The banking industry has witnessed a drastic transformation with the onset of artificial intelligence (AI) and machine learning (ML). Using these updated tools and techniques has overall changed the daily chores of every bank employee. After the banks started implementing these AI and ML tools, they started witnessing an edge over their competitors in terms of customer satisfaction, marketing, and process automation.
It is often noticed that due to the popularity of sophisticated chatbots like Gemini and ChatGPT, banks and other financial organizations prioritize AI technologies. Banks can benefit from the successful integration of chatbots that eventually led to enhanced automation, reduced costs, and improved client retention rates. As per a survey conducted by Grand View Research, 70% of banking and finance customers have used the same chatbot repeatedly.
The chatbot market was valued at $ 396.2 million in 2019, which in 2027 will reach $ 1953.3 million, and by 2030, the global chatbot industry is expected to reach $27.3 billion. The later sections of the article will discuss in detail the use of AI chatbots in banking and the prospects for these AI technologies in the banking and financial sectors.
*Note: The above data has been referred from various research papers and websites such as, Statistica, Research Gate, Hubspot marketing Statistics, and others.
What is conversational AI in the banking industry?
Conversational AI helps banks and their customers to communicate through text messages, phone calls, mobile apps, and websites. If implemented correctly, conversational AI banking gives customers a more convenient and fulfilling experience. Banks that implement these AI technologies not only understand their client's requirements and provide them with personalized solutions but simultaneously can provide an enriching and smooth experience for their clients.
Conversational AI enhances personalization, which further engages in human-like conversations and thereby retains user preferences, context, and meaning. These AI chatbots surge user engagement and provide a rich and fulfilling consumer experience. AI-powered solutions have several benefits, including easier scaling, better customer service, lower customer care expenses, and enhanced accessibility and efficiency.
Conversational AI is continuously evolving and helping with more chores and interactive features, which will eventually improve the quality of information provided, make communication efficient, and create an overall happy user experience.
History of conversational AI in the banking industry
In the 1950s, Alan Turing first proposed the idea that a computer program could communicate with people. Eliza, the first chatbot software ever created, was a computer program that mimics a psychotherapist's speech using natural language processing. For the first time, a conversation between a chatbot and a human was made possible. Smartphones have pushed forward the creation of artificial intelligence. It was hard for developers to get websites functioning on desktops, tablets, and smartphones and adjust to various screen sizes in the early 2000s.
They started wondering whether a better interface might be possible to enhance the user experience as they struggled to enhance functionality and graphical arrangements. The initial chatbot technology, released in 2001, provided a way of gaining information like news, weather, scores for sports, cinema showtimes, share prices, and yellow page directories by enabling the development of hundreds of mobile apps. SmarterChild on MSN and AOL was an important milestone in chatbot technology's growth.
Of the numerous apps created from 2010 to 2016, the most popular are Siri, Google Now, Cortana, Alexa, and Google Home. Apple developed Siri, a natural language artificial intelligence chatbot, in 2011. Translation, calculations, checking facts, payment, navigation, scheduling events and reminders, and controlling settings would all be facilitated by it.
Chatbots in Banking Trends
As the technology behind chatbots continues to evolve, they’ll go beyond the simple role of answering queries and continue to become fully featured virtual agents. These are the current trends driving the evolution of banking chatbots:
1. LLMs are being widely used to inform chatbot
Large language models (LLMs), such as those behind Google's Gemini, DeepSeek, Gemma, Falcon, and OpenAI's ChatGPT, are already being used to assist chatbots in creating more "human-like" conversational exchanges. Some of the primary hurdles that are faced while implementing LLM models can be avoided by using a resource with a more narrowly focused scope, such as the website of a bank.
This facility makes way for banking chatbots that can converse about the particular banking products and processes offered by that bank. More powerful and accurate natural language understanding (NLU) engines can be built by using the fine-tuning feature of intent models. This eventually allows the chatbots to understand even the most complex customer queries and easily respond with the correct answers.
2. Rise in the usage of Voice bot
Voice bots can now answer voice commands in natural language through ongoing advances in speech recognition and intelligent virtual assistants (IVAs). This makes it easier for direct interactions with employees and customer support. As conversational becomes popular, they will start to handle the majority of consumer transactions, like notifying customers of their account balance or conducting PIN resets. Further, they will be a routine tool for in-house purposes such as filling data in the bank's CRM automatically and recording conversations to refer to them later.
3. Automation of additional tasks
The ability of banking chatbots to manage consumer self-service financial transactions and other tasks will significantly increase in the near future. Advanced chatbots can read documents instantly when they are uploaded using methods like optical character recognition (OCR). They can also extract data to look for errors, discrepancies, or missing information. Giving chatbots more authority over documentation, such as internal reports and customer applications, allows them to upload the data into the bank's CRM systems, verify that the data on the documents is accurate, and even provide direct feedback to make sure all the data is correct.
4. Easy to Use
Increased usability. To maintain and update, modern chatbots don't require a great deal of programming knowledge. No-code platforms will offer any appropriately skilled employee of a financial institution the capability to develop on top of the original programming framework. AI trainers can modify integrations on their own with the use of robust, intuitive API self-service tools, eliminating the need to wait for a developer or communicate with suppliers for small adjustments. Future chatbots are ready-to-use, creative solutions that can be swiftly modified to accommodate evolving corporate procedures.
Components of conversational AI in the banking industry
Conversational AI is majorly structured on Four basic parameters, namely machine learning, natural language processing, data, and conversation design. Each of these parameters has been elaborated on in the section below.
1. Machine Learning
Machine learning consists of algorithms, features, and a unique data set that eventually improves with time. With the increase in consumer queries, the accuracy rate of the AI chatbot simultaneously increases. The banking sector incorporates machine learning (ML) to enhance customer service, credit rating, and fraud detection. Additionally, it can support trading, risk management, and regulatory compliance.
2. Natural Language Processing
Conversational AI uses NLP to analyze language using machine learning. Language processing techniques have changed over time from linguistics to computational linguistics to statistical natural language processing. Future conversational AI's NLP capabilities should be greatly enhanced by combining this with machine learning.
3. Data
Contextual information about each user and training data from similar conversations are crucial for conversational AI to succeed. The AI may determine the best time and method of communication based on user preferences, demographics, or transaction history.
4. Conversation Design
Concerned companies and organizations must create the content that the AI will share during the conversation. Developers can choose responses that fit the AI's specifications by using the best data from the conversational AI application. Natural language-generating methods or human writers can then fill the gaps.
How does conversational AI work in the banking sector?
The Four broad steps in the working of conversational AI in the banking sector have been listed below for your reference.
- The first step includes providing or feeding proper information, which can be done both through verbal and written methods. Voice recognition transforms the input into machine-readable text as it is spoken.
- The next step requires the application to understand the meaning of the text. This process is done by implementing natural language understanding (NLU), a component of NLP algorithms.
- The application uses dialog management to decide the response based on its interpretation of the meaning of the text's message. The other component of NLP is dialog management, which involves organizing responses and using natural language generation (NLG) to transform them into a format that is legible by humans.
- Depending on the platform, the user can receive responses through voice messages or text delivery from the application.
How are chatbots altering the banking industry?
Today's conversational chatbots are a step up compared to the previously constrained first-generation models. Despite the perception that chatbots are excessively impersonal, cold, and restricted, modern chatbots in banking include several specialized features, infinite scalability, the ability to conduct more natural-sounding conversations, and the capacity to learn thousands of topics. These chatbots offer several benefits to consumers and financial organizations by using natural language processing and machine learning, or artificial intelligence (AI).
There are several advantages of using banking chatbots, where they help enhance the user experience by retorting efficiently to various customer queries, streamlining outdated processes, and sending out updates and notifications. Apart from this, banking chatbots also help in building comprehensive customer profiles and facilitating more interesting and personalized interactions between customers and banks.
Banking operations previously needed a lot of time-consuming paperwork and hours of processing, which now can be streamlined, all thanks to chatbots. These chatbots help clients in operating various kinds of tasks, such as opening bank accounts, getting balance information, or obtaining certain account details. This, in turn, has created well-managed and qualified bank employees who can now devote their time to something more meaningful and essential for the business.
Following the research conducted by the Consumer Financial Protection Bureau (CFPB), it has been analyzed that all 10 of the largest commercial banks in America now have chatbots integrated into customer support. An estimated 37% of Americans interacted with a bank chatbot in 2022, and the figure is predicted to increase. Along with the development of chatbot technology, banks' utilization of the technology has changed as well. More sophisticated technologies such as large language models ("LLMs") and those labeled as "artificial intelligence" are now taking the place of simple, rule-based chatbots in banks.
Chatbots have been in the banking sector for almost a decade; therefore, they have gained immense popularity. Basic rule-based chatbots with decision tree reasoning or phrase or emoji databases that offer pre-coded, limited responses are employed by a majority of the industry today. Proprietary third-party software companies often power them. For example, AI chatbots are used by Citibank, while Kasisto provides conversational chatbots with a financial intent for TD Bank and JPMorgan Chase.
Uses of conversational AI in the banking sector
The top Four areas where conversational AI is used are as follows:
1. Customer support
Banking chatbots handle a diverse range of customer queries related to their bank accounts, statement requests, money transfer queries, and activating their cards. These banking chatbots use NLP to understand customer queries and give prompt responses.
These banking chatbots can be accessed 24/7, thereby making them accessible for information by any existing client or prospective client for a bank. In addition to answering various FAQs and sharing additional information about supplementary banking services or features, the banking chatbot can help consumers navigate online banking platforms.
2. Managing accounts
Banking chatbots have access to a client's account details, such as account balance, online money transfer, payment deadlines, and other related information. Banking chatbots help customers have relevant conversations when requesting information about their accounts, thanks to conversational AI. Banking chatbots can also incorporate relevant offers into conversations by understanding the context for every encounter, which raises the possibility that customers would accept and purchase.
3. Onboarding new clients
Banking chatbots lead customers through account opening by inputting the information needed, e.g., contact details, ID documents, and personal information. Banking chatbots can question users' questions and validate users' identities from their documents, such as driver's licenses, passports, or government documents. Finally, banking chatbots can help upload documents, verify documents, and mark inconsistencies or discrepancies for further review.
4. Applying for loans
By guiding their clients at every step, banking chatbots also help customers with the loan application process. The overall process may include steps such as gathering the required data and giving them status updates, which might, at times, be difficult for some clients to manage on their own. Banking chatbots can help users fill out and submit the necessary paperwork, including terms and conditions agreements.
Benefits of using banking chatbots
Some of the major benefits of using banking chatbots are listed below for your reference.
1. Superior customer experience
With the onset of conversational AI and banking chatbots, banks today offer personalized, effective, and 24/7 client support. This has further helped to get overall client satisfaction. There are ample tasks that are often performed by banking chatbots, such as loan applications and account balance checks.
2. Cost cutting
With the basic conversational tasks being performed by these banking chatbots, conversational AI has helped banks cut operational costs related to customer service and retention by automating routine tasks. Banking chatbots are also able to manage several tasks simultaneously, thereby helping employees to concentrate on more difficult and dominant tasks.
3. Advantage over competitors
Businesses and industries are continuously evolving based on their peer competition and dynamics. It is in situations like these that chatbots help keep these businesses ahead of their peers and competitors. Banking chatbots help in generating new business ideas as well as retaining existing customers by offering client-friendly banking services.
4. Efficient service
Conversational AI reverts with immediate responses to questions, increasing process speeds and minimizing consumers' waiting time. Self-service and automation accelerate issue resolution, ultimately increasing bank staff members' productivity and efficiency.
Challenges of using conversational AI in the banking sector
A CFPB report states the technical limitations of the operations of chatbots can have severe negative impacts on their potential customers. They can suffer a range of negative outcomes, including wastage of time, frustration and being stuck, incorrect information, and payment for higher trash charges.
When individuals are not able to receive specialized assistance for their issues, these issues come much more into focus. Discussed below are some of the challenges of implementing conversational banking chatbots.
1. Accuracy issue
Being a machine at the end of the day, the banking chatbot will only be able to interpret data from the customers that have been pre-fed to them. Any other queries of data asked to bank chatbots that aren’t programmed will not be responded to articulately. Large language models, often used in AI, will not be processed if the data is insufficient and unstructured.
2. Bias risk
Since banking chatbots often recommend based on their previous dialogues and histories, this can lead to them favoring particular demographic populations while discriminating against other sections of people. This will often require the banks to monitor and assess any sort of discriminatory issues led by banking chatbots.
3. Compatibility
The customer details in a bank are often saved using old software. This poses a big issue while pairing them with the newly updated chatbots. It is, therefore often suggested banks invest heavily in API development and cloud infrastructure to make sure of a seamless transition between the two.
4. Data security and privacy
Keeping user and customer information anonymous is the foremost priority of a bank. Thus, it is often advised that banks update their storage capabilities and implement various techniques, such as data masking and strict access policies, to encrypt their user data and make it safe.
List of banks that successfully implemented AI chatbots
The list of top banks that successfully implemented AI Chatbots are American Express, Bano of America, Capital One, JP Morgan Chase, and Wells Fargo to name a few. Some of the top baking chatbot examples and their results are elaborated below.
1. American Express
Launched in September 2016, American Express utilizes chatbots known as Amex for customer support. Users have access to information and support via their online account at americanexpress.com from a virtual AI assistant.
- In regards to tracking account balances, paying bills, reviewing recent charges, accessing Amex offers, and checking available credit or Membership Rewards, Amex chatbots can aid customers.
- Amex uses AI-driven analytics to make services and product recommendations personalized based on past transactions and the shopping behavior of customers to recommend special offers and rewards.
- Amex Flex is the distinct manner of American Express operating, where certain employment is kept open for full-time, blended, or all-virtual working.
- Daily and monthly limit, American Express virtual prepaid card connected to a GCash account enables consumers to spend domestically and abroad.
2. Bank of America
Launched in June 2018, Erica, the chatbot assistant for Bank of America, helps share information about how to save and deposit money, bill payments, and other account-related information. The list of crucial functions that the Bank of America chatbot Erica performs is listed below.
- Erica adds more options to rapidly respond to your questions, such as a live chat with a Bank of America expert
- Get notified when merchant refunds are posted to your account
- Get alerted when there are duplicate charges and check redeemed rewards
- Keep an eye on recurring charges and price increases
- Get bill reminders when payments are due
- See weekly summaries of monthly spending
- See balances for all your accounts
- Erica assists you in getting the most from your money and also assists you in replacing a missing or stolen card, reviewing balances, and remotely locking or unlocking your debit card
- Erica is able to assist you in obtaining your routing and account numbers
- Erica is able to provide you with quotes, monitor performance, execute trades* and match you with a Merrill advisor* from the Mobile Banking app
3. Capital One
Launched in March 2017, the chatbot assistant for Capital One is called Eno. Eno helps customers by sharing tips on investing their money, account security, and their account debit and credit bills. Some of the key features of the Capital One chatbot are as follows:
- By keeping an eye out for unexpected charges and generating virtual card numbers when you shop online, ENO safeguards your credit card account
- By keeping an eye on your credit card account and providing you with helpful insights, ENO can identify free trials, recurring expenditures, and other expenses. It also helps in tracking your spending
- ENO checks your balance and responds to inquiries regarding your account
- Eno also helps respond to various customer queries
- Eno notifies you of significant account activity, thereby enabling you to act promptly
- When you use your desktop PC to shop online, Eno can generate virtual card numbers
- Eno can respond to your inquiries and discuss your account via text message at any time
4. JP Morgan Chase
Launched in July 2024, JPM Bot is an interactive chatbot that is accessible in both Traditional Chinese and English with the goal of assisting you in accessing exclusive information and making well-informed investing decisions.
- To begin your investing journey with JP Morgan Chase, click the chatbot icon located in the lower right corner of the screen
- Use an intuitive carousel menu to get answers to frequently asked questions
- Provide industry themes that might interest you to gain instant access to the insights of JP Morgan Chase
- Provide you with guidance if you're unsure of where to begin. Simply type your inquiries, and JPM Bot will assist you
5. Wells Fargo
Launched in October 2022, Fargo, the AI assistant of Wells Fargo helps its customers by sharing information on spending summaries, FICO ratings, closing cash, and checking account details. Some of the key features and tasks performed by Fargo are listed below.
- Check savings and recommend investments
- Helps in online banking
- Suggests retirement plans for effective monetary affluence
- Helps protect customer accounts from online phishing and fraud
- Provides insights on home loans and the benefits of addons to it.
Conversational AI will significantly impact banking and financial services. Banks and other financial organizations need to enhance consumer interaction, optimize processes, and yield insightful data by implementing conversational AI and banking chatbots. With time, it is evident that conversational AI and banking chatbots will be the need of the hour for a seamless, transparent, and efficient working structure.
Conversational banking stands as a beacon for a symbiotic relationship between clients and banks. The system not only stops at the implementation of conversational AI but simultaneously requires banks to pave the way for what modern banking should look like and epitomize.
