Monthly Archives: March 2021

The need for Chatbots in Banking in a post-COVID-19 world

Banks, which have typically had a heavy offline interaction with customers, today, face challenges to business continuity as employees are stranded in various locations with uncertain return dates. Apart from this, dramatic changes in customer demand are putting banks under huge stress: sharp declines in demand present serious financial challenges to many businesses, while those facing demand surges and resource shortage risk disappointing and disengaging customers. COVID-19 has disrupted operations and will have prolonged impacts on continuity of operations, modes of working, and growth patterns. CIOs need to respond to the crisis with both short- and long-term actions to increase resilience against future disruptions and prepare for rebound and growth.

Banks have long had on their agenda to streamline and automate processes. The Banks who were leaning more towards cost-saving were exploring RPA for automation of administrative processes and those banks which were cash-rich and were looking to better their customer experience were looking towards Conversational AI to make workflows more efficient. And in times like these, the best of business continuity plans and sustainability forecasts have fallen short of making predictions for a global lockdown and disruption.
Let’s walk through the challenges faced by the banking industry and a few solutions to undertake for the short-term and long-term.

Digital transformation challenges faced by banks:

One of the biggest challenges top executives at banks face is the gamut of transformation required. From internal processes to customer-facing processes, all areas require digitization and automation.

As per the research done by our Analyst partner Gartner, data-driven technologies are still seen as a game-changer, but many banks are perplexed on how to move forward?

The 2020 Gartner CIO Survey asked banking IT leadership to identify which technology capabilities they see as crucial to their organization’s evolution. For the 2020 survey, data analytics, and artificial intelligence (AI) were at the top of the list and roughly equal for financial services CIOs (see Figure 1).

Figure 1: Game-Changing Technologies

Figure 1 Source: Gartner

During the last few years, there has been much discussion about how customer engagement in banking can better serve and provide new product opportunities to customers, considering all the points of engagement offered by banking institutions. Often-used buzzwords like “omnichannel” or “multichannel” have punctuated this discussion. Often, however, banks find that they are unable to digest and analyze customer data and transactions well enough to support the user interface or customer journeys.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Challenges faced by bank’s agents and employees:

With business continuity completely disrupted, employees are finding it hard to run the business as usual, for processes that were offline and even for the processes that were online.

Various quarantine measures and travel limitations undertaken by different Countries and cities have created big uncertainty around employees’ return to work dates. Even returning employees are often asked to self-quarantine for seven to 14 days. Internationally, indefinite travel restrictions by many countries are causing similar uncertainties to business operations. Operations have either been suspended or run on a limited capacity. Since the outbreak, demand for digital collaboration tools has skyrocketed as organizations are deploying these tools so that employees can work remotely.

Challenges faced by the bank’s customers:

Customers today are more available on conversational channels such as messaging apps and IoT devices. Whereas banks have successfully transitioned to mobile banking and app-based banking.

Where banks are focussing on icons, menus, and clicks; users have moved to expect a conversational user experience on voice and chat. Where banks are focusing on transactional coverage, customers are expecting transactional, service-related, and even advisory services to be made available, on the channels of their choice.

In Figure 2, Gap between Mobile Banking and Conversational Banking.

Figure 2. Source: Accenture Conversational Banking Insight

How can Conversational AI help banks drive business continuity and growth?

Conversational channels have the potential to help banks solving this customer interaction conundrum, capitalizing on three major consumer and technological trends:

Trend #1 Messaging is now the preferred customer touchpoint

Messaging apps are now the dominant form of mobile interaction, enabling easy, fun interactions on the move. Their simple, intuitive text or voice-based interfaces are loved by Millennials, as well as by consumers typically more reluctant to embrace digital channels too. They’re also AI-ready, offering easy integration with chatbots and cognitive agents.

Trend #2 AI is becoming ready for B2C

As AI continues to develop, bots are becoming more human-like in their interactions, and can now be built with self-learning capabilities. That enables not only the automation of repetitive customer care tasks but also low-value advisory services.

Trend #3 Mass personalization and liquid expectations

By leveraging new data-driven insights, companies are able to offer unmatchable customer experience and personalized digital services at a mass level. This creates competition across, as well as within industries, as customers’ “liquid expectations” means each digital interaction is expected to be as good as the best last experience, regardless of brand or industry.

#1 Solution: Conversational AI for Agents in Banking:

Using Yellow Messenger’s service desk automation, you can leverage the true power of AI by closing the automated-learning loop between humans, platforms, and bot. Empower your agents to do more with less at hand, by:

a. Providing agent assistance:

Using conversational AI, you can enable your agents to be highly efficient by enabling access to customer sentiment and the past context of the conversation. Integration with CRM and service desk backend tools enables the bot to show relevant data like past case references, customer history, and journey map. Agent assist feature searches for past resolutions by agents for similar queries to provide recommended responses which can be audited by the agents and sent across without typing a single letter. Entire agent workflows can also be automated and triggered by the bot on behalf of the agent at the click of a button.

b. Live Agent Transfer: Deploy Virtual Assistants to be the first line of respondents and take the load off ~65% queries which are standard, and only handoff to a live agent the queries that really need their input. Cognitive agent routing capabilities ensure that each ticket gets assigned to the most relevant agent-based on concurrency, ticket details, agent availability, past conversations, and several other factors. Agents can either chat with the customers or use voice and video calling capabilities to resolve issues on call. Our intelligent queue management allows you to manage and allocate ticket flow even when agents are offline. The service desk can be customized to show relevant data using integrations with your CRM, IT requests and support systems and is available as a mobile app for agents who do not have access to laptops at home.

#2 Solution: Conversational AI for Customers in Banking:

The implications of conversational AI in banking are far-reaching, especially when it comes to Customer Experience. Here’s how Virtual Assistants can help Banks enrich their customer experience:

a. Banking Virtual Assistant: Virtual assistants can provide a personalized banking experience, on-demand, across channels like Web, App, Messaging, Voice, and more. These virtual assistants can help answer FAQs and even take the role of advisory and upsell products that are more likely to be appreciated by the customer.

b.Contact Centre Automation: No more long wait times on calls. No more repeating your query at multiple handoff points. With IVR automation, customers can reap the benefit of being served in less than 1 minute and banks can enjoy cost and experience efficiencies.

Summary:

Conversational AI in banking is a necessity to drive business continuity at customer and employee end. For more information, visit: https://yellowmessenger.com/

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The need for Chatbots in Banking in a post-COVID-19 world was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How Alexa Education Skills can Help You Learn New Things

Nowadays there are more than 120 000 Alexa Skills available for everyone. Considering this, it’s a bit difficult not to find one that can help you learn new things. Even if your interest to learn goes for less popular topics. Probably there are Alexa learning Skills that can help you gain new information.

Amazon Alexa is attracting more and more attention lately. This has made it a launching point for many industries. Be it for pure entertainment or for more technical ones, everyone is rushing to have their Skill published.

All of this with the intention of making their presence in the voice world, a new market opens for everyone.

How to learn new things with Voice Applications

Using a smart speaker, like Amazon Echo, to learn new things might seem kind of strange. However, every Skill has been built with the intention of offering a plausible experience for every user.

First, let’s clarify what Voice Applications are

  • What are Voice Applications?
    Voice Applications are similar to applications you use on your smartphone. They can offer an experience similar to visiting a website online to receive a certain information, or to interact with others.

These Voice applications, called so for the simple fact that you control them using only your voice, when referring to Amazon Alexa are called Skills.

If you want to find a new Skill, all you need to do is go to the Alexa Skill Store. There you can do a simple search with certain keywords or search by category.

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1. How Chatbots and Email Marketing Integration Can Help Your Business

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3. My Journey into Conversation Design

4. Practical NLP for language learning

Amazon Alexa Learning Skills

There are already plenty of different Skills to learn from. You can find Skills to help you Meditate, teach you historical facts and events, learn soft skills, and how to make something with step-by-step instructions.

  • For example, a great way to learn how to do something is the wikiHow Skill. It has over 180 000 articles accessible from everyone. From soft skills to practical and technical ones, it can be really helpful.
  • If you need a guide on how to meditate you can try Real Simple Relax or a Guided Meditation from Headspace and take you first steps towards meditation.
  • In case you want to learn new facts by testing yourself, you can try Question of the Day and start everyday with a challenge.

What about the Kids?

When it comes to kids and teens, there are thousands of Skills available. They help kids to be entertained or help them with their homework. With it, they can learn new facts, play games, storytelling, learn new languages.

They are a great way to have your kids learn new skills, listen to different stories and audiobooks for their age. Also, they can learn new words, and develop their practical skills (math, physics, etc.)

Don’t forget to give us your 👏 !

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How Alexa Education Skills can Help You Learn New Things was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Review Analysing Application using NLP — Part 1

Review Analysing Application using NLP — Part 1

In my previous article, I talked about how machines communicate with humans. In there, I have mentioned the NLP — Natural Language Processing. NLP did a significant role in communication with computers and humans. Today I am going to implement a Simple Review Annalysing Application using NLP and python. I used PyCharm IDE for coding.

Review Analysing Application is a Text Classification application. This article has four sections. they are

  1. Import dataset
    2. Cleaning dataset
    3. Create bag_of_words Model
    4. Create a Classification Model
    5. Test Accuracy

1. Import the dataset

#CSV dataset vs TSV dataset

CSV means Comma-Separated Values, and TSV means Tab-Separated Values. Simply if the dataset is in .csv format, the values in the dataset are separated by comma(“,”). If the dataset is in .tsv format, the values in the dataset are separated by a tab. Usually, for NLP, we are getting .tsv form. Because in NLP, we are dealing with sentences. In sentences, there have most commas. See the example text,

Loved it...friendly servers, great food, wonderful and imaginative menu.

In this example, we have more commas. So if we used .csv, the separated values might be incorrect. But in here no tabs. Therefore we usually get the dataset format .tsv for the NLP.

#Importing the dataset

import pandas as pd
# Importing the dataset
dataset = pd.read_csv('DataSet/review_dataset.tsv', delimiter ='t', quoting = 3)

To import the dataset, I used pandas library’s read_csv function. I set the delimiter as tab because the dataset is in .tsv format. And also, I set quoting to 3 because I want to ignore double quotes also.

fig 1 — After importing the dataset

Here I have 1000 reviews. That dataset has two columns. They are Review and Liked or Not. The review section has the String text given by someone. And Liked or Not section has a Boolean value about his satisfaction.

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3. My Journey into Conversation Design

4. Practical NLP for language learning

2. Clean the dataset

Firstly I clean one review. And then I apply that method to all reviews.

# get one review
review = dataset['Review'][0]
fig 2 — After getting one review

For clean the reviews, I use a library called re.

# Remove all things except letters

For the review analysing I want only words. I don’t want any punctuation marks or any numbers. Therefore I remove all unwanted stuff. And I store that sentence again in the review variable. For that, I used sub function in re library.

# remove all things except letters
import re
review = re.sub('[^a-zA-Z]', ' ', review)
fig 3 — After Remove all things except letters

Now you can see there haven’t any punctuations or numbers.

# Set all letters to lowercase

and then I change all letters to lowercase. because I want to number of words in the bag_of_words

# set all letters to lowercase
review = review.lower()
fig 4 — After Set all letters to lowercase

# Convert the string to a list

I convert the string value to a list because another step I’m going to remove non-significant words. For that, I use a for a loop. therefore I convert string to a list

# convert the string to a list
review_words = review.split()
fig 5 — After Convert the line to a list

# Remove non-significant words

In the review, there have words to unwanted for the review analysing purpose. Such as I, am, are, this, that… So I also remove those words. For that, I used the nltk — Natural Language Toolkit package. In nltk have a list named ‘stopwords’. Using that word list, I can remove non-significant words.

# remove non significant words
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
review = [word for word in review_words if not word in set(stopwords.words('english'))]
fig 6 — After Remove non-significant words

# Stemming => taking the root of the words

we are ding stemming because we want to remove unwanted words from the bag_of_words. Directly stemming means taking the origin of the word. As an example, I get a sentence,

Loved it...friendly servers, great food, wonderful and imaginative menu.

In this sentence have a word named ‘loved’. But this is not the root of that word. The origin of that word is ‘love’. ‘love/loved/loving’ these all words are given the same sense about the review positive or negative. Therefore we don’t want those all words. We can use only the root word for that. If we use the origin word only, our bag_of_words also will be small. We use stemming to reduce the number of words in the bag_of_words.

Simply we,

loved/loves/loving/love   =>   love

for that, I used PorterStemmer in nltk.stem.porter library.

# stemming => taking the root of the words
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
def stemming(word):
return stemmer.stem(word)
review_after_stemming = [stemming(word) for word in review]
fig 7 — After Stemming

# Convert list into a string

Now the cleaning process is over. Therefore I again convert that list to a string.

# convert list into string
review = ' '.join(review_after_stemming)
fig 8 — After Convert list into a string

# Doing this method to all reviews

Now I’m doing that cleaning process to all reviews.

# doing this method to all reviews
corpus = []
for i in range(0,1000):
review = dataset['Review'][i]
review = re.sub('[^a-zA-Z]', ' ', review)
review = review.lower()
review_words = review.split()
review = [word for word in review_words if not word in set(stopwords.words('english'))]
stemmer = PorterStemmer()
review_after_stemming = [stemming(word) for word in review]
review = ' '.join(review_after_stemming)
corpus.append(review)
fig 9 — After Cleaning all reviews

Now 1 – Import dataset and 2 – Cleaning dataset is done. We will see other sections in Review Analysing Application using NLP — Part 2.

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Review Analysing Application using NLP — Part 1 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

DL models to train chatbot

What possible options can I explore for generative models, used to train large datasets for chatbot building. The chatbot has to be built for a large scale website’s support team using python, tesnorflow etc.. . So after the pre processing of dataset, I am confused on how to approach this further. There are various github projects but that has like mixed things up. Google search gives BERT, GPT as possible options. But I need suggestions on how to go about it.

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How can AI & Chatbots Improve Support Agent Experience & Productivity

With the increased expectation for a speedy resolution and more interactive engagement, customer service representatives (CSR) alone cannot handle the rapidly evolving needs of customer support. An American Express survey found that 78% of consumers have bailed on a transaction because of a lousy service interaction.

As the pressure to scale up processes and deliver quality customer engagements mounts, contact centers witness higher attrition and agent turnover. Hence, to be truly successful in providing exceptional customer experiences, an organization needs to view agent experience, meaning how efficient, empowered, and effective its agents are, as an integral part of its overall customer support strategy.

To augment agent effort, chatbots and other AI-driven technologies are making their way into contact centers. Let’s discuss how a hybrid, co-existential human and AI model improves support agent experience and productivity.

Learn more: https://botcore.ai/blog/chatbots-agent-experience-productivity/

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Can Credit card Chatbots double the customer base?

As customer experience becomes one of the primary drivers of growth , banks, and financial services institutions are increasingly looking at innovative technology solutions to simplify customer journeys as a strategic tool for growth. Conversational AI is making a mark as a primary agent of change as banks attempt to increase customer satisfaction while making their processes more efficient.

Conversational agents (Chatbots) have been helping banks save a precious 4 minutes per customer interaction. By 2022, these bots will power more than 90% of all banking interactions, including those related to credit cards. Even now, Credit Card chatbots are powering the entire life cycle of customer use, from application to payments.

Credit Card Life Cycle

Irrespective of the type of business, there are five primary stages of a customer life cycle; reach/awareness, acquisition, conversion, retention, and advocacy. From the perspective of credit card customers, banks/financial institutions and customers need to perform the following activities for each of the above phases;

Reach/Awareness

This stage is where a prospective customer becomes aware of various credit card services by a bank/institute and the benefits a credit card brings to them. To drive home the point, banks can also share special offers for customers to obtain the card.

Acquisition

If the cards’ availability and offers make the customers interested, the next stage is to capture this interest. This interest becomes a lead that banks can then pursue to win over a prospective customer. Banks can ensure the customers’ interests by providing more nuanced information, including each type of card’s benefits. These benefits include rewards and cashback, loyalty programs, and any other incentive a customer gets by availing of the card. Based on this information, the customers would either make a preliminary application or provide the contact information so that a bank can reach out with more details.

Conversion

If the customer is convinced about the benefits and applies for a credit card, the bank needs to get the required document to ascertain the eligibility. Banks also need to calculate the customer’s creditworthiness and ensure there is no fraudulent or inaccurate information. If everything is alright, the banks then issue and activate the card.

Customer retention involves two different aspects for the bank; they need to ensure that they are making profits from the credit card operations while ensuring that they do not go away. From the operational perspective, once the customers start using the card, banks need to keep track of spending and invoice the customers at predefined intervals. They need to send payment reminders until the customers pay. The banks need to conceptualize and convey continued rewards and benefits to the customers on the retention front.

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One of the critical phases of the consumer life cycle is continued loyalty and advocacy, where the customers become your brand ambassadors and growth agents. This stage is where your loyalty initiatives, including referral programs or enhanced credit limits for better customers, can ensure your customers become your advocates.

Cross-cutting Concerns

Apart from these lifecycle stages, certain events may happen anytime during the life cycle. Primary among them are users losing the cards or defaulting on the payments. In both cases, the banks may have to block the existing cards. Apart from this, there might be customer inquiries, statement requests, or requests related to adjusting the credit limits that can take place anytime.

How Credit Card Chatbots Help Both Banks & Customers

Well-designed chatbots can be valuable tools to serve credit card customers throughout the lifecycle effectively. Not only will they contribute to customer satisfaction, but they can ensure the efficient execution of operations for banks or financial services institutions.

Reach/Awareness

The conversational interfaces (chatbots) can succinctly inform your audience during natural conversations about credit cards you offer and what benefits they bring for the customers. These bots can work with multiple channels, including websites or your apps on mobile devices. These bots can be part of your comprehensive customer service toolkit to provide a consistent experience.

The AI technology powers the bots through natural language processing to identify the right context to introduce your credit card services without being intrusive. Possessing the capabilities of contextual conversations makes compelling for banks from a customer satisfaction point of view.

Contextual Targeting

can gain additional intelligence by analyzing the historical data about spending habits, credit history, transaction history, and many other parameters. By leveraging the intelligence generated through such analysis, you can utilize chatbots to identify the right audience segment that can benefit from credit card products.

By addressing the customer pain points through such behavioral and contextual analysis, banks & FIs can establish a proactive conversational channel. Customers value relevant information rather than random intrusive advertisements.

Such intelligent outreach can also utilize personalization by addressing individual customers’ unique needs and increasing the conversion rates for banks.

Learn more: 9 Best Chatbots in the Financial Services Industry

Acquisition

With chatbots, customers can get increasingly detailed information without too many hassles. Such data can include details about various cards, individual benefits, rewards, and eligibility, all with the natural conversational patterns. Bots can even present personalized information for individual customers to help each customer understand this information efficiently.

The proactive ease of information availability helps increase customer interest in your card product. Once the customer has all the information, they can apply for a specific card through the chat interface.

Conversion

Conversion is a crucial stage from an institution’s perspective as this stage must maintain a balance between customer service and operational efficiencies. Credit card chatbots ensure customer satisfaction by facilitating seamless interactions for customers. Be it uploading the documents by customers or informing them about the outcomes of each process step.

The underlying computer vision and natural language processing algorithms, along with predictive analysis and anomaly detection capabilities, find probable fraudulent documents and risky applications for better scrutiny. Of course, such capabilities rely heavily upon the algorithms having trained with large and accurate datasets. But gradually, these machine learning algorithms become more accurate through self-learning. The can act as conduits of all this information in natural ways between customers and banks.

Credit card chatbots play a vital role in retention, given their capabilities to facilitate proactive interactions with customers. The bots can remind customers about their dues and enable customers to make payments against those dues.

The bots can help convey any probable fraudulent transactions flagged by the underlying algorithms. Quick identification of such transactions helps both banks and customers against threats and risks.

Through the help of predictive analysis using Machine Learning (ML) and Artificial Intelligence (AI) algorithms, the intelligence component of the credit card chatbots can identify customers with higher flight-risk. Banks can then proactively take appropriate retention measures. Proactively taking such measures helps banks ensure higher retention rates.

Advocacy/Loyalty

As the prime drivers of customer satisfaction, conversational credit card chatbots help banks inform customers about loyalty programs and their benefits. They also facilitate customers to utilize those benefits.

Based on spending patterns and payment history, bots can suggest enhanced credit limits for the customers. This way, bots become agents of growth for banks and financial institutions.

Using demographic data and credit card purchase history, credit card companies can offer targeted promotions and deals to customers digitally.

As customer satisfaction increases through such personalized, contextualized, and practice information availability, the customers, in turn, become your ambassadors, recommending your services to others.

Banks can utilize the bots to advise customers on overall financial health management. Rather than customers reading a manual or an article on money management, a chatbot providing personalized financial advice would be a great way to turn a customer into your advocate and ensure long-lasting loyalty.

Cross-Cutting Concerns

can help customers block their lost cards and reapply for the new card. Royal Bank of Scotland’s (RBS) online chatbot “Assist”- earlier called “Luvo”- is an example of how customers can report lost cards and request blocking them .

Bots can also identify non-paying customers and send them reminders about pending payments. Upon non-payment beyond a threshold, the underlying business logic can also block the card.

Banks can also enhance the effectiveness of these bots and entire operations by capturing customer feedback seamlessly.

Fraud Detection

Fraudulent transactions are a significant concern for banks and financial institutions. With bots, banks & FIs can leverage predictive analytics into their existing fraud detection workflows to reduce false positives. Underlying AI capabilities could analyze and score credit card transactions on their likelihood of being fraudulent by assigning a risk probability score, using predictive analysis or anomaly detection.

Predictive analytics algorithms do need extensive training on a large amount of data. However, the training allows the algorithm to understand an acceptable transaction versus a possible fraudulent one. Such training may also discover previously undiscovered fraud methods and start flagging more fraudulent transactions. MasterCard’s “Decision Intelligence,” the fraud-detection solution, is an excellent example of how the power of ML & AI can help detect and possibly prevent a fraudulent transaction.

Learn how AI Chatbot will transform the Mortgage Industry in 2021

What care should you take while using Credit Card Chatbots?

While chatbots help efficient credit card operations and higher customer satisfaction, they need careful consideration and efforts to be effective.

1. You must ensure that you have trained the bot with enough high-quality data. The ML algorithms have better accuracy if they utilize large datasets. At the same time, inaccurate data labeling may result in erroneous inferences by bots. Both of these scenarios may harm customer sentiments.

Such training also determines the questions and queries that the bot can answer. Banks should be careful about including data that represents the entire cross-section of their customer demographics and operations. For example, institutes must train the chatbots in multiple languages that their customers speak to work in a multilingual environment. While some of these requirements may not be critical, thinking about such edge cases can provide you with an edge when it comes to customer delight.

2. You should use it as another channel of communication, not the only one. Your customers should have other options to perform the same operations if they find them more comfortable. For example, many customers may prefer to communicate via email in bits and pieces. Consistent omnichannel experience is critical for better customer satisfaction.

3. Let your customers also choose to talk to a human assistant any time they want. Limiting their options to virtual assistants may prove to be counterproductive.

4. Instead of rolling-out the bot’s capabilities in one go, incrementally releasing facilities help the customer better adapt to them. You can also benefit from customer feedback to provide better options for solving their acute problems. Once they see the benefits, you can then gradually roll-out increasingly complex use cases.

What are the Advantages of a Credit Card Chatbot?

A Credit Card Chatbot can benefit banking institutes in many ways, such as:

  • It can reduce the human agents’ workload by automating repeated queries of the new users and customers.
  • Enable an experience for your customers using different channels for interaction.
  • Stay available 24/7 and during peak hours to answer the repeated queries of customers.
  • It can speed up operations and reduce the cost of your operations.
  • Educate thousands of users at the same time about loyalty programs, rewards, and special offers.

Given the advantages, the use of chatbots to manage credit card lifecycles is growing fast. Bank of America, a banking and chatbot implementation leader, uses their chatbot, Erica, to serve clients with comprehensive service for their credit card transactions. Many other institutions like JPMorgan Chase and Fargo Wells are using chatbots to help their banking operations increasingly. As the market leaders see the benefits, the rest of the institutions follow the lead.

Check your credit score with this Smart Skill:

With credit card chatbots, banks and financial institutions can ensure better customer interactions and get an intelligent agent contributing to business growth. These automated intelligent agents , through conversational interfaces, offer better targeting by recommending the right product to the right customers. By analyzing customers’ interactions and data, banks can offer customized products to each customer via the . Through personalized communication, banks can ensure better customer relations, ultimately resulting in sustained business growth, and chatbots are vital instruments for that.

Want to develop an Intelligent Virtual Assistant solution for your brand?

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Can Credit card Chatbots double the customer base? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to create a FAQ Chatbot with Rasa?

A FAQ Chatbot will bring your First-Level Support to a new level!

In this article we will show how to implement a FAQ chatbot with Rasa to answer FAQs and fill in forms. For working with forms, the Rasa framework with the FormPolicy offers a simple way to create simple yet user-friendly bots for this task without the need to write extensive dialogs.

In the article “ Create a chatbot using Rasa” we described how to create a simple chatbot with Rasa. We showed how to install Rasa and initialize a first project. We also illustrated how to have a simple dialog with the bot.

To show this use case, we describe a chatbot which allows to reserve a hotel room and answers basic questions about the hotel. An exemplary implementation can be found at https://github.com/Steadforce/rasa_faq_form_bot. This article describes the creation of the chatbot using the implementation found there.

Rasa 1.8 was used for the creation. Since at the time of writing this article only the first alpha version of Rasa 2.0 was introduced, Rasa 2.0 is not covered. First, we will start with our chatbot answering questions.

Building the FAQ Chatbot

To start, initialize a project in an empty folder using the Rasa CLI. If Rasa is not yet installed and you don’t know how to set up an initial project, you can follow the steps in our previous tutorial.

After initialization of the project we delete the *intents, stories and unnecessary configurations of the domain. Now, intents are created for three questions. The FAQ chatbot can be asked questions about the location of the hotel, its appearance and possible activities there. We first create these within the NLU data under data/nlu.md.

With the ResponseSelector Rasa has a functionality that simplifies the handling of small talk and FAQs. This functionality is used below. For this purpose, the questions within the NLU data have to be specially marked, ie. the questions need to followthe pattern ## intent: faq/ask_<name>. The question about the appearance of the hotel is shown below as an example. To use the ResponseSelector, at least two intents must be created.

## intent: faq/ask_location
- Where is the hotel?
- Where can I find you?
- The hotel is where?
- In which city is the hotel?
- Where is the hotel?

After creating the intents, the responses, i.e. the reactions of the bot to the user’s intent, must be prepared. This is not done via responses within the domain as described in the previous article. To avoid the domain.yml file becoming too confusing and overloaded with configurations, we add a new responses.md to the data folder.

In this file the responses. to the previously defined questions are generated. Responses are created similarly to stories, but with only one switch between user and chatbot.

# Ask location 
* faq/ask_location
- The hotel 'To speaking bot' is located in the heart of Munich with a panoramic view of the Alps.

The advantages of the ResponseSelector are now evident in the creation of the story. It is not necessary to create multiple stories, which deal with each individual question. Within the stories under data/stories.md all FAQs are treated in the same way and there is no distinction between them. This can be a major advantage especially for chatbots with a lot of different FAQs.

## Some questions for faq
* faq
- respond_faq

Ultimately the intent faq and the action to answer the questions of the domain (file: domain.yml) must be added so that the FAQ chatbot can be trained with the command rasa train and then tested via rasa shell.

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4. Practical NLP for language learning

FAQ Chatbot: Filling a form through dialog

The FAQ chatbot is now able to answer simple questions. In the following we enable the Bot to accept reservations. In order to do this, Rasa offers the possibility to fill in Forms. This article describes only a PoC with limited functionality.

With Entity Extraction Rasa offers the possibility to enter complex data types into forms. Thus, it is possible to correctly interpret user data, such as “Tomorrow afternoon at 14:00”. For more information please refer to the Rasa documentation at https://rasa.com/docs/rasa/nlu/entity-extraction.

To fill a form in Rasa you first have to add the requiredFormPolicy under policies in config.yml.
The next step is to add the form that is to be filled to the domain within the domain.yml file.

forms:
- hotel_form

The required policy is added, and the form is included in the domain. As with the questions to be answered, we generate a story, which shows the process of filling the form. For this we first set up the intent request_room within the domain and define sample contents for the training under data/nlu.md.

Here we add two different versions of the intents. The first intent only contains the information that a room must be reserved.

## intent:request_room 
- I would like to book a room

Another possibility is to transfer information already in this intent. So, you can specify so-called entities within this intent. In this example, the arrival date is used for this purpose. It is important that the slot and the entity have an identical name, in this example date. Slots and entities will be specified in the further steps.

## intent:request_room  
- Is a room available from the [11/02/2020](date).
- I would like to book a room for the [10/03/2020](date).

These two intents now make it possible to fill one of the fields at the beginning of the form. We also include them in the domain under domain.yml. During the further completion of the form, the user is not asked which date he wants, since this date was already specified at the beginning.

Now we will build a story that shows the optimal case for filling the form.

## Form Happy Path  
* request_room
- hotel_form
- form{"name": "hotel_form"}
- form{"name": null}

iTo fill the form in a dialog, we write the Python class HotelFormwithin actions.py. This class inherits from the classFormAction and implements the four methods name, requiered_slots, submit and slot_mappings. The purpose of each method and its implementation is described below.

name

This method simply returns the name of the form as defined within the domain. This allows Rasa to perform *mapping between the Python class and the form defined within the domain.

def name(self) -> Text:
return "hotel_form"

required slots

This static method returns all mandatory fields/slots of the form. This is done as a list. The order of the list also defines the order in which the chatbot will ask the user for the mandatory fields. In this example, the number of people, the date of arrival, the number of nights and the room type are the required fields of the form.

It would also be possible to dynamically define fields as mandatory based on the user’s input. A use case might be, if children sleep in the room, the indication whether a crib is needed. If no child sleeps in the room, this question is unnecessary and is therefore not asked. In our example, however, dynamic mandatory fields are not included and therefore the code looks like this:

@staticmethod
def required_slots(tracker: Tracker) -> List[Text]:
return [
"number_of_persons",
"date",
"nights",
"room_type" ]

To use the slots we define them under slots in the domain. This is also done under domain.yml. DThe definition of a slot specifies the name, the data type and in special cases the possible values of the slot. In this example we only show the slots number_of_persons and room_type.

For the two other slots, a correct data type must be selected and then the procedure is the same as fornumber_of_persons. The slot room_type is an example of a slot with a limited choice of values. At this point the user has the choice between two variants, which are displayed in the chat with the bot as two buttons.

slots:
number_of_persons:
type: float
room_type:
type: categorical
values:
- Junior
- Senior

To complete the changes associated with this method we still need to prepare the questions that the FAQ chatbot will ask to fill the slots. These must also be created within the domain (file: domain.yml). They have to be set up according to the fixed structure utter_ask <slot_name>. A special element is the slot room_type. In order to provide the user with only the two selection options as buttons, we create this response as follows:

utter_ask_room_type:
- text: "In which room do you want to sleep. In a junior or senior suite?"
buttons:
- title: "Junior Suite"
payload: '/choose{"room_type": "Junior"}'
- title: "Senior Suite"
payload: '/choose{"room_type": "Senior"}'

submit

The submit method is executed when the form is completely filled. It may be used to save the reservation. This example only executes the action utter_submit. This action prints the user’s input and thus confirms it.

def submit(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict]:
dispatcher.utter_message(template="utter_submit")
return []

In order to execute the response utter_submit, it must be defined within the domain. By specifying the name of the slot within the response text, the current value can be output. For example, if we want to output the value of room_types, this is done as follows:

“We have received the reservation for a {room_type} suite.”

Within a response any number of slots can be output with their current value.

slot_mappings

Before we define the last method of the class calledslot_mapping, we have to add another intent to the domain. This is the intent that the user executes to fill the form. For this we add the entities number, data, days and room_type and the intent inform to the domain.

intents:
- inform
entities:
- number
- date
- days
- room_type

Now we add sample contents with the respective entities under data/nlu.md. The entity room_type does not need this, because it is filled by a choice of two buttons. We recommend to provide at least 5 examples per entity for a correct training.

## intent:inform
- There are [4](number) of us
- We are looking for a room for [2](number)
- The [03/05/2020](date) is my date of arrival
- On [04/15/2023](date)
- [4](days) days
- I will be in the hotel for [1](days) day

We added the entities and slots to the domain and set up the intents within data/nlu.md for the training. Now the last method is the mapping from entities to slots, here a very simple version. The mapping is done with the method form_entity of the class FormAction. As parameters the entity and a list of possible intents is passed. The mapping is done within the dictionary, which is returned by the method.

def slot_mappings(self) ->
Dict[Text, Union[Dict, List[Dict[Text, Any]]]]:

return {
"number_of_persons":
[self.from_entity(entity="number",intent=["inform"])],
"date": [self.from_entity(entity="date", intent=["inform"])],
"nights": [self.from_entity(entity="days", intent=["inform"])],
"room_type":
[self.from_entity(entity="room_type", intent=["inform"])],
}

Executing the FAQ Chatbot

To execute the chatbot correctly, we now start the action server of Rasa. Actions created by the developer are executed within this server. Even more complex actions can be created within Rasa. For example, we can integrate external interfaces and use them within actions. Thus, an error within an action does not lead to the termination of the bot itself, since it is executed independently.

We start the server with the command rasa run action. With the parameter -p <port> it is also possible to specify an alternative port if it differs from the standard port 5055.

If you now start the chatbot with rasa shell, filling the form won’t work. The reason for this is that in the configuration fileendpoints.yml we have to link the two servers, the action server and the chatbot itself. Therefore we have to add the initially commented lines:

action_endpoint:
url: http://localhost:5055/webhook

Now we can use the chatbot after a training via rasa train using rasa shell. An example dialog for filling the dialog is shown below.

*Intent = Intention of the user. For example, if a user enters “show me yesterday’s technology news”, the user’s intention is to retrieve a list of technology headlines. Intentions are given a name, often a verb and a noun, such as “showNews”.

*Entities/Entity = In data modeling, an entity is a clearly identifiable object about which information is to be stored or processed.

*(Data-) Mapping = The process that maps data elements between different data models. Data mapping is needed as a first step for various information integration tasks.

Prospects

In this article we have built a chatbot for answering simple questions and making a reservation. This implementation does not yet include error handling. So far, the bot can’t react to unexpected questions. Also, the user can’t ask any questions during the reservation.

This chatbot doesn’t offer the possibility to fill slots through external interfaces or based on dependencies either. For all these problems Rasa can offer a solution to create the optimal chatbot for each situation.

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How to create a FAQ Chatbot with Rasa? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Fintech Chatbots: How AI Chatbots Play A Role In The Fintech Industry

In recent years we have witnessed tremendous development in the financial sector and banking industry. Financial Technology is also acknowledged as FinTech, a state-of-art program designed for the financial industry to provide next-level customer service to their users through chatbots. Chatbots in fintech are intended to assist customers with their requests in the most dynamic way and act as a guidance channel from which they can better understand the customer needs.

What is a chatbot & its significance in distinctive industries?

Chatbot is an artificial intelligence (AI) computer software program known as digital assistance that simulates online chat or via text-to-speech using different languages through a website, messaging apps, or a telephone. Chatbot is designed to understand human skills. Chatbot interprets the user request and provides a prompt solution.

Chatbot is a cost-efficient system built that promotes any industry’s operational performance by offering them convenient and effective services for their customers. Chatbots act as an instigator across various business industries.

With emerging technologies in fintech, AI is developed on deep learning to assist a massive number of customers at the same time without reducing the quality of services. Right from facilitating swift money transfers via mobile banking and net banking with the highest security to single scheduling tasks such as paying bills, clearing client’s invoices, buying bitcoins, etc., with the help of a chatbot.

Chatbots have become an intelligent solution for the significant financial and banking industry. They have eliminated the long queues at their branches, saving time and energy, giving customers the liberty to get the work done from anywhere without compromising the safety.

In recent years, Haptik introduced ready-to-use 100+ smart skills tools, a high-quality virtual assistant custom-crafted for various industries from retails, e-commerce, insurance, finances, telecom, travel, and hospitality, etc.

Haptik’s smart skills in the FSI industry developed chatbots that assists the customer’s in a diverse financial queries such as quick account balance check, mini statement of last five transactions, credit score, application status, loan assistance, loan payment calculator, upload documents virtually, assisting the user with account opening, and much more.

With the emerging technologies in fintech, chatbots are reshaping the digital marketing of the industries. Current studies show that 85% of the consumers prefer to get the chatbots’ solution instead of visiting sites and scrolling or posting the question on the search box.

Fintech companies need to handle various complex databases as well they need to store some confidential data of their customers, and it is humanly not possible to handle everything at once. Instead of getting more employees to handle the work and get them trained will add more time, chatbots make it easier by running everything flawlessly. The company doesn’t have to depend on their staff, and no matter what time it is, they can access the data on the system.

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1. Use-cases of Chatbots in Fintech Industry

According to a Business Insider report worldwide, 67% of customers are using chatbots for customer support. Customer service in fintech is considered the primary essential function to run the organization. Hence, chatbots help companies have high customer service expectations and automate their profit while saving customer service costs by hiring more individuals.

Let’s find out how the emerging technologies in fintech adapt AI chatbots strategy and benefits to the industry and customers.

a. 24/7 Support

The 24/7 service can be integrated with a hotline number, website, quick text message assistance, mobile applications, and social media channels to empower the customers’ service greatly.

This service is an essential piece of staying always connected, offering a high level of professionalism, and assisting customers through a complex process such as blocking debit/credit card in theft, account balance summary, generating pin, etc.

b. Financial Advice

An interesting paradox of the real-time digital fintech era is to get financial advice online. The intelligent AI-powered chatbots analyze a user’s spending behavior and the transaction history to predict their future actions and recommend them.

For instance, if the fintech chatbot identifies an excellent transaction history, it helps them invest money for the future. Likewise, if it detects bad spending habits, it assists with money management.

c. Digital Payments

Digital payments in chatbot fintech are peer-to-peer-payments and help the customers make various kinds of transferring money processes. The automation in fintech chatbot has linked the bank account or Paypal or Apple Pay or Stripe or with other digital apps such as Paytm, amazon payment, etc. of the customers and assist them with paying bills, money transfer, online shopping, etc.

d. Advertisement

Emerging technologies in fintech, the chatbots, help the industry grow with an online advertisement on robust social media platforms such as Facebook, Google, Twitter, youtube, and quora. With the millions of social media users, the AI-powered chatbots in fintech scan the users to target the audience.

e. Investments

Notably, a fintech chatbot can assist the users with personal financial advice and digital payment and assist in growing money in different ways. Digital fintech chatbots powered with AI help users create and manage online portfolios and assist in ingenious savings methods.

f. Insurance & Loans

Automation in the fintech industry is offering insurance and loans with ease and the comfort of home. Chatbots are designed to provide interactive insurance and loan product solutions to more people.

A customer needs to answer some simple and specific questions. A chatbot can then automatically respond to the queries, offering them customized and cost-effective services and assisting the customer in uploading the proper documentation to avail the services

Read more : How personal finance chatbots can help users redefine money management

How Chatbots Can Transform the Fintech Industry?

With the emerging technologies in fintech, AI chatbots are revolutionizing the customer experience. Customer service provided by any fintech industry is equally important as the services or products they sell.

As the fintech industries are now enabling wide-spread adoption of quick mobile wallets to their customers effectively, it introduces affordable options over the smartphone, proving to be a reliable financial instrument.

Let’s find out how the digital fintech provides customers with the opportunity to use the services seamlessly.

a. Removes The Friction Of Cluttered Interface

38% of people close the sites or apps within five seconds if they find the software’s complexity. AI integrated fintech chatbots help to smooth the friction and reduce the tech barriers. The inbuilt chatbots are designed to detect the human behavior and understand their needs.

b. Offers Proactive Suggestions

With the small pop up screen, a few numbers of the display can be seen at a time, and rather than the users keep searching for the services or product they need, the chatbot assists them by giving offers and proactive suggestions based on the answers the users provided for the simple questions. It is enhancing the personal experience.

c. Keeping Up With Millennials

Digital fintech chatbots are designed to keep up with millennials. With every recent technology advancement, the demand for instant assistance has grown tremendously, and millennials are steadily communicating via social media. Chatbot access and convenience quickly assist them and help to simplify millennial’s lives.

d. Automates Fraud Detection

Data privacy and security are the most important concern for any business because their prestige relies on them. Digital fintech chatbots are effectively monitored and issue a warning flag when they detect any scam activities and alter the bank and the customer.

e. Easy Marketing of Services

Automation in fintech chatbots helps in diversifying the audience traffic and promotes the company’s services and products. Assisting the consumer in buying the products directly in the chat. Chatbots promotes the brand image via diverse social media channels such as Facebook, Twitter, Instagram, YouTube, etc.

f. Enhances Customer Loyalty

Fintech chatbots are designed to provide users engagement more proactive. It also attracts more customers who wish to browse the services or products without having a verbal interaction. The information gathered via chatbots helps the companies understand the consumer requirements and provide them with the services and products they need. It automatically enhances customer loyalty towards the companies.

g. Reduces Cost

Fintech chatbots are a one-time expense that turned into an investment and cheaper than hiring more agents. It is not only a cost-effective system, but the ability of the chatbots to deliver the result in seconds saves time as well.

The users love the chatbot interaction because they can ask effortlessly what they need in their own language. Emerging technologies in fintech offer chatbots for one-stop solutions and personalize each user’s experience by lessening the wait time and navigating sites.

i. Increases Revenue

Fintech chatbots powered by AI technology helps in generating business revenue. Chatbots work like the way how e-mail marketing operates. The difference is the sales funnel developed in chatbots is interactive and stimulation the user’s conversation assisting them in choosing the right services and products they need.

From personalized shopping to a simplified buying process to secured payment getaways leads to a higher number of consumers.

Future of Fintech

The future of fintech brings the revolution in financial sectors by providing better and new services and products to the consumers. Fintech is forever changing the finance industry’s prospects. AI changes the way enterprises interact with their consumers.

The financial sector is one of the industries that is considerably operating with complex systems for decades and urges to react to keep up with the evolving technology to fulfill the demands of tech-savvy users.

The spread of automation in fintech with AI-powered chatbots is considered the hottest service to provide various users assistance in less time. More unprecedented access to consumer information through AI, cloud computing, and analytic continue growing and changing and targeting users with different behaviors and needs.

[Expert guide] — Factors to Consider While Implementing Conversational AI for Financial Services

Conclusion

Fintech is reshaping the way people think about money. The introduction of chatbots allows fintech companies to provide value-added services to their customers and change the command based role to a conversation-based part. The emerging technologies in fintech immediately solve the consumer’s queries and support.

Improved customer service experience leads to more customer satisfaction, which makes the company attract more profits from loyal customers. All data collected via fintech chatbots give the company insights on how to improve and provide better communication and financial results.

Want to develop an Intelligent Virtual Assistant solution for your brand?

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Fintech Chatbots: How AI Chatbots Play A Role In The Fintech Industry was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.