How AI Chatbots in Finance are Reshaping Customer Service

AI chatbots in finance, insurance companies and banks have established themselves as the best tool to add value to the user experience and cut management costs.

In a previous article, we talked about the different use cases of chatbots in the BFSI industry: from financial assistants to fraud prevention and internal operations management. AI chatbots are spreading across all departments; however, where they’re mostly used is in customer support.

This is no surprise.

Customer support and satisfaction are fundamental to the success of a business, no matter what product or service it offers. In fact, according to a research study, one-third of consumers say they would consider switching companies after a single case of poor customer service.

However, support centers are also among the most expensive services for a company, especially if they’re not efficient. If we consider the number of employees and resources needed in a call center to serve users from different time zones, languages and channels, the cost is significant.

In this article, we’ll discuss how AI chatbots in finance are reshaping the customer service experience, reducing operational costs and increasing internal productivity.

AI Chatbots in Finance Significantly Reduce Business Expenses

One of the main advantages of a chatbot is that, unlike live agents, they can provide support 24/7, 365 days a year, anywhere in the world. A chatbot can answer a query or resolve common issues immediately; whether it’s resetting a password, managing transactions or finding the nearest open office.

This means that many more requests can be handled at the same time, reducing user waiting times and cutting problem resolution time, saving up to 4 minutes per chatbot enquiry compared to traditional call centers.

For the company, this translates into significant cost savings. According to a Juniper study, companies that implement conversational solutions such as virtual agents and chatbots in their call centers, will save over $8 billion per annum by 2022.

Source: Juniper Research

Optimize Internal Resources with AI Chatbots

While some may think that the use of this technology in customer service will leave many people out of work, what we are seeing is that rather than taking over jobs, chatbots and humans will work together to provide an even higher user experience.

In general, 80% of call center requests don’t require specialized knowledge. By implementing AI chatbots in finance to handle routine requests, such as balance requests or payment information, call center employees can focus on more challenging and high-level tasks that require a higher degree of expertise. The live agent can always intervene if the situation requires it and work together with the virtual assistant to resolve the query as quickly as possible.

Source: Accenture

AI Chatbots are the Innovation Financial Companies Need

Reducing internal costs and increasing productivity are not the only business benefits a financial company should aim for. This technology makes it possible to stand out from the competition and generate an image of an innovative and customer-focused company.

To achieve this goal, choosing the right conversational AI technology is critical. There are hundreds of chatbot development tools available, but few meet the demands of financial organizations.

The focus should therefore be set not on a simple bot, but on a conversational AI chatbot, like the Teneo-based solution.

Unlike most bots, based primarily on machine learning, Teneo’s conversational AI chatbots are capable of understanding a customer’s intent, no matter how the question is phrased, and perform complex operations without the intervention of a support agent. From checking an account to making payments or managing a refund.

Moreover, they can integrate with external providers or backend software, such as CRM or RPA solutions, to perform even more complex tasks.

Teneo also gives full access to conversational data, a gold mine of valuable insight about what your customers think and how they feel when interacting with your brand. This is particularly useful in customer service applications, that allow you to analyze the user sentiment towards the company, the products and services, as well as towards the conversational chatbot itself.

This data is crucial for analyzing your solution and understanding how to optimize it.

Main KPIs to Ensure your AI Chatbot is Performing

Launching your conversational chatbot is only part of the process. How do you make sure your AI chatbot solution is working properly and meets your business needs?

To evaluate the success of your financial chatbot in your overall customer service strategy, we have listed the main Key Performance Indicators (KPIs) to keep in mind when analyzing your chatbot performance.

Retention Rate

The retention rate refers to the percentage of users who have consulted the chatbot repeatedly during a given period. By confirming that your customers continue to use this channel, you ensure that they find value in the automated interaction, as it can be compared to the typical frequency of customer contacts in traditional channels.

This KPI can indicate whether your current level of investment in the technology is sustainable and show its level of acceptance among your customers, or whether some aspects of the technology need to be adjusted or refined to improve the experience.

Containment Rate

This metric refers to the number of queries that have been successfully resolved and handled by the Digital Assistant. In other words, it answers the question: “Did my chatbot eliminate the need for a conversation with a call center agent?”.

If there is a high number of dialogues that are handed over to a human agent, then our user is not really getting value from the conversational solution. This is one of the key metrics to evaluate and prove the business benefit of your conversational AI solution.

Customer Satisfaction

Above all, companies using customer service chatbots must measure the impact of this tool on customer satisfaction. It is not always clear what customers think and feel about their interactions with chatbots, so assessing customer satisfaction is a must.

One way to achieve this is by tracking metrics such as the Net Promoter Score (NPS), a customer loyalty metric that measures the likelihood that our customers will recommend the brand to other users. Or by measuring users’ responses to an exit response. For example, the chatbot can ask “Did I get all your questions answered today?” The users’ answers will help you track the chatbot’s performance.

The impact of AI Chatbots in the Finance industry is undeniable and we can already see an increasing number of companies benefiting from the implementation of this technology in their business strategy.

It’s no longer a question of if conversational AI will begin to change the market landscape, but when.

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