Introduction

Digital transformation has been present in the banking system for a long time, but does it help customers from every aspect? This is a question that needs to be asked. A bank is a financial organisation that depends strongly on the trust of customers. And customers rely upon a bank when it is agile and steadfast in their processes. From ensuring savings, providing loans, to running safe transactions, every banking task should be fast, smooth, and hassle-free to provide trustworthy services to customers.

With the introduction of technology in the banking organisations, the procedures have gained speed and efficiency indeed. But is that enough to build and retain people’s trust in a bank? No.

The trust comes when the banking procedures are smooth and error-free, and customers can directly talk to the officials about their queries, doubts, and suggestions anytime. In a busy industry like the banking sector, dedicating this much time to 24×7 for every customer seems an impossible goal to achieve. However, conversational AI agents are significant technological advancements that can be deployed to meet this gap. When manual efforts seem inadequate, artificial intelligence becomes the saviour.

Considering the growing significance of conversational AI in banking, it is recommended to have detailed knowledge about its work process. This blog will discuss how customer support in banking can be automated with conversational AI agents.

What is Conversational AI?

Google Assistant, Alexa, Siri — diverse instances of conversational AI are available around us. Conversational AI is a technique of artificial intelligence that enables machines to engage with people like a human being with people. It understands and responds to natural language. In other words, it talks like humans and attends to customers swiftly. By combining the work process of machine learning (ML) and natural language processing (NLP), two core elements of conversational AI, it initiates useful conversations between machines and humans.

The large volumes of data, including speech and texts, are given to conversational AI agents, which they recognise and utilise to interact with people. The AI algorithm in the techniques is constantly improved as the NLP processes flow into a consistent feedback loop with machine learning processes. By comprehending how your target users would like to interact with your organisation or respond to your product/ service, you can create conversational AI agents accordingly. Examples of it in banking include the virtual agents and advanced chatbots used by various banks.

Chatbots and Conversational AI- Are they the same?

Amidst the discussions on conversational AI, one question often comes up which is- Are chatbots and conversational AI the same? Chatbots and conversational AI agents are not the same. Conversational AI is more advanced and built with improved AI algorithms, whereas chatbots are a traditional form of conversational AI.

Chatbots are the first tools that use AI technology, but eventually, more dynamic and useful conversational AI agents steal the show. Whereas chatbots are rule-based and need human efforts, conversational AI has the capabilities of ML and NLP that minimise manual labour.

For example, if your banking transaction shows some failure, you may have to ask chatbots specific questions like- “how to solve transaction failure?” If you ask questions like “ Why can’t I proceed with my transaction?”, chatbots are likely to show no answer. But, with conversational AI agents, you can get an answer to your queries in diverse forms as they learn from given inputs and improve their capabilities.

Use Cases of Conversational AI in Banking

Beginning from the IVR with basic speech recognition technology, which helped customers navigate menu options using voice commands, the technology in banking has come a long way. Previously, the digital customer assistance in the banking sector involved only text-based chats, which, though not fully conversational AI, prepared customers to adapt to technological upgrades.

Later, with the latest emergence of conversational AI agents, the banking sector has become more trustworthy, showing a customer-oriented brand image. However, conversational AI helps to improve banking services in many ways. The major use cases are:

24/7 Customer Support
Improved Self-service facilities
Smart account management
Smooth and Fast Customer Onboarding
Hassle-free Loan processing
Automated payment reminders

How Conversational AI Becomes Beneficial in Automating Customer Support in Banking

Connecting with customers is a crucial part of banking systems, which has become easier with conversational AI agents. They automate the overall customer support system by incorporating advanced chatbots, voice-assisted applications, virtual assistants, and so on. Developed with NLP and ML technologies, these AI agents can automate answers for complex customer queries and organisational responses to their feedback. While the ML is built with a set of data, algorithms, and characteristics that improve over time, NLP helps in analysing human languages with the help of ML.

The four steps of NLP involve:

Accumulating inputs: Customers give their inputs in voice or text through the bank’s website or application.
Analysing the inputs: NLP technology involves NLU or natural language understanding to analyse the text-based inputs, whereas ASR (automatic speech recognition) and NLU are used together to analyse speech inputs.
Formulating response: At this stage, NLP takes the help of NLG or natural language generation and forms a response to the customer’s inputs.
Continuous learning: Finally, ML algorithms gather the data and ensure more accuracy and relevance in answers over time.

The basic work process of building conversational AI for your banking organisation includes:

Finding the most asked queries from your customers that show their needs and concerns
Using the queries to develop conversational AI goals, like addressing customers’ issues
Developing relevant nouns and keywords based on the goals
Accumulating all of these to create a meaningful conversation with customers

By automating customer support in banking this way, you can ensure better reliability and smoother work processes in your banking organisation.

The benefits of using conversational AI agents in customer support in banking are as follows:

Saves time and resources: By utilising conversational AI in customer support in banking, banks save time for customers and help them use their human resources wisely. Customers get prompt answers to their doubts, and you don’t have to go through the long process of appointing an expert to respond to customers’ feedback and queries.
Helps in decision making: With prompt replies to their issues, customers can make crucial decisions related to their banking transactions easily and without delay. Whether it is loan processing or account opening, they can make firm decisions for every case.
Improves reliability: With automated customer support using conversational AI, banks can ensure maximum reliability from their existing customers. Moreover, many other people can be interested in relying upon the bank, knowing about their prompt AI-driven services.
Enhances customer engagement: Unlike conventional chatbots with limited dialogues, conversational AI agents can engage customers for a long time. They understand the customers’ concerns and give suitable solutions. The enhanced engagement retains customers as well.

The Future of Conversational AI in Banking

By transforming the banking industry, conversational AI is now an integral part of most banking organisations. The tech-smart assistance offered by it improves the efficiency of the banks and their trustworthiness. However, the question about its sustainability in the future often arises. A study from Polaris Market Research can address the concern. According to the study, the market size of AI in banking was valued at $19.84 billion in 2023 and is estimated to grow to $236.70 billion by 2032 at a CAGR of 31.7 per cent. Banks have already embraced conversational AI to improve their services and scale up. Considering today’s scenario, we expect to see more involvement of generative AI and conversational AI in customer support and other banking tasks.

SquareOne: Your Trusted Partner to Use Conversational AI in Banking

Known for excellence and years of experience, SquareOne is a trusted technological partner to drive digital innovation for businesses across industries. It helps to automate the customer support of your banking system using conversational AI in the most efficient way. Providing diverse technological solutions, it aligns businesses, including banks, with the modern, dynamic business environments.

Final Thoughts

The banking organisations that are prompt to adapt to the evolving surge of AI-driven tools will indeed redefine the industry. Creating a new dimension in customer relationship management, the banks will go ahead on the path of progress. That is why it is recommended not to delay in embracing the new upgrades, like conversational AI agents, and enhancing the capabilities of the financial sector. Stay competitive in today’s digital banking landscape.

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