Banking isn’t what it used to be. Most of us need fast, efficient, flawless banking services that are hassle-free and, most importantly, reliable. It only makes sense to shift to digital banking channels that can provide these things. As it turns out, artificial intelligence (AI) and machine learning (ML) powered virtual assistants can do precisely that.
Are virtual assistants in banking the solution to outdated, cumbersome branches and lengthy wait times? More and more banks are turning to chatbots and virtual assistants for customer service.
Artificial intelligence is becoming more ingrained in day-to-day life. A recent Ipsos-Forbes Advisor survey found that an overwhelming majority of Americans — 76% — used their bank’s mobile app to conduct everyday banking tasks last year
This blog will look at how a virtual assistant can help you with your banking.
How Virtual Assistants Shape The Banking Industry?
1. Everyday Inquiries
A banking virtual assistant can help you with everyday inquiries. Since they are available 24/7, they can assist you when an issue arises after-hours or even on weekends.
With the right training data, bank virtual assistants can be taught to answer customer queries quickly and precisely, providing faster resolutions.
For example: For example, one bank’s virtual assistant service helps customers find out if their account needs attention by answering questions like “How much money did I spend last week?” Or, “What have my recent purchases been?”
Virtual assistants can make banking easier by handling:
Inquiries about account balances, deposits, withdrawals, and transfers;
Inquiry about account details (such as interest rates);
Inquiry about applying for loans;
Inquiry about transferring funds between accounts; and
Inquiry about bill payments.2.
2. Save Time on Typing
Typing is one of the most time-consuming parts of any job-especially when it comes to banking. A voice assistant can help you get everything out of the system quickly by typing all of your documents for you.
You can fill out forms or enter information without the use of text feature with a simple conversational AI command or tap-and-go gesture. That means less time spent on repetitive tasks.
For example, the Bank of America mobile app has Erica, a virtual assistant, as part of its digital banking experience. It helps customers save time with activated voice commands by automatically performing repetitive tasks such as entering account numbers or routing numbers into forms. The goal is for Erica to make customers’ lives easier by removing tedious and manual tasks from their day.
3. Make Simple Transactions
Banking VAs are great at processing simple transactions for you. You can easily ask them to transfer money between accounts or pay bills on autopilot.
VAs will send you reminders when you need to make a payment and handle all the details for you, so all you have to do is confirm the transaction and wait for it to complete.
Here are some examples of simple transactions:
4. Notifications about Unusual Activity
Your bank should monitor your accounts for unusual activity to alert you if anything looks suspicious or out of place.
However, these alerts can often be buried deep within lengthy emails or notifications that don’t always reach their intended recipients in time to take action before any damage is done.
Banking virtual assistants can help you keep track of your accounts and detect suspicious activity before it becomes serious enough for you to notice on your own.
For example, if someone tries to make an unauthorized withdrawal from your account, you’ll want to know immediately to take action before any damage is done. Capital One’s virtual assistant, Eno, can keep track of all of this activity for you and let you know if anything seems out of place or suspicious.
5. Get Advanced Insights
Banking VAs can help you find the best bank account by providing advanced insights into your banking and financial situation.
You’ll get an idea of how much money you spend on certain types of transactions and what rewards program would work best for your lifestyle.
Here are some examples of advanced insights that Bank of America’s VA, Erica, can provide:
Track your monthly income and expenses
Changes in your FICO score
How much money do you spend on groceries, dining out, or other common monthly purchases?
Recurring spending trends
Your account balance trends
Conclusion
As a result of technological growth, financial institutions seem capable of striking the right balance between making banking more convenient and increasing customer loyalty by providing virtual assistants.
As is often the case with new technology, this is still in its early stages. If you research and vet your options properly, you can get an effective assistant to improve your banking experience.
Author Bio
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of .com, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
When a Conversational AI solution is implemented, one early decision that needs to be made is the level of personalization that is required to create an exceptional customer experience. Our experience shows that the bots that are able to do more and are able to handle the more complex scenarios have access to more detailed information about the user. The more effective the experience, the higher the satisfaction of the user, and the more likely that user is to re-engage the Conversational AI solution again when they want to perform an action.
Customer Experience: To Personalize or Not Personalize
Depending on the use cases, there are many reasons to go down the personalization path for your Conversational AI solution, but also some use cases where you don’t need it for your experience to start, or ever.
Organizations who are beginning to experiment and understand the value of Conversational AI may choose to not implement a personalized customer experience right away, and that’s perfectly understandable. You need information in order to understand how your users are engaging with you. Approaching your Conversational AI investment in a traditional product model of starting with a Minimum Viable Product (MVP) and starting to capture feedback will help to understand the priority for other conversational flows that can be implemented.
Personalized Chatbot Flow for Online Banking Services
For businesses that are focused on responding to FAQs, implementing personalization within the customer experience may not be a priority, and again, that’s OK. For many, the ability to provide clear information to all users with the same messaging may not need the additional activities to support a personalized customer experience. This may mitigate some of the noise to your live agent center, chat or voice, by answering some of the more common questions that don’t require the solution to know who you are — what are your hours of operation, what is your return policy, etc. Information that is fairly static and will be the same, regardless of who the user is.
As soon as you want to provide the ability for users to self-service, you need to consider personalization as part of the customer engagement. The ability to understand who the user is, what their interests are, and what they have done in the past means that your chatbot or voice bot can be a true conversational AI assistant to the user and help them perform some action, rather than just answer some questions. Thinking of these use cases and implementing them should be done as part of outlining the user journey, so that the right level of personalization is planned. The value of the Conversational AI bot goes up immensely, and can significantly reduce the impact of effort for your live agent team, but even more importantly it can now provide a 24/7 service to your users that may not have been possible before.
Essentially, a non-personalized Conversational AI solution will let users learn and answer questions, but the ability to do anything meaningful is limited. Creating the ability to personalize the solution allows the user to perform activities on their own, at their convenience, whenever they want.
When we discuss personalization around the customer experience, we have a strong focus on the data. The data drives the information, and then a strong conversation design makes the information valuable in the context of the chat. With the design and user journey planning, you understand what information is needed in order to bring value. But now you need to get that information from somewhere.
Integration to the data stores where the personalized information resides is vitally important if you want to create a successful Conversational AI customer engagement. The ability to extract the right information and use it in the right way means that you’ll create a collection of services for your users that allow them to do something meaningful, such as:
Renewing an insurance policy will require looking up your current coverage details and then understanding the comparable rates for the next term;
Purchasing a new phone and using previous purchases and experiences to showcase the most likely device for you; and,
Paying a bill online through a conversational solution.
These are just a few examples, but they are meaningful ones that most people can resonate with. Anything that allows the user to self-service without the need to engage someone, and do it at a time and place of their own convenience, brings immense value to the customer experience.
In many cases, this data may be borne out of multiple systems and so multiple integrations of Conversational AI are required. It’s rare you will have the customer information, generally driven out of a CRM system of some making, and the product information residing within the same system, but not impossible. But you need to understand where the source of the data you need for these flows originates from, and you need to ensure that you have access to that information in order to serve up the data in an effective manner.
Customer Information Sources for Message Personalization
As we talk about personalization in the area of customer experience, there is one key piece that needs to be in place, and is another integration of sorts.
The Need for Authentication
When we do personalization, we need to know who we are personalizing the information for. You may want someone to have the ability to pay off your bills, for example as part of Use Case of Conversational AI for Finance, but you don’t want them having access to your transaction history or your balances. And as such we need to make sure the user is who they say they are.
Authentication is vitally important to ensure that we are delivering the right information at the right time and to the right person. If you’re historically an Android phone user, we probably shouldn’t be promoting iPhone services to you as you are less likely to purchase one of those. But if we are talking finances, we need to make sure that the user is the right person, either through direct or inferred user validation techniques.
Authentication provides a level of personalization, but also the feeling of security when engaging with the bot. As a best practice, the Conversational AI bot only accesses the information it needs for the task at hand, and nothing more. It’s coordinated through official services that the business offers, and any transactions (such as transferring funds) are requested by the bot but performed through existing services, ensuring that the proper checks and balances are in place to monitor and log the transaction, and ensuring that the request originated from an authorized user.
Final Thoughts
Personalization in the area of customer experience requires some work to implement, but it doesn’t have to be a lot of work. You need to understand what information you require, where that data resides, and then determine how you can access it and what transactions (if any) are available for use. But creating a way for the user to actually do something and stay in the context gives them more freedom — when they want to do it, and how they want to do it. This customized customer experience leads to more customer engagement and service satisfaction, which can lead to users wanting to do more, and businesses then discovering additional use cases for business process automation, which can help with cost management of call centers and live agents, who can then be reserved for those complex and custom scenarios that need the human touch.
Want to learn more about how your Conversational AI can be enhanced with personalization?
Artificial Intelligence (AI) is the theory and creation of computer systems that can carry out tasks that would typically need human intellect, like speech recognition, language translation, and visual perception.
Bot Libre, through its open source platform develops chatbot and artificial intelligence solutions that are ready to use in the metaverse. These solutions are central to the success of businesses and even the world, as they drive efficiency, enhance user experience, provide detailed analysis and solve problems.
While the overarching goal of AI is to solve problems and make life easier, the way this is accomplished is distinct depending on whether the AI is strictly automated or is autonomous.
Automated AI
Automated systems often operate within a clearly defined set of constraints and have a relatively limited range of tasks that they can complete. Automated systems use predetermined heuristics to inform their judgments and actions. For example autopilot, cruise control and yes, Bot Libre chatbots, are all results of clever automation. When you deploy a Bot Libre chatbot to web, mobile app or social media, you can program it to respond to any of your customer’s questions, from a variety of topics and using diverse personalities.
Autonomous AI
On the other hand, autonomous systems can learn from dynamic situations, adapt to them, and evolve as their surroundings change without the need for human involvement. The data it picks up and adapts to might not be what the system’s designers had in mind when they implemented it. These systems will process and learn from growing data sets more quickly and eventually more consistently. The goal for developers is to make technology as intelligent as possible. Building autonomous systems will be part of that process.
Bot Libre’s Hybrid Model
Bot Libre chatbots learn on new data as they are trained with new responses, and some of our features and scripts use learned data to change behavior, even dynamically at runtime. Bot Libre also has 2 parts, the chatbot engine, and the deep learning engine. The two can be combined for a hybrid solution.
Our Metaverse support is also a hybrid. For our navigation functions we have several modes, some are automated, some are more autonomous. The automated ones use compiled scenes and are faster and more accurate. The autonomous ones are more generic and work in any scene or augmented reality.
It is important to know that automated and autonomous AI are on a spectrum, as systems that were automated at first with a clearly defined set of inputs and outputs, may need to evolve over time as their usage and the environment in which they function do. As a result, one may add certain autonomous capabilities to an automated system, increasing both the system’s lifespan and its range of usage.
Benefits
Automated AI
Increased speed, efficiency, time-savings
Ability to scale using AI the insights, flexibility, and processing power
Able to augment businesses’ capabilities, while off-loading repetitive tasks to the machine
Autonomous AI
Avoid risks and errors
Instant interconnectivity by speeding up communications and the passing of information
Increased productivity through clearly detecting areas of opportunity for each business, and anticipating future events.
So which do you choose?
Experts share that it all depends on the problems you are trying to solve. How do you want to engage with your clients? Automated AI is a thriving market and with Bot Libre open source platform you can build automated chatbots, with little to no programming.
Meanwhile, Autonomous AI serves as a link between the state of technology today and where it will be in the future. If your company is considering implementing autonomous AI, Forbes Technology Council contends that you should “begin by hiring skilled resources with strong expertise in artificial intelligence, machine learning and neural networks, a branch of cognitive computing. These experts can develop systems for much more proactive IT monitoring, problem detection and resolution.”
You can get access to a team of AI , chatbot and metaverse experts by joining the Bot libre Beta Program, where there is a community of software experts at your disposal. Collaborate and legally own what you create. To apply: send an email to sales@botlibre.com
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IBM Watson is a powerful platform where software developers and non-technical users can co-create AI solutions. Its easy-to-use interface allows you to quickly create an AI model, from simple chatbots to complex enterprise solutions.
In this article, we will explore how to create, train and integrate your first Cognitive Virtual Assistant.
Step 1: Information Architecture
The Information Architecture defines a collection of all the sections to be included in your assistant, placed in a hierarchical order.
The following example shows all the sections for providing useful information about a city regarding shops, landmarks, museums, etc.
Step 2: Creating the Assistant
An assistant is a cognitive bot to which you add skills enabling it to interact with end users. To create a new assistant, you need to log into the IBM Cloud platform first — https://cloud.ibm.com.
Once logged in, go to the catalogand search for “Watson Assistant”.
2. Set up the assistant with the following attributes:
Region: “Dallas”.
Pricing Plan: “Lite”.
Service Name: This is the name of the Watson Assistant service being created.
Resource group: Only those who have access to this Resource Group will have access to the Watson Assistant service. For the moment, leave it as “Default”.
Accept the terms and conditions and click on “Create”.
3. Once the assistant is created, click on “Launch Watson Assistant” to access the Modeling Tool.
4. Click on “Create assistant” and name it.
Click on “Create assistant”.
Step 3: Creating a Dialog Skill
A dialog skill uses Watson NLP — Natural Language Processing — and machine learning technologies to understand user questions and requests, and respond accordingly.
1. Click on “Add dialog skill.”
2. Select “Create skill” and set up the skill with the following attributes:
Name: In case of having more than one skill, enter meaningful names in order to establish a clear differentiation between them.
Language: Set the language that the assistant is going to support.
Click on “Create skill”.
Step 4: Adding Intents and Entities
In this step, you will add training data built by IBM and your own training data to the skill.
Content from Content Catalog
First, add the General content catalog so the assistant can greet users and end conversations.
Open the Dialog by clicking on it.
2. Click on “Content Catalog” from the side menu. Go to General and select “Add content +”.
Intents
An intent is a collection of user statements based on intentions and examples of what the user might say.
1. Select “Intents” from the side menu and click on “Create intent”.
2. Go back to the Information Architecture from Step 1 and create an intent for each section.
Name the intent and click on “Create intent”.
3. Once your intent is created, add at least five examples of what the user might ask.
Keep adding new intents and examples until you have them all created, and don’t forget to include the #menuintent.
Entities
Entities represent information from the user input that is relevant to the user’s goal.
In this example, we are not creating entities, but using system entities instead. A system entity is a synonym that is prebuilt for you by IBM. They cover commonly used categories, such as numbers, dates, and times.
Select “Entities” from the side menu and click on “System Entities”. Enable @sys-number entities as shown in the example below.
Step 5: Dialog Flow
A dialog flow defines all the possible ramifications of the conversation in the form of a logic tree. The dialog matches intents (what users say) to responses (what the assistant replies).
The “Welcome” and “Anything else” nodes
The following two dialog nodes are created automatically:
Welcome: It contains a greeting that is displayed to your users when they first engage with the assistant.
Anything else: It contains phrases that are used to reply to users when their input is not recognized.
1. To edit these nodes, select “Dialog” from the side menu.
2. Click on the “Welcome” node, and add your greeting message in the response section.
3. Click on the “Anything else” node, and add your response variations for those questions that the bot cannot answer.
Adding Nodes
Let’s add a new node containing the menu.
Click on “Add node”.
2. Name the node as “Menu”. In the “If assistant recognizes” section, add the #menu intent. Finally, add the text for the menu in the response section based on your Information Architecture.
3. Now, let’s try out what we have built so far.
Click on “Try it” to open the pane. You should see your welcoming message.
Ask your Virtual Assistant for the menu as shown in the animation below.
Great! The assistant recognized the intent and replied with the menu.
Child Nodes
Let’s add a child node inside the Menu based on the Information Architecture’s hierarchy.
Select the Menu node and click on “Add child node”.
2. Name the node. Add the intent in the “recognize” section and, using the logical operator “or”, add the entity representing the same intent (in this example, we set @sys-number:1). Then, add the text in the response section.
3. Add a new child node inside the child node.
4. Name the node. Add the intent and the entity representing the same intent with the logical operator “or”. Then, add the text in the response section.
5. Let’s try it out.
First, click on “Clear” to reset the conversation. Then, ask the assistant for the menu and navigate using option numbers as shown in the animation below.
Awesome! The assistant recognized the entities, and now we can go through the different menus.
Jumping between Nodes
An important aspect about cognitive bots is their ability to recognize user requests and interpret the natural language used by us (humans). Thus, users should be able to directly ask what they want, and the dialog should “jump” to the node containing the response.
Go to the menu node and click on “Customize”.
2. Turn on the option “Multiple conditioned responses”.
Click on “Apply”
3. To respond with the menu, use the #menu intent.
4. Click on “Add response +” and add the intent to jump to the corresponding node.
5. Click on the gear icon between the response and the delete icon. Go to the end of the customization pane, and in the “Then assistant should” section, select “Jump to”.
6. Select the node you want to jump to, and select “Respond”.
Click on “Save”.
7. Finally, in the menu node, add the intent with the logical operator “or” in the “recognize” section.
8. Let’s try it out.
Wow! The assistant recognized the question and was able to respond accordingly.
Step 6: Training
The more you use your assistant and the more examples you teach it, the better its AI model will get at answering those questions it does not know yet.
You can use the “Try it out” pane not only for testing purposes, but also as a training tool. Simply ask those questions that the user might ask that were not added in the intent, and change the “Irrelevant” tag to the desired intent as shown below.
Step 7: Integration
For the integration, IBM provides a REST API. The Assistant v2 API provides methods your client application can use to send user input to the assistant and receive a response.
Go to the Assistants section, select your assistant and go to Settings.
2. Here, you will see the API details to use the REST API.
3. Access the API documentation here and replace {apikey}, {url} and {assistant_id} with the information from the API details.
Artificial intelligence is slowly permeating into every industry as technology moves forward. However, as it becomes more popular, companies are starting to get concerned about the future. Innovation — and productivity — aren’t what they used to be in today’s world. You see, your employees will no longer be able to focus on their single task, because computers do this for you all day long. You might already know about A.I., or other forms of machine learning, but now is a good time to sit back and think about what innovations could change your business in the near future.
AI Generated
A.I Everywhere
Artificial intelligence (A.I.) is a broad area of computer science that deals with the creation of computer systems able to perform tasks, activities and decisions that normally require human intelligence. These tasks are usually carried out through the use of data and logic, but they can also be carried out by a human interacting with a machine. In this article, we’ll look at the top 10 reasons why every company needs A.I.
AI is no longer just about voice assistants or chatbots. AI has become an essential part of our lives, from self-driving cars to smart homes, and it’s only going to get more pervasive in the future. Here are 9 reasons why every company needs artificial intelligence:
Quick and efficient decision making for companies
A.I will augment human intelligence
A.I Improved productivity
Enhanced customer experience
Addresses the issue of fake news and information
Enhance service delivery and improve network performance
Improved Security
Machine learning used in fraud detection and risk management in financial institutions
Bots can improve customer support services in companies by enabling users interact with machines through voice, phone, etc.
Quick and efficient decision making
A.I. will help businesses make decisions faster and more accurately. The technology can be used in industries like manufacturing, logistics and financial services where data is important to the business. The A.I.-powered robots at Amazon’s warehouses are able to scan packages quickly, saving human workers from having to do so manually.
A.I will augment human intelligence
The ability to make decisions, learn from data and improve based on feedback is one of the biggest promises of AI. The more data companies have about their customers, the better they can target them with relevant offers. Artificial intelligence will also help companies predict customer behavior and deliver more personalized marketing campaigns.
A.I improves productivity.
Artificial intelligence is a computer program that can perform tasks that a human would be able to do. It uses algorithms and data analysis to make decisions, which can improve your company’s productivity.
AI can help companies with complex decision-making processes by automating some of these processes. For example, an AI-powered chatbot could be used to answer customer service questions or provide information on products or services.
Enhanced customer experience
Artificial intelligence can enhance customer experience. A.I. can help companies improve the way they communicate with customers, simplify the experience and make it intuitive.
For example, it’s not uncommon for a company to send out an email newsletter that contains a link to a product page on the website. Customers who click on the link can expect to be taken to a new page where they can purchase the product or receive more information about it if they select that option. But what if A.I. could automatically find, categorize and organize all of this information?
This could eliminate much of the work that is currently done by marketers, making their jobs simpler and more efficient. And it would eliminate any confusion for customers who may not understand why they’re seeing certain things or being directed to certain pages in relation to one another (or even why they’re receiving an email at all).
Fake news and information
Fake news and information have been part of politics in different parts of the world for a long time, but recently they have become more prevalent in the United States and other parts of Europe and Asia as well. One way A.I. would help address this problem is by allowing companies to identify fake news stories or information on their own websites before they publish it to their readership base or customers.
Companies will also be able to identify fake followers on social media accounts so that they can verify that these accounts are fake and not actually real people who are following them on social media sites like Facebook or Twitter.
Enhance service delivery
One of the major benefits that AI can provide to the network is its ability to analyze traffic patterns.
It can also be used in network management to monitor and optimize network performance, as well as enhance service delivery by improving the quality of customer experience.
Improved Security
The threat of cyber security is an ever-present concern for all companies. The most common type of attack is a DDoS attack, which can be prevented by using AI to block the attacks before they happen.
AI has been used to create algorithms that detect malware and other types of cyber attacks. These types of attacks are becoming more and more sophisticated, so AI will be necessary to combat them.
The use of AI in cyber security has been increasing over the years. The use of AI in this field has been increasing rapidly because it can help detect fraudulent transactions, track down cyber criminals, and more.
Risk management in financial institutions
Machine learning is used in fraud detection and risk management in financial institutions. It helps to detect patterns and anomalies that can be used to identify potential frauds. Machine learning tools are also used for risk assessment and credit scoring, which helps the banks know their customers better. This is because they have a large volume of data that can be analyzed by machine learning algorithms.
Improved Customer Support Services
Artificial intelligence (AI) bots are the next evolution in customer service. They can improve the efficiency of customer support services by enabling users interact with machines through voice, phone, etc.
The bots can be used for virtual assistants like Siri, Alexa and Cortana; chatbots that can be used for self-service tasks; and bots that can provide information to customers about products or services.
For example, if you’re looking to buy a new car but don’t have enough time to research it online, you could use a bot to help you narrow down your options. You could then schedule a meeting with the dealer who’s got what you need — say, a hybrid SUV — and ask questions about its features and pricing options.
Takeaway
Artificial intelligence (AI) is among the most promising technologies of our time. It’s capable of disrupting market landscapes and transforming business models around the world. AI is increasingly prominent across sectors (ranging from automobiles and healthcare to finance and retail) and its potential to bring value to businesses is significant. It’s not hard to see why AI is stirring up so much excitement. Enterprises everywhere are beginning to consider how AI can help them become more innovative, operate more efficiently, and scale their operations.
Chatbots are software tools created to interact with humans through chat. The first chatbots were able to create simple conversations based on a complex system of rules. Using Flask Python Framework and the Kompose Bot, you will be able to build intelligent chatbots.
In this post, we will learn how to add a Kompose chatbot to the Python framework Flask.
You will need a Kommunicate account for deploying the python chatbot.
Kommunicate is a no-code, hybrid chatbot platform that is built over a powerful Conversational AI system.
Also, you will need Python and the Flask framework installed on your system. To read more info about the Flask framework, please follow this link.
We will be using Flask in this tutorial. If you are looking to add Dialogflow chatbot to the Django framework, you can see this tutorial.
Steps to Create a Chatbot using Kompose and Flask Python Framework:
Step1: Login to your Kommunicate dashboard. If you don’t already have a Kommunicate account, you can create one here.Navigate to the Kompose bot builder category and create a new bot by selecting the “Create Bot” button.
Step 2: Navigate to the Kompose Bot Builder, select your bot and click on the “Settings” option present at the top right corner.
Click on the Webhook option present on that page. Here, we need to put the Webhook Name and Webhook URL.
Step3: Create Webhook URL using Python with Flask and enable the webhook server using ngrok
The webhook requires a URL, and it should be an HTTPS protocol. The webhook URL will receive a POST request from the Kompose Bot every time an intent triggers the webhook.
We are using the Python programming language and the Flask framework to create the webhook.
Create a file (for example — app.py). Import all the necessary libraries (ex: os, JSON, flask_ngrok, request) needed for Python. Please check if you have Flask on your system. If not, install it using pip, and here’s the documentation for the same.
To handle all the agent webhook requests, we need to define and add a route/webhook method with a POST request. This URL /webhook will receive a POST request. It executes all the methods inside the method.
After setting up the Python process, let’s use flask ngrok to create a public URL for the webhook and listen to port 5000 (in this example). For Kompose webhook, you will need an HTTPS secured server since the local server (localhost) will not work. You can also use a server and point a domain with HTTPS to that server.
Copy the URL you created (https://85e6-203-189-248-8.ngrok.io/webhook) — in this example and paste it into the Kompose webhook URL field. Here, I have written the Webhook name as “Test.”
Step 5: Create an intent by clicking on the +Add button under the “Answer” section and “Train the Bot.”
Here, I have created Flask intent and added a training phrase as “What is Flask?”
Step 6: Click on the “Bot Says” option and select the webhook that you created earlier. Here, I have selected the Test webhook created earlier. Now, click on “Train Bot.”
Step 7: Testing
Once the setup is done, you can easily add to your website or apps using Kommunicate.
& test if the Python chatbot is working.
There you have it, a Python chatbot for your website created using the Flask framework. If you want to create your own chatbot check out our How to build a chatbot guide.
If you are in thought that chatbots and virtual assistants are the same things, then your understanding of AI applications and their uses requires an upgrade. Both of these technologies have a key difference in the way they add value for customers and employees as well.
AI technologies have evolved at an astounding pace and promise to offer incredible value to both customers and employees. This being the reason, tech-savvy organizations are looking forward to adopting these trending technologies for boosting their business revenue and employee productivity. Also, multiple organizations are extending their efforts by identifying more use cases and deriving more value from AI. If we look closely, chatbots and virtual assistants are ripened fruits of AI that often set confusion due to both being the conversational interface. Hence, it’s crucial to understand their differences to get more value from both of them in the business process.
Let’s drill down deeper to learn about what chatbots and virtual assistants are and what makes them different.
What are chatbots?
Chatbots are automated programs that are designed with the purpose of engaging with customers in human-like conversations. Also, these chatbots can be deployed by businesses to interact with customers and offer assistance around the clock.
Let’s understand the role of a chatbot with an example. Deploying a chatbot in banking operations can help staff agents to get rid of repetitive activities. And chatbot can help in-
Checking account statements 24/7
Get account settlement and transaction options
Block/unblock credit, and debit card
Gain user data insights for better product promotion
Provide easy KYC initiation
However, if there is any query that is beyond chatbot capabilities, it keeps humans in the loop for further assistance and query resolution.
What is Virtual Assistant?
A virtual assistant is a digital software-based agent that helps businesses and individuals in assisting performing daily activities like scheduling appointments, making calls, initiating messages and etc through text and voice commands. To sum up, simply, a virtual assistant works like a personal assistant who reminds us to do our “to-do list”, or read an email and any other important details to us. Moreover, to make virtual assistants smarter, enterprises can leverage AI and cognitive technology for performing more corrective actions. By handling user intent like a professional, an intelligent virtual assistant enriches communication with a human without any manual intervention.
What makes Chatbot and Virtual Assistant Different?
Chatbots and virtual assistants both can be leveraged through different channels. Chatbots, on the one hand, can be leveraged through website channels, mobile applications, messaging channels, and in-app chain widgets, and intelligent virtual assistants can be used through mobile applications, laptops and interactive devices.
Benefits of Chatbot and Virtual Assistant
Chatbots and intelligent virtual assistants both come with a set of benefits and value-added propositions that make user and employee experience smooth, better and faster.
By utilizing chatbots on websites, enterprises can gain deep insights from customers for improving their market strategy and create better plans. Also, chatbots can be leveraged for manual and repetitive queries that take high manual effort. But, with chatbot implementation manual efforts get reduced that eventually leading to high customer experience, low operational cost and high employee productivity.
Virtual Assistants, on the other hand, simplify the process of handling routine and repetitive processes that take a lot of human time. Utilizing a virtual assistant helps users set reminders, and alerts, add tasks to the calendar and directly fetch information from the web or any other sources.
Conclusion
Both chatbots and virtual assistants are two strong pillars of AI technologies that can address multiple enterprise requirements and enable them to create a significant customer experience. Moreover, these chatbots and virtual assistants can be leveraged through multichannel support for easy data access and sharing. It’s high time for technology leaders to shift their focus on these AI interfaces for giving their businesses a competitive edge.
Bridging the gap between your organization and beneficiaries is no small feat. Chatbots are gradually becoming the most user-friendly and cost-effective method to approach this issue. It’s especially true during the pandemic era when some NGOs shifted from on-ground activities to reaching their beneficiaries through chatbots.
Even so, there is still a lot of untapped potential where NGOs just like you, big or small, can leverage the power of chatbots to reach your target beneficiaries effectively. Let’s talk about this further.
Why does your NGO need a Chatbot?
There are multiple use cases for how you can extend your NGO’s impactful initiatives to your target audience through the use of chatbots.
Chatbots can be used to
Create awareness about your organization or product: – Relay information to the beneficiaries.
Take feedback: – Q&A – FAQs
Understand user response to your program or content.
Monitor engagement and reach.
Collect data.
The possibilities are endless.
Process for Designing the Conversations
We have an exciting journey ahead. Let’s start!
Step 1: Think about the Chatbot’s Goal and Users’ Challenges:
You will first need to determine the end goal for the chatbot: what is it that you want to achieve through the chatbot. Reflect on the pain points of your target users– what are the problems they face that your NGO can solve through the chatbot. Document the goal and pain points.
For instance, the goal can be to regularly update the users about events that your NGO is conducting for the beneficiaries and create awareness about your programs.
Pain points of the users can be
They are not receiving information that is specific to their interests.
The information about your NGOs programs is not easily accessible.
Chalk out a plan that consists of the upcoming courses of action which we are going to discuss in detail further. It’ll be helpful to deliberate on their timelines as well.
Summary of the Action Steps:
Step 2: Examine your Current Bot if any:
Feel free to skip to Step 3 if you don’t have a pre-existing chatbot. But if you do have one, read on.
NOTE: In both cases, we suggest that it will be helpful for you to look through the definitions of what, why, how, where and when mentioned below.
If you wish to enhance your current chatbot’s user experience, you should begin by conducting an audit of the same. Go through the existing flows and messages. Some of the aspects that you need to check while conducting this audit are:
Length of messages and flows: Are they short and to the point?
Do the messages add value to the users?
Are there any links broken between the flows?
Do the messages sound engaging to the users?
If there are any external links and media added, do they add value to the users?
Are the external links working?
Following this process, generate reports and analyze the scope of improvement. You will need to document and define the 5 main questions: what, why, how, where and when.
“What”: What are the aspects that you want to refine in your chatbot? Is there anything you would like to add/remove?
For example,
Content length
Tone of messages
Use of rich media and external links
What kind of data is being collected
If the content is relevant and personalized to the users’ challenges
“Why”: Why do you think the aforementioned aspects are an issue?
For example,
If the messages are lengthy, users can get lost many times in a single conversation. You need to remind them where they are and what they are doing by constantly checking if they’re following the messages.
If the messages are not personalized to the users’ needs and challenges, they are likely to ignore the messages along with the ones that might be relevant to them.
The users might not be engaged if the flows are all content and no play, such as rich media.
“How”: How are you planning to make these changes?
For example,
People generally appreciate short, easy-to-grasp messages that quickly get to the point and answer their questions.
Get your creative juices flowing. Make the content more engaging and informative. Girl Effect’s Indian chatbot, Bol Behen, is a great example of creating a quirky and friendly message tone. Similarly, you can take inspiration from Mukkamaar’s bot as well.
Keep trying different images and media so that you find the one that sticks.
“Where”: You need to identify the moments in the existing flows(users’ chatbot journey) where the edits will be made.
“When”: Define your project’s timelines.
Summary of the Action Steps:
Go through the existing flows and messages and check what can be improved.
Document this process by generating reports and defining the What, Why, How, When and Where.
Step 3: Step into the User’s Shoes:
Take a deep dive into the user’s mind and try to think of what could be the intent of the user and the barriers faced by them.
User intent is what the user wants to get done or the outcome they’re seeking when they engage with a chatbot.
The most effective method to do this is to directly speak to the target audience. This will help you to get closer to the primary problem and understand it better. It will lead you to think of the most optimal solution to provide value to the users through the chatbot.
Here’s a template that you can use to identify Intents and Barriers:
Summary of the Action Steps:
Define the intents of the users and the barriers faced by them.
Step 4: Map the User Journey:
Think about the ideal conversation your chatbot needs to have with the users to solve their respective challenges. It can range anywhere from 2 to 20 messages, that’s up to you to decide and depends on user engagement.
Also, think about the actions they’ll take while accessing the chatbot. These actions could be quite minimal such as viewing the attached media, checking out external links attached, calling for support, etc… Mostly, they will be just responses to the messages. You need to keep them as easy and minimal as possible.
Create a rough draft of the user’s journey and iterate on what will work best.
Sample User Journey Flow
Summary of the Action Steps:
Ideate an ideal conversation that the chatbot will have with the users.
Plan what actions the users will take while accessing the chatbot.
Time to Set Up the Chatbot!
Step 5: Bot Persona:
While setting up the chatbot, giving the bot a backstory and personality will help it find its voice and make it unique. Come up with aspects like motive, experience and age of the bot. A persona is a major factor in building an engaging chatbot.
If your existing chatbot already has a personality, you can go forward with creating the conversation flows in the same voice and tone or refining it to be more human-like and engrossing.
While talking about Bot Personas, two NGOs come to mind- Girl Effect and Mukkamaar. Their chatbots are excellent examples of constructing exciting Bot Personas. They resonate with their target audience and keep them engaged.
Snapshots from WhatsApp based chatbots of Girl Effect’s Bol Behen and MukkaMaar’s POWER with Mukki
Summary of the Action Steps:
Give the bot a backstory, motive and personality by ideating on aspects such as a person with age and experience in life.
Step 6: Create Conversational Flows:
Let’s begin with creating a message concept. A message concept is fundamentally a summary and basic idea of each of the conversation flows that you’re going to write.
It can look something like this:
Go through the message concepts back and forth and decide what will work best to engage the users throughout their usage of the bot.
For the messages to sound effective and engaging, connecting to the user on an emotional level is the key. The messages should be preferably short and crisp and add value to the users. The messages can sometimes be detailed enough to build confidence in the user. For a good user experience, you can also:
Sprinkle some emojis
Add fun and valuable quizzes for the participants
Give points for each task they complete
Add pop culture or any references as per the audience
Add GIFs or short videos
…the creative opportunities are limitless.
Summary of the Action Steps:
Create conversation concepts of the messages.
Write the final messages as short, crisp and those that add value to the users’ challenges.
Step 7: Decide the Development Platform of the Chatbot:
There are a ton of options available to develop the chatbot on. Many organizations use RapidPro but it may not be viable for all NGOs. Another prominent platform is Glific. It’s used by 40+ NGOs globally and in India and reaches lakhs of beneficiaries. The option of manual and automated engagement with the beneficiaries makes it highly convenient and cost-effective. This allows NGOs to scale their operations to wide geographies.
ColoredCow provides set-up support for Glific and RapidPro as well. Let’s talk more about it later.
You will also need to decide whether your chatbot needs to be based on WhatsApp, Telegram, Facebook Messenger, Signal, or any other social media platform.
Summary of the Action Steps:
Decide whether you want to base your chatbot on WhatsApp, Telegram, Facebook Messenger, Signal, or any other social media platform.
Think of which development platform you should use. Some of the options are Glific and RapidPro.
Step 8: Set up Flows:
Once the conversation flows have been reviewed and finalized and the development platform has been set up, you can start uploading the flows on the platform you have chosen or whichever platform supports your processes. You can make the messages interactive by creating buttons that the user clicks on and the flow takes its course according to the choices of the user.
Image courtesy: Glific
Summary of the Action Steps:
Upload the conversation flows on the platform that you have chosen or whatever process the platform supports.
The Testing Phase
Step 9: Write Testing Script:
Testing Script is a line by line description of all the actions that are necessary to perform and test on specific user journeys.
It starts with a series of instructions for the internal testing team that can be as simple as “Test the flows on your mobile phone”. They are asked specific questions about their experiences while using the chatbot. In the end, they document their feedback about the same.
It can look like this:
Image courtesy: Girl Effect. Based on our experience working with them.
Summary of the Action Steps:
Write a series of instructions for the internal testing team.
Then ask them questions about their experience of using the chatbot.
Step 10: Define Success Parameters:
Success parameters are clear and concise metrics that will help you decide what the bot’s success will look like. For this, you need to formulate a series of report metrics and set up a dashboard. These parameters can be unique to your programs such as how many new users you are onboarding daily, how many users started vs. completed a quiz or learning flow, retention rate etc…
Summary of the Action Steps:
Formulate a series of report metrics.
Set up a dashboard.
Step 11: Internal Testing Phase:
There needs to be internal testing of all the flows before launching it to the users. The team can test the bot and mention their feedback according to the testing script. You can parallelly make the changes in case of any technical glitches or changes in content to be fully prepared for the next phase.
Summary of the Action Steps:
While the internal team is testing the bot, you can make the suggested changes parallelly.
Step 12: User Testing Phase:
A sample of users is recruited from the target audience to conduct User Testing. Now you need to work on an outline of the process through which you will conduct User Testing. Write some questions that will help you build a rapport with the respondents.
The questions can be
Introductions between you or the UX tester and the respondent.
Respondent’s experience before interacting with the chatbot and the solution.
Specific questions to understand the user’s intent of engaging with the chatbot.
Why do the respondents take certain actions to reach their expected outcome?
What do they feel about the chatbot?
Then it’s time to put together a series of UX Tasks that are scenarios that will help you gauge the experience of the respondents while they access the flows.
Summary of the Action Steps:
Recruit a sample of the target audience.
Work on a list of questions to ask them.
Put together a series of UX Tasks to gauge the respondent’s experience while they access the flows.
Conduct the aforementioned activities with the respondents and document your observations.
Step 13: Update the Flows:
Based on the findings from User Testing, you can review the flows and make changes to the messages.
The Last Stage
Get…Set…Ready to Launch!🚀
We’ve come a long way! Now you’ve done most of the hard work, it’s time to create some exciting marketing campaigns and launch your chatbot to the world. Soak in the feedback and keep iterating the bot by learning from the audience.
It might take a lot of iterations to get the message across just right. But as long as the user’s needs are in focus, the key to your success is if your efforts lead to any positive influence in the lives of the users.
ColoredCow is a software development company based in India which is at the forefront of digital user experience. This article is based on our experience of building chatbots for numerous organisations in the social sector.
Chatbots are not just the helpdesk assistants of the future, they are the helpdesks of the future.
There, we said it.
As a company that makes chatbots and has access to a tonne of data that repeatedly shows how chatbots are more effective than your typical customer service agent, we at Kommunicate believe that there will be only one channel of communication between you and your customers in the future — chatbots.
1.4 billion people on the planet currently use chatbots on a regular basis, according to this report by Acquire. That’s close to twice the population of Europe.
As more and more millennials become paying customers who prefer to talk to a computer rather than a real human being, adding a chatbot to your website is not just common sense, it is a matter of survival.
But what if you already have a chatbot involved, which is taking care of all those customer conversations, doing a bit of lead generation for you, handling customer support, and even helping out the sales team. You must have got it all figured out and would now just have to sit back, sip iced tea (or coffee, if you prefer) and watch your business grow, right?
Wrong.
Chatbot analytics is as important as adding a chatbot to your website. Having a feedback loop helps you find areas of improvement for your chatbot, which can only lead to greater business.
In this blog post, we are going to examine:
Why it’s important to measure chatbot performance
Top metrics you should monitor
Additional resources to learn more about chatbot metrics
Focus on the right metrics: Implementing chatbots to your website is just step 1 of ensuring that none of your customers falls through the cracks. Many times, businesses do not get the desired results from chatbots because they have been optimized for the wrong metrics. Measuring chatbot analytics helps us to track the most important KPIs and make decisions that are data-driven.
Gauging how effective is your chatbot: If you have the right performance indicators in place, you can measure how effective it is to have a chatbot on your website. Answering a few basic questions like, “How helpful are chatbots in solving my customer queries?” or “ Are the chatbots directing my customers to drive profitable actions?” will go a long way in gauging the effectiveness of your chatbot.
Understanding customer journey: To be data-driven, you need to visualise certain aspects of your customer journey on your website, such as user paths, exit points etc. A good chatbot solution comes bundled with a chatbot analytics dashboard that helps you map all these details, helping you understand the customer journey that much better.
Measure ROI on business: Chatbots have a real impact on business, and chatbot analytics provides information such as the total number of leads generated, total tickets resolved, average time spent per conversation etc. With these metrics in hand, you can take calculated business decisions such as how much additional investment is required on your website and in which areas.
Top Metrics to Measure Chatbot performance
We have classified the metrics that you need to track into 4 broad categories, and are listing them out here in the order of least important to most. Here are the top 12 metrics, in our view, which you need to keep track of to measure how effective your chatbot is.
Goal Completion Rate
Conversation Starter Messages
CSAT Scores
Bot intent analytics
Bot Messages
New Users
Total Users
Active Users
Engaged Users
Bounce Rate
Fallback Rate
Conversation Duration
Goal Completion Rate: GCR is on the top of our list because it successfully measures how effective your chatbot actually is, by capturing the percentage of user interactions that have been successful over the chatbot. Your bot essentially exists to answer a customer query, and this metric tells you how effectively your bot processes input and gives a response that answers that customer query satisfactorily. GCR is dependent on how good your Natural Language Processing and Artificial Intelligence Capabilities are.
Conversation Starter Messages: Interactions between the bot and the customer is a two-way street, and the number of times the bot initiates the conversation forms the basis for this next metric. Companies need to initiate conversations with customers so that they stay on the website longer, so in a way, conversation starter messages help measure the organic reach of your platform. Be careful to not sound too pushy in your conversation starter messages though, they may scare away your potential customers. Sound natural, and be warm.
CSAT scores: Customer satisfaction scores are an important metric to measure since all businesses want happy customers who will keep coming back. To measure customer satisfaction with your chatbot, all you have to do is ask the customer to leave a “like” or a “thumbs down”, or leave a score out of 10, as we do here at Kommunicate.
As you can see, Kommunicate’s chatbot “Eve” is right there at the top with the human agents when it comes to CSAT scores, meaning the bot is effective and people actually like interacting with it. But since the agents still beat Eve to the top position, there is a lot of scope for improvement. Another way of measuring the effectiveness of your chatbot is by measuring the CSAT scores before and after the bot was introduced onto your website. If the CSAT scores show a dip, it means that probably the chatbot was not as effective as you wanted it to be and you might want to reconsider having one on your website.
Bot Intent Analytics: Bot Intent Analytics helps your developers assess how their messages are mapped to specific intent categories. It is a measure of how “smart” your bot currently is and how it can be improved.
As you can see, the Welcome intent was triggered the maximum number of times, which means most of the visitors to your chatbot started their question with a “Hi” or “Hello” and the bot responded accordingly.
Bot Messages: The total number of messages sent by the bot during the course of a conversation forms the basis of this next metric. This metric measures the length of the conversation between the customer and the bot, and we generally want this number to be high. An important caveat to note, we don’t want this metric to be high for the wrong reasons, like, for instance, if the bot gives the same answer over and over again to a query it doesn’t understand.
New Users: This is an important metric to measure, especially if you have just deployed a chatbot onto your website. The number of new users that your chatbot has helps you gauge how popular your chatbot really is, which will then drive your business decisions on making the chatbot handle more things. As with any new fancy technological advancement, customers’ interest is bound to decrease over time in your chatbot, and if you are able to attract a good pool of new users to your chatbot, you can keep the momentum going.
Total Users: As the name suggests, this metric tells you the total number of users who are interacting with your chatbot. This is an important metric to track since it allows you to measure the impact of your chatbot and its overall success. The total users also give an indication of the amount of data that the chatbot is exposed to, and you can use this information to calculate the market size.
Active Users: Active users are those who have read the messages from your chatbot in a given time frame. This is an important metric to track because, given the number of your active users, you can easily get an idea of how many potential customers you have for your product or service. This in turn lets you measure the effectiveness of your marketing efforts, and you can now invest resources where they are actually required. Note that with Active users, Engagement is not guaranteed, and the metric only shows how many people have seen the content on your chatbot.
Engaged Users: Unlike Active USers, Engaged users measures how many people actually send back messages to the chatbot, once your bot has initiated the conversation. This is an important metric to track, since it can give you access to conversation statistics, telling you exactly how effective your chatbot is. If you have set up a chatbot to answer FAQs or simple billing questions, you want this number to be higher.
Bounce Rate: This is an important metric to track, not just to measure the effectiveness of your chatbot, but to measure how well your website is performing as a whole. The bounce rate represents how many people are visiting your website, and leaving without interacting with your chatbot. We want to keep this number as low as possible since there is no point in making your bot smart and able to answer all those complicated queries if no one is interacting with it. There are a wide variety of reasons why bounce rates are high, including poor UX, website design, longer loading times etc.
Fallback rate: A FallBack response is one in which the bot does not understand the query from a user and gives a canned response that has been set by the bot designer. The rate of occurrence of this Fallback response is called the Fallback Rate and to effectively design a chatbot, you should know the user messages that trigger these fallbacks. If the chatbot is placed wrongly, then the FBR is bound to go up, or it could also be a fault in the NLP engine if the bot is not able to understand what the user is looking for.
Conversation Duration: The last factor on our list is a bit tricky to quantify since there are 2 sides to this coin. The conversation duration between your chatbot and the user needs to be just right, neither too long nor too short. If the conversation duration is too long, it means the user is having a tough time finding what they are looking for, and will most likely navigate away from the website. If the conversation duration is too short, it means the bot has effectively failed to engage the user, and they have moved out of the website without staying too long. Either way, it is very important that you keep a close eye on the conversation duration since it will help you make subtle changes to your chatbot design and keep your customers engaged on your website.
Parting words:
Having close to half a decade’s worth of experience building chatbots, we at Kommunicate have access to chatbot data that is sure to make the analyst in your team go “Ooohhh.”.
In this blog post, we have told you about the top 10 metrics you must absolutely look at if you are investing in a chatbot for your website, but we want to leave you with more.
If you want to learn how to build a chatbot from scratch, you can read our blog post here.
If you prefer to watch a video instead, we have created one on how you can build your own chatbot without a single line of code, which you view here.
WhatsApp is one of the most popular messaging platforms out there, and if you want to learn everything about WhatsApp chatbots, you can read why e-Commerce businesses need WhatsApp chatbots here.
Insurance, and Financial companies like Wells Fargo, Citigroup, Bank of America, JPMorgan Chase & Co., American Express, and Fidelity Investments own the largest call centers in the US and witht over 3.3 million call center workers nationwide. However, we’ve seen a shift in how enterprises are investing in technology to reduce customer support costs and automate the bulk of customer requests. According to Juniper Research these operational cost savings will reach $7.3 billion globally with the help of Conversational AI in banking by 2023.
In this article, we’ve looked at the Top Conversational AI Chatbot Use Cases in Banking and Financial Services industries as well as the benefits of these implementations.
Conversational AI Chatbot Use Cases in Banking and Financial Services
Top Conversational AI Chatbot Use Cases in Banking and Financial Services
Chatbot Use Cases in Banking #1. Checking account/card balances
A great benefit that chatbots’ offer is their ability to solve a myriad of issues and answer questions all in one place, 24/7. With the help of a banking chatbot, banks can cover more personalized requests, AI-powered chatbots request user verification, and only after this, all account information becomes available. Checking account or card balances is a top user request, as 36% of Americans check their balance daily.
Chatbot Use Cases in Banking #2. Payment due date questions
Banking chatbots can easily answer questions around payment due dates, whether it be for bills, loans, or credit cards. According to the FRS, Delinquency Rates under Consumer loans rose to 1,73% in the first quarter of 2022. This number could be even less with the automatization of regular payment and more availability through different channels. AI banking chatbots are able to proactively remind customers of their upcoming due dates to prompt users to make a payment.
With the everyday hectic schedules of Americans, there’s a lot to balance and ensuring all bills are paid on time can be a challenge. Conversational AI solutions address this pain point by offering bill reminders, answer payment due date questions and can even perform payment activities from customer requests. Chatbots can now guide users through paying their bills as they understand their balance and can use saved payment methods on their accounts to make payments once advised to. They also can set up automated payments for customers, leaving them to have one less thing to worry about.
Chatbot Use Cases in Banking #3. Making a payment (e.g: loan or credit card)
Conversational AI integrations of a bank’s backend systems can guide users through the process of making a payment or managing their payment methods. These conversational experiences can actually be faster than a user paying over the phone, website or even an app, due to the agility and speed of a chatbot and its ability to perform a multitude of tasks and actions.
Chatbot Use Cases in Banking #4. Transfer funds between accounts
Transferring funds between accounts can also be performed also with the help of AI banking chatbot, but even more, it could prevent fraud and cyber attacks. The number of victims of credit or debit card fraud rose to 127 million people in the United States by 2021. Fraud prevention in banks and overall finance is critical, and Conversational AI chatbot has a strong potential for its detection.
A finance chatbot can ask a user questions from the context to prove that the user is not a robot and immediately track the geographical location to check the transaction history. If some of these factors are new, an chatbot can immediately ask the user some of the questions from the previous context to identify that it’s the correct person engaging. If there are some concerns, then a more detailed authentication activity can be involved to verify the user is who they say they are, which may include escalation to a live agent in some cases.
Chatbot Use Cases in Banking #5. Report lost or stolen card
In case a customer loses their credit card, action should be taken immediately to freeze or lock the card. To proceed with this, the client needs to find a relevant phone number and call the credit card issuer. But waiting on a long list for an available live agent — is not the best option for the user, and here is where an AI banking chatbot can support. The user may report their lost/stolen card, check out if the money is still available under the account, and then try to physically find a card.
Chatbot Use Cases in Banking #6. Ask for the most recent charges on an account or card
Another benefit of these banking virtual assistants is that they can track recent transactions and charges, ready to answer these questions from customers about their latest spending activities. According to CNBC, 42% of Americans have forgotten that they’re still paying for a subscription they no longer use and these chatbots can answer questions about subscription charges or list the high spending categories of a user.
Chatbot Use Cases in Banking #7. Exchange rate or stock price questions
Chatbots can easily answer questions related to currencies, exchange rates and stock prices. No longer do customers have to search through different pages on websites or apps. They can simply ask the banking chatbot a question about the markets in real time and get an accurate answer instantly.
Chatbot Use Cases in Banking #8. Current interest rates for loans and mortgages
AI-based banking chatbots offer a viable alternative to human personnel in providing a whole spectrum of information for company services and latest propositions. They can answer queries related to interest rates for loans and mortgages all in real time, giving the most up to date information to customers instantly.
Benefits of Conversational AI chatbot in Banking and Financial Services
Financial Service institutions have been one of the leading adopters of Conversational AI as part of a push to modernize financial services, primarily banking, making them easier to use and more accessible. Let’s take a look at these company-wide benefits of Conversational AI in banking and finance.
Benefits of banking with Conversational AI
Reduce customer support costs.6 in 10 financial services and organizations ranked customer experience as their top priority as most consumers say they are more likely to switch service providers for poor customer support. Conversational AI for Finance helps enhance customer experience through 24/7 support. With Master of Code, companies can deploy the most advanced virtual agents for a wide variety of issues.
Faster payment services are directly tied to higher revenues across the industry. Conversational AI enables businesses to significantly cut down time-to-payment with purpose-built sales bots. Businesses can deploy AI finance chatbots on popular platforms with built-in payments services for instant checkout, expanding the company’s Point of Sale (PoS) systems in the process.
Reduce resources for customer acquisition. The average cost per customer acquisition in the banking industry is USD 300. Financial service organizations can reduce this cost (and time) with the right finance chatbot. AI chatbot implementation for banks leads to a more effective conversation start with potential customers. Chatbots reach a wider audience but have a low cost and minimal effort implementation, thereby reducing the customer acquisition cost.
Personalized Customer Experience. 72% of customers rate personalization as “highly important” in financial services organizations. Despite its importance, most modern FSI organizations are unable to customize the customer experience beyond the very basics. FSI companies use AI in finance chatbots to leverage customer data and tailor a customer experience based on their preferences, previous queries, personal details, and all this within a secure infrastructure.
The financial services industry (FSI) is at the forefront of testing and deploying the latest consumer-facing technologies. As a pioneer in Conversational AI, Master of Code is a proud partner for numerous innovating and forward-thinking financial services providers.
1,4 M hours saved with implementing the automated customer service tool Erica at Bank of America. Ready to transform?
