Alex Kreger, UX Strategist & Founder of the financial UX design agency UXDA, increases banking and fintech products' value in 36 countries.
In just two months after its launch, GPT-3-powered ChatGPT reached 100 million monthly active users, becoming the fastest-growing app in history, according to a UBS report (via Reuters). ChatGPT is a language model that uses natural language processing and artificial intelligence (AI) machine learning techniques to understand and generate human-like responses to user queries.
I compare GPT's appearance with the launch of the internet in terms of its impact on the future of humanity. It enables machines to understand and generate language interactions in a revolutionary way. GPT (generative pre-trained transformer) AI could disrupt how we engage with technology much like the internet did.
It's only been about two months since the launch (as of the time of this writing), but we can already see how much ChatGPT impacts our experience. The internet is full of examples of crazy prompts to which ChatGPT and other large language models (LLMs) often provide accurate and competent answers. People are rapidly adopting ChatGPT and similar models for uses such as content creation, programming, teaching, sales, education and so on.
The main question for me, as a financial UX strategist and founder of a company with services including conversational banking, is how such technology will impact the banking and financial customer experience: because customer experience is key to business success in the digital age.
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According to a North Highland survey (via Consulting.us), 87% of leaders surveyed perceived CX as a top growth engine. Emplify research found that 86% of consumers would leave a brand they were previously loyal to if they had just two or three bad customer service experiences. An Accenture study from 2018 found that 91% of consumers are more likely to buy from brands that recognize, recall and provide relevant offers and recommendations.
To secure a primary competitive advantage, the customer experience should be contextual, personalized and tailored. And this is where I think AI will become the breakthrough technology that supports this goal. According to a survey from The Economist Intelligence Unit, 77% of bankers believe that the ability to unlock the value of AI will be the difference between the success or failure of banks. In a 2021 McKinsey survey, 56% of respondents report AI usage in at least one function of their organizations.
I forecast that LLMs and AI will impact the user experience in the banking industry in multiple ways.
First, they can analyze customer data to understand their preferences and needs and use this information to provide personalized customer service and support to users by addressing their queries and concerns in real-time. Banks could also use AI models to provide customized financial advice, targeted product recommendations, proactive fraud detection and short support wait times. AI can guide customers through onboarding, verifying their identity, setting up accounts and providing guidance on available products.
Second, AI can automate many routine tasks, such as account balance inquiries and password resets, freeing customer service representatives up to focus on complex issues. It could increase efficiency and reduce costs for banks while providing faster and more accurate customer support. And all of this would be available 24/7, making it easy for customers to get help by answering questions, resolving issues and providing financial education outside of regular business hours.
Third, companies could leverage AI to provide a conversational banking experience by integrating models with banking applications to provide a single point of contact for users to make transactions, view account information and receive alerts through the chat or voice interface in multiple languages. It could simplify the user experience and reduce the complexity of banking operations, making it easier for even nonnative speakers to use banking and financial services worldwide.
So, what are the obvious use cases for AI and LLMs in banking?
1. Account Inquiries
Banking users can employ chatbots to monitor their account balances, transaction history and other account-related information.
2. Money Transfers
Users could potentially make fund transfers to other accounts or to pay merchants through a chatbot.
3. Loan Applications
Banks can deploy chatbots to assist users in applying for loans and to guide them through the application procedure.
4. Credit Score Monitoring
Companies can develop chatbots to assist users in checking their credit ratings and provide advice on how to improve them.
5. Financial Advice
Banks could train chatbots to provide investment information and assist users in making informed investment decisions.
6. Fraud Prevention
Banks could explore ways to use AI to prevent fraud by monitoring user transactions and spotting unusual activity.
7. Customer Service
Banks could train chatbots to provide rapid and effective customer care by answering common questions and fixing simple issues.
8. Account Management
Banks could train AI models to assist users in managing their accounts by arranging automatic payments, changing personal information and more.
9. Insurance Claims
Banks could also create chatbots with the capability to submit insurance claims and get information about the claims procedure.
10. Financial Planning
Chatbots could assist users with financial planning tasks, such as budgeting and setting financial objectives.
Challenges And Considerations For Banks
Despite the inspiring prospects that AI technology opens up for improving the customer experience in banking, implementing it into banking products can pose some challenges. One of the main challenges is safeguarding the security and privacy of customer data. Banks should ensure that their chat interface is secure and that sensitive data is protected from unauthorized access or disclosure.
Another challenge is training an AI model to understand the language and terminology specific to the banking industry. Banks should provide relevant training data and integrate the model with their existing systems to ensure that it can provide accurate and appropriate responses to user queries.
And one more challenge is customer adoption. Banks should ensure that customers are aware of the chat interface and its benefits and that they are comfortable using it. This will require them to make additional product UX design considerations and invest in education efforts to provide an easy-to-use chat interface.
Natural language-processing capabilities and an understanding of customer data mean AI could become an excellent solution to provide a more personalized, efficient and convenient user experience in banking and financial services.
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