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Conversational Banking: Everything You Need to Know in 2024

Conversational Banking: Everything You Need to Know in 2024Conversational Banking: Everything You Need to Know in 2024

According to McKinsey, when compared to their competitors, banks with higher customer satisfaction grow deposits 85% more quickly.1 Therefore, to ensure customer satisfaction, almost 80% of banks invest in digital technologies. However, 45% of bank executives consider their customer-centric banking experience as insufficient, according to KPMG.2

In this article, we introduce conversational banking in-depth, which can help bank managers to build their desired level of customer satisfaction. Thus, we will cover:

  1. Definition of conversational banking.
  2. Benefits of conversational banking for customers and banks.
  3. Use cases of conversational banking (supported by case studies)
  4. Steps to build a conversational banking experience for your customers.

What is conversational banking?

Conversational banking, also called chat banking, is a branch of conversational commerce that aims

For example, an offline investment decision experience includes customization. To create a tailored investment portfolio, a typical bank worker should become familiar with the following personal information:

  • Risk attitude
  • Expected return in a determined duration
  • Income information of client etc.

To collect this information and necessary documentation and verification, the banker and the client communicate in a bank branch.

Conversational banking collects the same information and tries to reach the same engagement via variety of digital communication channels such as:

  • Messaging apps like WhatsApp
  • Mobile apps of banks
  • Banks’ websites
  • Email and SMS (see Figure 2).

Although live agents support conversational banking activities, due to the high amount of traffic, conversations are automated via AI chatbots, intelligent banking assistants, and voice bots. Conversational AI solutions use customer data to provide personal notifications and recommendations (see Figure 2).

Figure 2: Principles of conversational banking.

Image shows the principles of conversational banking.

Top 10 benefits of conversational banking

Chat banking can enhance customer experience in the banking industry’s operations in the following 10 ways:

Benefits for banking customers

1. Fast response the customer queries

Engaging with a live agent may take a long time, depending on the volume of traffic. However, AI chatbots can manage multiple conversations at once, which can substantially allow faster response and query resolution times.

2. Multi-Language support

We live in a global world where in many urban locations, there are people from different backgrounds. Communicating with clients in their native language makes them feel more comfortable. 

Chatbots or IVAs can respond to many languages fluently. Thus, you can determine the languages your customers speak and deploy your conversational AI solution accordingly. 

3. Personalized service

According to PwC, around 30% of clients are willing to pay more for personalized service, products, and recommendations.3

Conversational banking can ensure personalized service thanks to:

4. Flexibility

According to McKinsey, about 35% of Americans conduct omnichannel interactions, and this number is rising.4 Customers demand flexibility where they can engage with companies via channels they like. 

Thus, financial institutions should be accessible to customers on:

  • Facebook
  • WhatsApp
  • Mobile apps
  • Websites and so on.

 to ensure satisfied customers. 

Benefits for financial institutions

5. Cost reduction 

Over time, banks that invest in scalable solutions like conversational messaging systems will operate more profitably and pay less for overhead. In some cases, finance chatbots provide  90% of end-to-end automation rate. It means that customer support costs reduce due to less need for customer service employees.

6. Improve customer lifetime value

Customer journey is a continuous process. Therefore, banks should invest in strategies that sustain customer engagement, thus increasing customer lifetime value. Thanks to its benefits for clients, conversational banking improves customer satisfaction. Thus, firms can:

  • Decrease customer churn rate.
  • Increase the number of loyal customers.
  • Attract new customers easily thanks to positive word of mouth.

7. Augment workforce

Customer support representatives may find it boring to respond to frequently asked questions. Additionally, delivering answers to basic questions that customers can find with a quick website search is not a work that adds value, especially if it can be automated. Nevertheless, 75% of customers ask such questions.

You can augment your workforce in the digital age. Banks can automate customer support thanks to conversational banking. Thus, financial institutions can better allocate their workforce and improve corporate efficiency.

8. Reduce the burden of fraud

AI-driven tools effectively detect and prevent fraud. Finance companies can send customized transaction-related notifications via IVAs thanks to conversational banking. Customers can then confirm fraudulent transactions, stolen credit cards, etc., and get in touch with banks to cancel their credit cards right away or restrict the ability to transfer money out of their bank accounts.

Also, two-factor authentication messages on various channels (SMS, Mobile Apps, WhatsApp etc.) banks can ensure transactions are made by their customers.

9. Enhance data security

Another benefit of personalized notification is related to data security. Thanks to two-factor authentication messages, banks can secure the transaction information of customers before sending it to third parties.

10. Provide consistent customer service

Chatbots can transmit the same messages on different communication platforms. Thus, the sales-marketing and customer care activities of banks align. Conversational banking ensures homogeneous:

  • Wording
  • Level of formality of customer engagements
  • Font and size of characters, and so on.

Top 5 use cases of conversational banking

1. Loan assistance

Mortgage chatbots can provide loan assistance by imitating bank staff on digital platforms. According to survey findings, 60% of banking customers feel at ease interacting with chatbots or IVAs to issue loans. The loan origination process is typically streamlined by conversational AI solutions. Additionally, as users look for assistance and support around the clock, automated support boosts the customers’ journey.

In addition to being conversational agents, mortgage chatbots utilize customer data to make forecasts and predictions and produce reports that give details regarding clients’ credibility. Analysts can easily examine and evaluate these reports to find potential clients for generating loans. The below video demonstrates mortgage chatbots in action.

2. Financial advisory

Conversational banking can improve customer engagement by providing investment tips online. There are two main ways of doing this:

  1. Wealth management chatbots and IVAs can communicate with the customers and understand their
    1. Expected yield 
    2. Risk attitude 
    3. And the current financial situation offers a portfolio.
  2. Chatbots can send subscribers mass messages with industry news and institution predictions for specific stocks (see Figure 5).

Figure 5: Chatbot provides stock ideas to customers.

In the illustration, a chatbot offers financial advice to an user by proposing stocks that could yield a profit in the near future.
Source: Haptik

3. Customer onboarding

Financial institutions can automate customer onboarding processes via conversational banking. AI chatbots can collect necessary data for onboarding and verify it to qualify new customers. By providing digital onboarding service on various channels you can improve customer satisfaction.

For instance, Upstox onboarded more than 220K customers via conversational AI solutions.

4. Automated customer service

One of the common applications of conversational banking is the automation of customer service via chatbots and IVAs.

Chatbots can answer the following customer queries:

  • Regarding account balance (see Figure 6)
  • Contact information
  • Money transfer
  • Credit card cancellation and more.

Figure 6: Chatbots can provide account balance information.

Image shows a conversation example where a chatbot automate customer service.
Source: Haptik

5. Personalized notifications

Chat banking can automate sending personalized notifications concerning:

  • Personalized credit card bonus campaigns.
  • Transaction history.
  • Two-factor authentication code for secure payments (see Figure 7).
  • Due day of debt payments and so on.

Figure 7: An example of an authentication code for secure payments.

Image is an example of authentication code for secure payment.

5 steps to building a conversational banking experience

1. Identify customer journey

There are 2 main steps of the mapping customer journey:

  1. Find which digital channels your customers use. By knowing this info you can start to build data-driven conversational banking strategies. We also recommend banks to be cautious regarding generational trends. For instance, younger generations might use WhatsApp more frequently than Facebook Messenger. By considering generational factors, you can reduce your legacy costs.
  2. The second step should be knowing your customers to be aware of:
    1. FAQs customers ask
    2. Finding current pain points of your customer service
    3. Deciding which types of queries should be handled via human touch and so on.

2. Find a suitable conversational AI vendor

Due to traffic, it is often not financially possible for businesses to provide omnichannel messaging via live agents. Therefore, some of the conversations need to be automated via:

  • Chatbots
  • IVAs
  • Or in some cases, voice bots.

The problem is that few financial institutions have the IT resources necessary to develop their own conversational AI solution. For the majority of businesses, the path to conversational banking depends on finding an appropriate conversational AI vendor who creates an effective chatbot (see Figure 8).

Figure 8: General capabilities of successful chatbots:

Image shows features of well-designed chatbots.

Banks should consider the following criteria while choosing a vendor:

  • NLP and NLU proficiencies of vendors, which are fundamental to intent recognition and generate logical responses.
  • Finance-related training data set of vendors. The greater quality and quantity of the data set is associated with more intelligent conversational AI solutions.
  • Finance chatbots-related experience of vendors is another important factor you should take into consideration. You can check case studies and references of vendors to assess their finance expertise.
  • Omnichannel capabilities are another important factor banks should consider. You can ask which messaging channels candidate vendors can build conversational AI solutions.
  • More supported languages imply a better customer journey. Multilinguality is another metric you should assess while choosing a vendor.
  • Banks can evaluate the performance of their chatbots with the aid of chatbot analytics. Make sure your vendor offers such a capability.

You can read the following articles to find more information regarding choosing a conversational AI vendor.

  1. 50+ Chatbot Companies To Deploy Conversational AI.
  2. WhatsApp Business Partners: Everything You Need to Know.
  3. Conversational Commerce Platforms: Data-driven Benchmarking.

3. Use live agents

Conversational AI tools must be supported via live agents since:

  • More than 80% of users expect that chatbots will always offer a mechanism to contact a live agent.
  • With today’s technology, chatbots cannot replace humans. 
  • Chatbots fail due to:
    • Their AI capabilities are insufficient sometimes.
    • People make lots of typos, grammar mistakes and sometimes combine a few languages. Thus, the input becomes difficult for bots to understand.

As a result, AIMultiple recommends banks to combine conversational AI technologies and live operators. with chatbots handling simple consumer enquiries. On the other side, customer care agents can manage more difficult problems that require human intelligence for solution. 

4. Improve your cybersecurity posture

Data breaches can harm businesses in many ways (see Figure 9). More conversations with your customer mean more personal data banks reach. Protecting these valuable inputs is important in terms of compliance and ensuring customer trust. 

Figure 9: Possible costs of cyberattacks.

Image shows short and long term costs of data breaches.

To improve cybersecurity posture, banks can consider:

To learn more about cybersecurity best practices, you can read our Top 9 Cybersecurity Best Practices for Corporations article.

5. Collect customer feedback

To understand whether your conversational banking strategy is successful or not, you need to regularly collect feedback from your customers. To collect feedback, you can design some surveys. Their feedback provides banks with a data-driven roadmap.

  • General success of investment
  • Areas need for improvement
  • Next steps banks can start.

If you have further questions regarding conversational banking you can reach us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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