Why Conversational Banking is the Future of Customer Experience

Welcome to the era of conversational banking, where every interaction is a step toward a more personalized, effortless financial journey.

To illustrate this new banking paradigm, this guide delves into the core of conversational banking, its significant benefits, and real-world applications. We'll also delve into a blueprint for financial institutions aspiring to integrate this innovative model into their strategic framework.

Key takeaways

• Transformation through AI and NLP: Conversational banking, powered by AI and Natural Language Processing (NLP), enables interactive, real-time communication between banks and customers, thereby improving customer engagement and operational efficiency.
• Practical implementations: Real-world applications of conversational banking encompass the streamlining of loan processes and the automation of account services. These actions not only simplify banking procedures but also foster a more intuitive, user-friendly banking experience.

What is conversational banking?

Conversational banking represents a contemporary banking approach that enables interactive communication between banks and customers via digital channels, using natural, conversational language.

The emergence of this model is grounded in the digital acceleration reshaping the financial industry. The ascent of conversational banking is primarily driven by the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies into customer-facing systems.

Traditional methods of client engagement, often perceived as inflexible and time-consuming, fall behind AI-powered solutions that offer unprecedented ease of use and speed.

The core objectives of conversational banking encompass:

• Enhancing customer engagement through real-time, personalized interactions
• Automating routine banking tasks to enhance operational efficiency
• Providing customers with a seamless banking experience across diverse digital platforms

A concrete example of conversational banking in practice can be observed through the deployment of a virtual assistant named Ceba by the Commonwealth Bank of Australia. Fueled by AI, Ceba has the capability to manage more than 200 banking tasks. This includes tasks such as checking account balances and facilitating fund transfers.

Through interactions with Ceba, customers can complete a wide range of banking transactions without the necessity of visiting a physical branch or navigating through intricate online banking menus.

The role of AI and NLP in conversational banking

Natural Language Processing is a branch of artificial intelligence that empowers machines to comprehend, interpret, and generate human language. While AI provides the foundational algorithms and computational capability, NLP contributes a nuanced understanding of human language.

In conversational banking, AI serves as the foundation for creating intelligent chatbots and virtual assistants capable of handling a wide range of banking tasks. NLP, on the other hand, enhances these interactions by enabling these systems to understand queries and respond to customers using natural, human-like language.

A practical instance of NLP's role in conversational banking is demonstrated by Bank of America's virtual assistant, Erica. Erica utilizes NLP to grasp conversational inquiries. With this technology, customers can inquire about their account balances, schedule appointments, and receive financial advice as if Erica were a genuine relationship manager at Bank of America.

Benefits of conversational banking

Here are the four key advantages of this innovative banking approach:

1. Operational efficiency and cost reduction

Conversational banking technologies have the capability to automate the management of routine inquiries, which previously necessitated human intervention. The integration of voice bots and chatbots into contact center operations is resulting in enhanced efficiency by reducing call waiting times and service bottlenecks.

According to McKinsey, chatbots utilizing generative AI can potentially decrease the volume of human-assisted contacts by up to 50%, with the extent of reduction varying depending on the company's level of automation.

Additionally, this technology is also finding its way into the handling of payments and claims through payment bots and Robotic Process Automation (RPA), leading to a significant reduction in back-office and operational costs.

2. Enhanced customer engagement and personalization

Conversational banking goes beyond the initial chatbot capabilities that primarily addressed simple inquiries. Allowing customers to engage with bank systems in a natural, conversational manner eliminates the barriers often associated with traditional banking interfaces.

A noteworthy example is the implementation of Cyberbank Konecta by the US-based fintech company Galileo. One standout feature of Cyberbank Konecta is its capacity to create automated conversation summaries based on interactions between the financial institution and its customers.

Through the analysis of these summaries, Galileo's systems gain a better understanding of each customer's requirements, preferences, and challenges. This, in turn, enables them to customize offerings and responses more precisely to individual customer needs.

4. Streamlined loan processes

At the core of loan processing lie data collection and verification, and conversational banking automates these processes by gathering essential information through natural language interactions and swiftly verifying the data.

As applicants progress through the loan application, they often require guidance. Conversational AI offers real-time assistance, addressing queries and providing necessary guidance, thus expediting the process. This real-time support extends to credit assessment by analyzing the applicant's financial data, resulting in quicker pre-approvals and an accelerated loan processing timeline.

Furthermore, this technology streamlines document submission and enables applicants to track their application status in real-time, enhancing transparency and reducing the anxiety associated with loan approvals.

Conversational banking tools can also be utilized to interact with bank employees. For instance, Lendesk Technologies has introduced the "Lender Spotlight AI Assistant" to assist Canadian mortgage professionals in navigating a wide array of mortgage options, numbering over 7,000. This tool helps them save time and effort on research and policy analysis when identifying suitable products for their clients.

This "mortgage chatbot" provides immediate and accurate responses to various queries from mortgage professionals, empowering them to concentrate on building relationships and offering expert advice to their clients.

5. Automated account services

The automation of account services through conversational banking can greatly enhance the convenience and efficiency of banking transactions. Here's how:

• Account inquiry and management: Customers can easily access information about their account balances, recent transactions, and monthly statements by using a conversational AI interface.
• Transaction initiation: Conversational banking empowers customers to initiate transactions, like fund transfers or bill payments. Customers can simply express their intent and provide necessary details when prompted by the AI.
• Fraud alerts and dispute handling: In the event of suspicious activity on a customer's account, conversational AI can send real-time alerts and assist customers in taking necessary actions, such as blocking a card or disputing a transaction.
• Subscription and service management: Customers can efficiently manage their subscriptions, loan repayments, and other banking services by interacting with conversational AI, allowing for changes and receiving instant confirmations.
• Scheduled updates: Conversational banking can provide scheduled updates on account activities, loan repayment due dates, and other essential information, helping customers stay well-informed and manage their finances effectively.

An excellent illustration of these capabilities is the previously mentioned Bank of America's virtual assistant, Erica, which assists customers with a variety of tasks, including checking account balances, scheduling bill payments, and providing credit report updates.

In fact, Erica (as of July 2023), in its 5 years of implementation, has already surpassed 1.5 billion client interactions, logged more than 10 million hours of conversations, and helped over 37 million Bank of America clients manage their finances.

How to build your conversational banking strategy

Embarking on the path of conversational banking is a strategic undertaking that requires a methodical approach. The following steps provide a structured framework for developing a conversational banking strategy:

Map the customer journey

The foundation of a robust conversational banking strategy begins with the mapping of the customer journey. This entails outlining the various interactions a customer has with the bank, from the initial consideration of a service to its utilization. Each touchpoint represents an opportunity for the integration of conversational banking to enhance satisfaction and efficiency.

For instance, consider a customer inquiring about a home loan. Initially, they seek information regarding loan amounts, interest rates, and eligibility. A chatbot can swiftly provide answers, directing them to pertinent resources.

As they progress to the application stage, conversational AI can assist in form completion by requesting necessary details in a conversational manner, simplifying complex forms. Following submission, a conversational interface can provide real-time status updates.

This understanding guides the allocation of resources for the development of conversational solutions in specific parts of the customer journey that require focus.

Select the right conversational AI vendor

Selecting a proficient conversational AI vendor is essential for the successful implementation of conversational banking solutions. A reputable vendor should provide a conversational AI platform with the ability to comprehend complex banking queries and provide accurate responses as well as offer the highest security standards to protect customer data.

Furthermore, the vendor's capacity to seamlessly integrate with the bank's existing systems is critical to prevent operational disruptions. For instance, the AI platform should be capable of connecting with the bank's CRM system to offer a unified view of customer interactions.

Lastly, the ideal vendor should offer regular updates and refinements to the AI tools based on evolving customer needs and feedback. Given that these are emerging technologies, these solutions should not only meet current customer needs but also be adaptable to address future demands.

Another choice is to develop in-house conversational AI tools. This approach allows your organization to have increased flexibility and ownership over building AI customized products. Your team can be bolstered with team extensions to enhance necessary skills.

Balance human and AI interaction

While AI-powered technologies excel at swiftly addressing routine inquiries, specific situations necessitate the empathy and nuanced understanding of a human representative.

Consider a customer facing a financial crisis, seeking to understand their loan options. An AI can promptly provide information on loan types, rates, and eligibility. However, when the customer requires advice or emotional support, transitioning to a human agent becomes crucial.

Implementing a seamless handover protocol, where the human agent is briefed on the conversation history, enables a smooth transition and prevents the customer from having to repeat information.

Additionally, a hybrid support model can be adopted, where AI handles initial interactions and, depending on the complexity of the issue, either resolves the query or escalates it to a human representative. This model not only enhances efficiency but also maintains a human touch.

Therefore, the fusion of AI with human interaction in conversational banking optimizes resource allocation and ensures that customers feel heard and valued. This is indispensable for building long-term customer relationships and enhancing overall satisfaction.

Build robust security and privacy measures

Security and privacy are of the utmost importance in conversational banking, especially given the sensitive nature of financial information. Implementing robust measures to ensure data integrity and confidentiality is an absolute necessity.

For example, consider a situation where a customer initiates a transaction through a chatbot. The system must employ strong authentication methods, such as multi-factor authentication, to verify the user's identity before proceeding.

Additionally, encryption should be in place to secure data transmission between the user and the bank's systems.

While many of these security and privacy principles and systems are not new to financial institutions, it's essential to acknowledge that, given the novelty of conversational AI tools as a banking channel, firms must be vigilant about identifying and addressing new forms of threats and risks specific to conversational banking.

Measure performance of conversational AI tools

Evaluating the performance of conversational AI tools is crucial to ensure they meet their intended objectives and continue to provide value over time. Here's how a bank could approach this:

• User engagement metrics: Monitor how frequently and in what ways customers engage with the conversational tools. Metrics such as the number of interactions, duration of interactions, and the range of queries handled can offer insights into the tool's effectiveness and areas for improvement.
• Response success rate: Evaluate the accuracy and relevance of responses provided by the AI tool. A high success rate in accurately responding to customer inquiries indicates effectiveness, while recurrent misinterpretations signal a need for refinement.
• Customer feedback: Collect customer feedback, specifically focusing on their experiences with the conversational AI tool. This could encompass ease of use, satisfaction with the responses received, and any challenges faced.
• Resolution time: Measure the time taken to resolve queries. A shorter resolution time, without compromising accuracy, reflects efficiency.
• Cost savings: Analyze the operational cost savings resulting from automating customer interactions, including reduced call center volumes and human intervention.
• Learning and adaptation: Assess how well the AI tool learns from interactions and improves over time. This could be measured by a decrease in misinterpretations and an increase in successful query resolutions.

The future for conversational banking

Conversational banking stands as a beacon of the symbiotic relationship between technology and human-centric service. It's an invitation to financial institutions to not just keep up with the times but to lead the way in redefining what a modern banking experience should entail.

Through conversational banking solutions, the future of customer experience in the financial sector isn’t just promising. It's exhilarating.