversational experiences, quicker. The pre-built bots are set up with intents, sample utterances, and slot types for credit card use cases and are integrated with Amazon Connect contact circulations.
Lets evaluate a sample conversation concerning a policy quote as we cover the various components of the pre-built solution:

Agent: Welcome to ABC insurance coverage. How can I assist you today?
Caller: I want to get a new policy quote
Agent: Great. I can assist you with that. What kind of policy are you calling about? Auto, or Home?
Caller: Auto
Representative: Ok. I will need some info about you in order to serve you the best. Please state your date of birth, including the year.
Caller: January 1, 2001
Agent: Ok. What is your given name?
Caller: Joe
Representative: And your surname?
Caller: Smith
Agent: Ok, please say or enter your 10-digit telephone number.
Caller: 1234567890
Agent: Ok. State or enter the 5-digit postal code where you wish to insure your vehicle.
Caller: 1234
Representative: Tell me the make of the automobile.
Caller: Ford
Agent: And what is the design?
Caller: Focus
Agent: And year?
Caller: 2020
Agent: Ok. What is the expected annual mileage?
Caller: 10000 miles
Representative: Thank you for providing the information. Just a second while I bring up the quote …
Agent: Based on the postal code, mileage, and other automobile information, we can do $125 monthly or $1200 yearly billing.
Caller: Thank you!

In the preceding sample discussion, the agent first collects fundamental details (insurance coverage type, date of name, birth, and phone number). The pre-built option consists of bots for vehicle insurance coverage services that can be deployed on Amazon Lex to automate the conversations.
The AutoInsuranceBot contains intents for typical policy holder activities such as getting policy quotes, making superior payments, making a claim, discovering an advisor, adding or eliminating a chauffeur, and adding or eliminating a car. It includes the following intents:

GetPolicyQuote– This intent collects caller contact details and info associated to the automobile such as make, design, year, and annual mileage to process the policy quote

MakePremiumPayment– The intent gathers info such as the payment amount, source account ID, and date, and processes the payment

PaymentByCard– The intent gathers charge card details from the user to make a payment toward a policy

MakeAClaim– The intent gathers details associated to the occurrence and raises a claim

FindAnAdvisor– The intent recommends an insurance coverage representative, based upon the area and product category

AddDriver– The intent includes a driver to the existing policy by collecting the chauffeur details

AddVehicle– The intent includes an automobile to the existing policy with the necessary information such as make, design, and VIN

MakeModelYear– A follow-up intent to AddVehicle and MakeAClaim intents

EndConversation– An intent to end the discussion on caller inputs such as “Bye” and “I am done”

Fallback– The intent is invoked when the input doesnt match any of the configured intents

After the contact number is associated, the solution is all set to be checked.
Check the solution.
If you utilized an Amazon Connect circumstances for release, you can call in to the Amazon Connect phone number and engage with the bot. You can also check the option straight on the Amazon Lex console utilizing voice or text.
Car insurance: Key abilities.
Lets examine a few of the core abilities provided by the pre-built option, consisting of re-eliciting slots, duplicating essential info, utilizing context and context characteristics, and contact center flows.
Re-elicit details.
In a consumer service discussion, agents have to clarify a question as they collect information. If the caller doesnt respond with a particular part of the cars and truck, a follow-up question could be more authoritative: “Sorry, I did not follow that. The following code shows this discussion flow example:.

if previous_message:.
return dialog.elicit _ intent(.
active_contexts, session_attributes, intent,.
json.loads( previous_message)).
else:.
return dialog.elicit _ intent(.
active_contexts, session_attributes, intent,.
[
contentType: PlainText,.
material: “I am sorry. I am unable to repeat that.
Is there anything else I can help you with?”].

IAM access and secret crucial qualifications.
Optionally, an existing Amazon Connect circumstances (if you prepare to release on Amazon Connect).

Tidy up.
To prevent incurring any charges in the future, erase all the resources created:.

Amazon Connect contact circulation– AutoInsuranceContactFlow.

An AWS account.

PhoneNumber: phone_number,.
DOB: dob,.
PaymentAmount: payment_amount,.
PaymentMethod: payment_method, Date: date.
,.
120, 10).
dialog.set _ active_contexts(.
intent_request, ElectronicCheckPayment,.

The bot meaning consists of a total dialog along with the prompts to handle the discussion. Each bot also integrates with an AWS Lambda function which contains code to simulate business reasoning execution; combination with Amazon Kendra offers the ability to answer natural language concerns during the discussion.
Option architecture
Lets evaluate the general architecture for the service (see the following diagram):.

return
sessionState:
sessionAttributes: session_attributes,.
activeContexts: active_contexts,.
dialogAction:
type: ElicitSlot,.
slotToElicit: slotToElicit.
,.
intent: intent.
,.
requestAttributes: ,.
messages: messages.

Navigate to the Amazon Connect control panel and choose Phone numbers on the Routing menu in the navigation pane if you offered an Amazon Connect ARN throughout stack production.

After the caller is confirmed, the control is passed to the main insurance coverage bot to carry out deals (for example, make a payment) or offer the caller with inquired (for instance, “What good driver discounts do you provide?”). We utilize Lambda to run company logic needed for running transactions.
To respond to any user concerns, we set up an Amazon Kendra search index so the bot can look up the info and supply a reaction.
You can deploy the conversational experience on an Amazon Connect instance or integrate with your site.

IAM roles– LexRole, LexImportRole, LambdaRole, and ConnectRole.

Amazon Lex bot– AutoInsurance.

DynamoDB table– auto_insurance_table.

In the Parameters section, go into worths for the for Amazon Lex bots, DynamoDB table, and Amazon Connect contact circulation.

Amazon Lex bots.
Lambda functions.
DynamoDB table.
Amazon Connect contact circulation.
IAM roles.


Manage circulations.
The discussion circulation can differ depending on the deal. When making an insurance payment, the flow changes based on the option of the payment method. If the customer wishes to use a charge card as the payment approach, then the representative needs to gather the 16-digit card number, expiration date, and security code. If the caller chooses to use an electronic check, the representative needs to collect the account ID and the routing number. The pre-built services utilize a context management capability so you can manage such circulations. The bot utilizes 2 different intents to collect the payment details depending upon the payment type (card or check): PaymentByCard and PaymentByElectronicCheck. The MakePremiumPayment intent records fundamental info such as policy ID and sets the output context based on payment type. Upon the next user utterance, the proper intent is set off based on the input context attribute. The following code demonstrates how the contexts are handled:.

Evaluation the IAM resource creation and select Create stack.

Access to the following AWS services:.

# fulfilment of MakePremiumPaymentIntent.
if payment_amount:.
status, date, remaining_statement_balance, quantity.
= insurance_system. get_next_payment_details(.
customer_id).
dialog.set _ active_contexts(.
intent_request, CardPayment,.

Release the pre-built service.
To release this option, finish the following steps:.

if year_of_vehicle and not damaged_auto_component:.
previous_slot_to_elicit = dialog.get _ previous_slot_to_elicit(.
intent_request).
if previous_slot_to_elicit == DamagedAutoComponent:.
“.
User input did not get resolved to a slot worth. Re-eliciting the.
same slot one more time with a guided prompt.
“. timely =” Sorry, I did not follow that. Where did the damage happen?
You can state something like to the windscreen, to the bumper.
or to the door etc”.
return dialog.elicit _ slot( DamagedAutoComponent, active_contexts,.
session_attributes, intent,.
Repeat reactions.
Callers may want the representative to duplicate with a basic demand (” Can you state that again?”) to confirm a concern or just capture essential info. The pre-built service provides an ability to keep the reaction and then play it again when asked for by the caller. AMAZON.RepeatIntent reacts to the users expressions and words, to repeat the bots previous message. The following is the code for duplicating bot responses:.

Choose Launch Stack to release a CloudFormation stack in the Region of your option:.

if intent_name == Repeat:.
active_contexts = dialog.get _ active_contexts( intent_request).
session_attributes = dialog.get _ session_attributes( intent_request).
intent = dialog.get _ intent( intent_request).
previous_message = dialog.get _ session_attribute(.
intent_request, previous_message).

Amazon Lex to produce bots.
Lambda for business reasoning functions.
DynamoDB to develop the tables.
IAM with access to develop roles and policies.
AWS CloudFormation to run the stack.

Lambda function– AutoInsuranceBusinessLogic.

We utilize an Amazon Lex bot to first validate the caller by confirming info that is saved in a safe database.
We utilize Lambda to imitate access to backend systems and perform the authentication function. For the purposes of this post, the insurance plan data used for authentication is saved in an Amazon DynamoDB.

For Stack name, get in a name for your stack. This post uses the name auto-insurance-solution.

We consist of a design template that develops an AWS CloudFormation stack consisting of all these AWS resources, along with the needed AWS Identity and Access Management (IAM) functions. With these resources in place, you can use the pre-built solution for auto insurance on the Amazon Connect channel.
Prerequisites.
You ought to have the following requirements before we deploy the solution:.

Next, associate a contact number with the card services contact circulation.

# following is a dialog utility function.
def elicit_slot( slotToElicit, active_contexts, session_attributes, intent, messages):.
intent [ state]=InProgress.

Conclusion.
Amazon Lex for Insurance provides pre-built solutions that you can use to accelerate the delivery of engaging conversational experiences for increased consumer complete satisfaction. In this post, we evaluated an option for the car insurance coverage client service function, related to activities such as providing a quote, processing a claim, and making a payment. The pre-built solution provides a ready-to-deploy contact center setup with Amazon Connect. You can easily extend the option with extra discussion streams that specify to your organizations requirements. AWS allows a nimble infrastructure for insurance organizations to optimize and innovate your company and deepen customer relationships. Attempt the pre-built insurance coverage services on Amazon Lex today!

About the Author.
Sandeep Srinivasan is a Product Manager on the Amazon Lex group. As a keen observer of human behavior, he is enthusiastic about customer experience. He invests his waking hours at the intersection of people, technology, and the future.
Harish Lanka is a Conversational AI Specialist on the Smartbots.ai team. He spends his time speaking with organizations, comprehending their pain points and developing services using conversational AI. When he is not at his desk, he is either trading stocks, enjoying football or reading about spirituality.

After a couple of minutes, your stack must be complete. The core resources are as follows:.

if not session_attributes:.
session_attributes =
session_attributes [ previous_message] = json.dumps( messages).
session_attributes [ previous_dialog_action_type]=ElicitSlot.
session_attributes [ previous_slot_to_elicit] = slotToElicit.

Amazon Lex provides the sophisticated deep knowing performances of automated speech acknowledgment (ASR) for converting speech to text, and natural language understanding (NLU) to acknowledge the intent of the text, to allow you to build applications with highly appealing user experiences and lifelike conversational interactions. The pre-built bots are configured with intents, sample utterances, and slot types for credit card use cases and are incorporated with Amazon Connect contact circulations.
If you utilized an Amazon Connect circumstances for deployment, you can call in to the Amazon Connect phone number and interact with the bot. The contact flow uses a Get customer input block to invoke the Amazon Lex bot. Amazon Lex for Insurance offers pre-built solutions that you can utilize to accelerate the shipment of engaging conversational experiences for increased customer satisfaction.

,.
120, 10).
prompt=”Okay then, how would you like to pay,.
by charge card, debit card, or electronic transfer?”.
return dialog.elicit _ intent(.
active_contexts, session_attributes, intent,.
[]. Contact center streams.
You can release the pre-built solution as part of Amazon Connect contact circulations. The contact flow to which they are sent out is the one designated to the telephone number that they called when consumers call into your contact center. The contact circulation uses a Get customer input block to conjure up the Amazon Lex bot. The following diagram illustrates the vehicle insurance coverage contact circulation.

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