Table of content
In 2021, The Fintech Times reported that the percentage of midsize banks and credit unions using chatbots tripled in a single year. Usage leaped from only 4% to 13%. By the end of last year, analysts predicted usage may double from that.
As their presence grows, chatbots are beginning to redefine the banking experience itself. Fintech AI—and chatbots in particular—have the potential to reshape user expectations and desires when banking. Already, chatbots have:
- Strengthened customer loyalty
- Empowered better financial management
- Improved lead conversion rates online
- Streamlined routine banking processes
- Improved investment outcomes
- Automated fraud prevention
These successes point toward a future where banking chatbots are the norm. But, they only work when well-implemented. Use them poorly, and chatbots can backfire.
Discover the different types of chatbots banks can use and how each type works. Then, learn how banks like yours apply chatbots to unlock their full potential.
Banking Chatbots: An Overview
Chatbots are software applications that simulate conversations. The simulated conversation serves customers by giving them information, instructions, or advice.
Banks and credit unions typically integrate chatbots into their websites and mobile apps. A bank will often stylize the chatbots' appearance, vocabulary, and tone to fit with its brand.
The chatbot may offer to help, or a user may seek out their help by clicking or tapping on the icon.
Banking chatbots are customer-facing. Which program is best depends on which customer needs the bank wants to serve.
To answer common questions, a rules-based chatbot is typically all you need. But to meet more complex needs—or, to simulate conversation more realistically—a bank might need an AI-driven bot.
Rules-based chatbots simulate conversations by using a simple flowchart. At various points in the conversation, the chatbot asks a question, and it gives users a few options.
Each answer from the user causes the conversation to go down a different "path" in the flow chart.
When the chatbot makes all the correct path choices, based on the customers' answers, it gets the customers the information or page they're looking for.
The effectiveness of a rules-based chatbot comes down to the accuracy of the chart. Computer scientists sometimes call these applications "simple chatbots" or "set guidelines chatbots."
The set guidelines are the information and pathways in the chatbot's internal "flowchart." Most set guidelines are decision trees or if-then trees.
Menu Chatbots (Decision Tree)
Menu chatbots present options to the user in the form of a menu or buttons. A banking menu chatbot may present buttons with frequently asked questions, like, "How do I find my routing number?"
Or, a menu might offer categories of information, like "account security" or "open an account."
Problems with Menu Chatbots
Two problems with menu chatbots are language barriers and non-intuitive categorization.
Language barriers mean a customer may speak English as a second language, or that his regional dialect doesn't match the chatbot's English. A customer may close the menu if the chatbot uses unfamiliar terms, even if the customer knew what he was looking for.
Non-intuitive categorization is another problem with menu chatbots. A customer may have a common request: she's looking for information on personal loans.
But, she may not intuit that the bank has categorized personal loans under "borrowing."
If the menu has no "loans information" category, she may be frustrated and try to call a human agent—even though the chatbot had the information she wanted somewhere in its menu.
Linguistic/Conversational (If-Then Tree)
Linguistic chatbots use an if-then tree. They're slightly more sophisticated than menu chatbots. They overcome language barriers better than menu chatbots. And, a linguistic chatbot doesn't require customers to intuit its categorization.
Rather than require a user to click on specific buttons, linguistic chatbots can respond to a range of human statements, questions, and responses.
These chatbots understand synonyms. They can get users the information they want, even when users type different words to get there.
AI Chatbots (With Machine Learning)
AI chatbots are considerably more sophisticated than "flowchart"-style chatbots.
Machine learning enables scientists to train chatbots. Chatbots are trained to understand relevant information and human speech through massive datasets.
Training lets chatbots understand both the definition of terms in a question and the question's context. It also supplies chatbots with methods to give customers access to their personal information in a secure way.
While training improves a chatbot's sophistication and realism, it's also expensive. Sometimes the return on investment is worthwhile for banks, particularly if it improves customer acquisition.
Keyword-based chatbots use Natural Language Processing (NLP).
NLP is an AI application that helps chatbots understand human languages by focusing on keywords. It enables chatbots to match keywords customers use to relevant information.
Some engineers have created hybrid chatbots that combine menu chatbot functions with NLP. While NLP improves a chatbot's accuracy, it still has some shortcomings.
For example, some keyword-based chatbots don't always distinguish between "what" and "how" questions with the same keyword, so they supply the same answers for both.
Contextual chatbots can get to know individual customers personally. Machine learning enables these chatbots to remember things a specific customer said or did previously.
This lets chatbots serve customers faster. And, it increases the likelihood that the customer gets what she wants the first time.
Engineers train voice-based chatbots to understand keywords and context. They also help their "voice bots" interpret a wide range of spoken dialects and vernaculars.
These chatbots can specifically engage in spoken conversation rather than just text. Siri and Alexa are famous examples of voice-based chatbots.
Hybrid chatbots are a popular compromise for many banks. These models have more sophistication than rules-based chatbots. But, they're not as expensive as complete AI chatbots.
How Chatbots Improve the Banking Experience
Chatbots are revolutionizing banking by automating services across the board. As conversational AI, chatbots are equipped to do many customer service tasks, which frees up human agents' time.
Banking chatbots also streamline repetitive processes. All of this culminates in eight key improvements to the banking experience.
Lead nurturing increases banks' customer acquisition. Customers open accounts more often after a pleasant introductory conversation with a chatbot.
Lead generating conversations with chatbots also play a role in conversion.
Chatbots streamline service by getting people answers to their most common questions. This reduces the workload burden on customer service agents.
When there's a high call volume, agents can focus their energies on the more complex customer needs. This lets customers meet their needs, no matter the complexity of their issues.
Opening a Bank Account
Chatbots can walk a customer through opening a bank account. They can also answer questions about documentation and using the website.
If there's an unexpected issue, the chatbot can transfer a customer to a live agent. And, it can automatically send over the customer's data.
Navigating Personal Banking
Chatbots help customers navigate personal banking with:
- Automated support
A bank can set up a chatbot to automatically send a message upon a trigger. The trigger may be a keyword or an issue like an overdrawn account. Keywords may also trigger messages that advise about savings or loan options.
Chatbots can also recognize customer patterns. This lets them remind customers about things like upcoming automatic payments.
Loan and Mortgage Help
Banking chatbots can quicken mortgage and loan processes. Mortgage loan processing specifically involves repetitive tasks. Chatbots can streamline the loan application process. They walk applicants through the process and answer questions.
Chatbots also help pre-qualify a borrower for a loan. They can communicate important, personalized information about risk factors to the customer.
They can automate data extraction and document classification as well. This can improve loan application success rates and streamline repetitive parts of the process.
Chatbots can facilitate fraud prevention and anti-theft services. This may include:
- Automated fraud detection alerts
- Proactive communication (anti-theft measures)
- Account monitoring
24/7 Customer Service
Chatbots never sleep. They can offer services to customers during non-banking hours. They can also set up a callback from an agent if necessary.
Customer Feedback Aggregation
Chatbots are a great source of customer feedback. They can ask customers questions about their experience in an engaging way.
This lets customers let banks know about their qualitative experience. Chatbots can then sort and funnel this data to other AIs.
These AIs can analyze the aggregated data. Analysis may reveal points of improvement. Or, it may suggest possible solutions to recurring customer problems.
How Will Chatbots Revolutionize Your Experience?
AI programs can improve services in a wide array of industries. Chatbots are now iconic applications that remind us what makes AI truly great.
Banking chatbots save time, engage customers, and improve human agents' workflow. How can chatbots change your work for the better?
Find out yourself. Book a demo!