These days smart AI chatbots are a powerful asset for fintech companies and beyond.
They help you streamline first-line customer support, reduce operational costs, enhance staff productivity while elevating customer experience to forge lasting relationships that strengthen your market position and drive business growth.
Indeed, with recent AI advancement, creating such a chatbot has become easier than ever before.
OpenAI’s GPT, impressively capable large language models (LLM), are a perfect building block for creating custom, fully functional conversational chatbots.
In this article, we’ll elaborate on the concept of building a customer support chatbot powered by GPT for fintech.
- Business case or what problem are we trying to solve
- Key benefits of the GPT-powered chatbot for fintech
- Technically feasible use cases of the GPT-driven chatbot
- Navigating fine-tuning or how it all works
- Security aspect of the GPT-driven chatbot
- Exploring potential challenges and risks
- Strategies for mitigating them
- Prospects of a more advanced chatbot version
- Consider Kindgeek AI assistant services
- Final thoughts
Business case or what problem are we trying to solve
Enhancing customer service and achieving better operational efficiency – are two fundamental goals within the realm of financial services that executives try hard to attain.
Though being essential priorities, these two ones are often challenging to achieve.
Customer service: constantly raising bar
Amid such a wide choice available in the financial services industry now, people grow more demanding when it comes to customer service. Especially in times of recession, as they want to be guided, feel understood, and get efficient support for their financial concerns and inquiries.
That being said, now more than 64% of customers expect human-like service from chatbots and 76% get frustrated when their unique needs are not met. Indeed, a staggering 76% would switch to a business’s competitors after several bad customer service experiences, which leads to a depletion of business opportunities and revenue correspondingly. That’s a statistic bank executives cannot afford to leave unnoticed.
Operational efficiency: an evergreen necessity
Simultaneously, the need to streamline operations and optimize resource allocation has become paramount to maintaining profitability and remaining agile in the face of evolving market dynamics.
Striking a balance between superior customer service and cost optimization is a delicate yet necessary task for financial services providers, as it contributes to long-term success, customer satisfaction, and sustained growth in an ever-evolving industry.
And a GPT-driven chatbot can help with that. Here, let’s see how.
Key benefits of the GPT-powered chatbot for fintech
The GPT-driven chatbot is a powerful tool for banks helping them improve digital customer service, empower staff productivity, and cut operational costs.
In case you’ve missed, check out our previous article for a deep-dive on powerful benefits of modern AI chatbots in fintech – all backed up with recent stats.
GPT to improve customer experience
Unlike traditional chatbots that often use a predetermined scripted conversation flow and fail to understand the user and help them effectively, GPT-powered chatbot can deliver exceptional customer experience, as it’s adept at:
- conversing in natural, human-like manner to build meaningful interactions;
- understanding user intent to provide helpful outputs;
- grasping the context to build dynamic interactions;
- deciphering human sentiment to form empathetic responses;
- learning from previous interactions to offer personalized suggestions.
GPT to elevate operational efficiency
Employing a powerful GPT-driven chatbot you can streamline your first-line customer support, cut operational costs and reduce the pressure put on customer support teams, helping them refocus their efforts on more complex tasks.
Besides, such a chatbot has a game-changing impact on staff productivity, enabling them to be more efficient in their operational tasks.
Recent studies have indicated that GPT assistance increases customer service worker productivity by a remarkable 14%. Indeed, it superchargers least-skilled workers to complete their tasks 35% faster. Agents with 2 months of experience assisted by AI have performed just as well or better than those with half a year of experience without artificial intelligence.
Feasible use cases of the GPT-driven fintech chatbot
Users can troubleshoot issues and ask for guidance or step-by-step assistance in navigating through various app features, settings, functionalities and tools.
Examples of possible queries :
- “How do I update my personal information in the app?”
- “Can you explain how to set up recurring payments in the app?”
- “I’m having trouble logging into the app. How can I reset my password?”
- “How can I enable two-factor authentication for added security?”
Users can learn about services available through the bank and inquire about various aspects related to these services.
Examples of possible queries :
- “What types of loans does the bank offer?”
- “Does the bank provide investment services?”
- “Are there any fees for transferring money to other banks?”
- “Can I use a debit card for online purchases?”
The chatbot can provide financial education in the form of general explanations and guidance to users seeking a better understanding of various financial aspects, terms, and best practices.
Examples of possible queries :
- “What is the difference between a savings account and a checking account?”
- “Can you explain the concept of a fixed-rate loan and how it differs from a variable-rate loan?”
- “What are some common investment mistakes to avoid?”
- “What are the advantages of using a debit card?”
Direct account management
Due to pretrained commands, a user can ask the chatbot to execute specific actions within the app – initiate transactions, check balance, set spending limits, etc.
How it works?
- User Intent Understanding – the chatbot leverages natural language processing to analyze the user’s query and determine the intent behind it.
- Parameter Clarification – if the user’s query requires specific parameters to perform the desired action, the chatbot asks relevant questions to gather necessary details (e.g. account numbers, transaction amounts, dates, etc.).
- Back-end Call – once the chatbot has a clear understanding of the user’s intent and has gathered all necessary parameters, it performs a back-end call to trigger the execution of the requested action.
Examples of possible queries :
- “Show me the transaction history of my credit card.”
- “What is the balance in my savings account?”
- “Increase the daily spending limit on my card.”
- “What is the status of my recent transaction?”
- “Enable transaction alerts for purchases over $500.”
Transition to a human agent
Chatbot can seamlessly transition the conversation to the customer support team whenever it encounters a query beyond its capabilities. //in cases where it cannot handle the query.
Personal financial assistant
Chatbot can provide users with personalized responses, recommendations, and insights helping them make informed financial decisions aligned with their unique needs and objectives.
Examples of possible queries :
- What is the best deposit option so far?
- “Can you recommend a suitable budget plan based on my current financial situation?”
- “How can I optimize my credit score and improve my financial standing?”
- “Can you review my spending patterns and identify areas where I can cut costs?”
- “What investment options would be suitable for my financial goals?”
Chatbot can facilitate a seamless and efficient onboarding for both new customers and employees fostering a positive start to their journey and setting the foundation for the lasting relationship with the business.
Chatbot is able to address frequently asked questions (FAQs) promptly, and provide real-time assistance to employees asking for advice, guidance or some necessary information about company policies, services-related details, etc.
Navigating fine-tuning or how it all works
Fine-tuning approach as the best solution
While GPT has out-of-the-box knowledge of basically everything, it needs to be customized to give relevant outputs to the customers.
And this can be done through fine tuning – the process that helps to optimize the model for a specific use case or domain for better performance.
Given the evident benefits when it comes to the quality of results, fine-tuning also ensures that your product features won’t be exposed to your competitors, as the fine-tuned model becomes your private, not a public one.
Choosing the right pre-trained model
GPT-3 models: Our experience
In the process of fine-tuning GPT to fit the chatbot use case, Kindgeek developers have experimented with all GPT-3 models, exploring their strengths and weaknesses, and figuring out various nuances in practice.
Ultimately, we have decided to go with the curie model, as it is the second-best capable GPT-3 model. It has fewer parameters than davinci – 175B vs 6.7B – which means it is less creative and would require fewer data to be fine-tuned as compared to the broad and sophisticated davinci. Besides, curie is proven to be faster and indeed costs 1/10 the price of davinci per API call.
Broadened possibilities with GPT-3.5 Turbo and GPT-4
Up until July 2023, fine-tuning was only available for the original GPT-3 models.
As of now, the possibilities have remarkably broadened. OpenAI has made the fine-tuning feature possible for its most recent astonishing models – GPT-3.5 Turbo and GPT-4 – with the former being already available since Aug 22, while the latter, GPT-4, is promised to come up in fall this year.
So, whatever your AI model preference is, Kindgeek experts are capable of customizing it for your business use cases and seamlessly integrating it within your product and communication channels.
Possibilities beyond OpenAI models
If your choice lies beyond OpenAI models, Kindgeek professionals can work on your specific case and develop your smart AI chatbot with that very model at core. It only takes the availability of fine-tuning and API access for our developers to bring your specific needs to life.
Security aspect of the GPT-driven chatbot
Security is an all-time priority for any business, especially fintech.
We follow a fine-tuning approach because it ensures a sufficient security level by making the fine-tuned AI model a completely private one.
This ensures confidentiality of your information, and excludes the possibility of data leaks.
However, to grant the utmost security and compliance with essential regulations and standards, it’s crucial to employ additional safeguards and stick to the best industry practices.
Here are some of them.
Deploying multi-factor authentication (MFA) adds an extra layer of security to your product. It helps to validate the identity of the users and make a clear distinction between the capabilities of authorized and unauthorized ones.
While unauthorized users may interact with the chatbot to discover services, and learn product-related or industry-specific information, their access will remain restricted from any account management functionality.
This method ensures that access to account management is granted solely to customers who verified their identities.
No sensitive data for AI model
Users’ personal and financial data (PIN, card details, etc.) is processed completely outside of the AI model.
The chatbot here acts solely as a facilitator: it initiates the back-end call, triggers the necessary workflows within your application, so that users can input sensitive data right within your product’s API.
Data moderation layer
Moderation allows to implement mechanisms and techniques to monitor and address any possible concerns like biased or unsuitable responses generated by the chatbot.
Besides, it also helps to review and moderate any inappropriate input or sensitive data provided to the chatbot.
Employing moderation, one can ensure that all interactions within the chatbot are safe, ethical, and aligned with the values and standards of the organization it operates within.
As cyber criminals get more creative with their tactics, implementing robust end-to-end encryption is essential to ensure that user data remains protected from unauthorized access, malware or brute force attacks.
Hence, potential risks associated with data security and privacy are significantly reduced and users can leverage your product feeling safe and protected.
Exploring potential challenges and risks
Chatbot may sometimes fail to produce a correct response to the user’s query. While Generative AI models are undoubtedly impressive “word guessers,” they don’t have the ability to think and form rational judgments. As a result, they may inadvertently generate answers that are inaccurate or misleading.
Bias is the inherent problem of AI. Depending on data quality and training accuracy, the model might develop conflicting or biased viewpoints reflected in its responses.
The AI chatbot may give out a confident response that is not justified by its training data. “Plucked out of the air”, so to speak.
This phenomenon commonly happens when the AI models are confronted with ambiguous or novel queries that fall outside their training data.
Employing risk mitigation strategies
The significance of rigorous training for the AI model can’t be emphasized enough.
This iterative process involves feeding the chatbot with vast amounts of data and allowing it to learn from various interactions, which enables it to understand user inputs better and provide more accurate and relevant responses.
Proper training lays the groundwork for AI chatbot’s optimal performance, adaptability, and reliability.
Quality training data
Indeed, the overall performance is significantly influenced by the quality of the training data. By exposing the model to high-quality representative datasets, developers can fine-tune its parameters and optimize its performance, leading to higher accuracy and efficiency during inference.
Besides, with diverse datasets it is easier to mitigate bias, leading to fairer outcomes. Additionally, thorough training enhances the model’s ability to tackle complex problems effectively.
Thorough testing & model improvement
In-depth iterative testing and fine-tuning are essential to address potential issues, biases, and limitations, as well as to keep the model up-to-date with product updates and changing user needs.
Indeed, as the model is trained with more data, its accuracy shows a noticeable linear improvement.
By consistently refining the model, the chatbot can deliver an increasingly satisfying user experience.
Prospects of a more advanced chatbot version
Today, chatbots can seamlessly go beyond text-based interactions to provide more human-like and immersive experiences.
With technologies like real-time face animation and advanced text-to-speech, user experience skyrockets to a whole new level.
The chatbot becomes highly interactive, providing natural and emotionally engaging interactions complemented by lifelike facial expressions and a human-like voice to ultimately blur the lines between human and AI interaction.
Consider Kindgeek AI assistant services
At Kindgeek we offer a comprehensive suite of AI assistant services to help you seamlessly build your custom smart chatbot powered by the state-of-the-art OpenAI models and beyond.
From technical advisory services to end-to-end integration and prompt engineering, our team of experts is dedicated to meeting your unique business needs at any point along the digital transformation journey.
As banks and financial organizations seek to level up their digital game, optimize resource allocation and get more efficient, the right AI chatbot can help them do just that and even more.
Chatbots with a powerful AI model at the core, like OpenAI’s GPT, can cover a wide range of use cases, benefiting both internally and externally and ultimately driving a competitive edge.
Fine-tuning helps customize the OpenAI GPT models and similar for a specific business case, allowing for seamless integration within your fintech product and communication channels. Being secure by design, the fine-tuned GPT-powered chatbot can be additionally protected to meet the industry’s best standards and practices.
And while there are some risks to face, like incorrect responses or bias, they can be efficiently mitigated through a high-quality dataset, rigorous training, and thorough testing in between.