AI chatbots have been growing across industries for various use cases, such as sales and support. Gartner predicts that chatbots will be a primary medium to service customers for 25% of all businesses by 2027. Banking businesses are joining this list of beneficiaries where AI-powered chatbots can add plenty of value for customers and companies.

Traditional banking can be tiresome, especially for customers when they are approaching customer service for help or support. However, AI and Natural Language Processing (NLP) represent another digital transformation in the form of AI-powered chatbots for banking. In this blog, let’s dive deeper into:

  • What are banking chatbots?
  • What are the biggest pain points in the banking industry?
  • Why do you need AI chatbots anyway?
  • What are the benefits of AI chatbots in banking? 
  • What are the challenges of using AI chatbots in banking?

What are banking chatbots?

AI banking chatbots can be virtual banking assistants for customers to reach out and share their queries. With NLP and a well-trained ML model, these banking chatbots will be able to support customers accurately, instantly, and at any time of the day. Unlike traditional chatbots, AI chatbots don’t just answer user queries based on limited rules.

The AI chatbots can understand context, customer history, and sentiments to drive the discussion in a natural and hospitable manner. AI chatbots can fast-forward digital transformation into banking. Customers could just go ahead and ask about transaction history, current bank balance, outstanding loan balance, and so much more to get the right information in no time.

What are the biggest pain points in the banking industry?

There are a set of areas where the banking industry has to improvise. Here are some key pain points:

  1. Delayed & tedious query resolution

Conventional banking customer support through a call, creating tickets, or with a rule-based chatbot can be tedious, frustrating, and time-consuming. Any customer complaint toppled with a poor response can further ruin the customer experience.

  1. Poor data management

Often, dealing with customer queries can come with complications, even for a human support/sales executive. This is due to different fragments of customer queries that can be laid out through their previous inquiries and history. An AI-based chatbot can be completely aligned with the past history to present the desired response to users.  

  1. Lack of productivity

Conventional query resolution is not as productive. If it is through a rule-based chatbot, a significant amount of unique queries are unanswered and directed to human representatives. If it is through direct human support assistance, it is a waste of human resources.

  1. Higher operational costs

Operational costs of underutilized human resources can be burdensome for banking companies. Relying on manual support for activities that are repetitive can actually result in unnecessary operational expenses.

  1. Purchase abandonment rates

Just like e-commerce, the banking industry also suffers from purchase abandonment from its customers. Customers would often explore and consider buying a banking product but may not follow through with the purchase due to poorer engagement and clarity about the policies or terms. 

  1. Difficulty finding accurate information

Often, customers find it hard to find consistent and reliable information about particular policies, terms, and product comparisons. Through old web pages to untrained sales staff, they can all lead to a direct loss of revenue because of the ambiguity. 

  1. Limited capacity to handle customers

Depending on manual representatives limits the bank’s ability to answer numerous customer queries at a time. A solution that is as efficient as manual assistance but is also scalable is necessary for users and companies.

Why do you need AI banking chatbots anyway?

Limitations of traditional banking operations create a need for a better, smarter, and more responsive solution. Here are five reasons why we need AI chatbots in banking and finance.

  1. To increase sales conversion:

99% of business respondents confirmed in a survey that having a chatbot can increase sales conversions for businesses and generate more revenue. With AI and NLP for customer engagement, banking chatbots can cut down abandonment rates and boost revenue for the company.

Having an AI chatbot to entertain incoming websites and converse with them about your banking products can increase the revenue for banking companies, that too without any additional marketing or sales overhead costs.

  1. To increase customer engagement & retention:

Customer engagement is a building block of brand identity for any growing banking company. Better customer engagement and satisfaction can help banking companies retain their business. As per 64% of customers, 24x7 availability is the most helpful aspect of chatbots.

Customers knowing that their queries will be answered and resolved at any time of the day can go a long way. While there are competitor companies in the market, being answerable to customers would build trust and loyalty, which is exceptionally crucial for the banking sector.

  1. To increase productivity while reducing operational costs

In terms of performance, AI chatbots outclass rule-based chatbots by far. As such, companies must equip customers with the best solution. AI chatbots can also provide quicker resolutions to customers in contrast to manual support. While more human, manual support comes with much higher operational expenses for banking companies.

While AI chatbots can come with a list of advantages, these three are the fundamental reasons why AI chatbots are growing like wildfire in the banking industry.

What are the benefits of AI chatbots in banking? 

AI chatbots can help advance the banking domain into its various operations. From sales to support, there are several benefits of leveraging AI chatbots, including:

  1. Effective query resolution:

AI chatbots are better than traditional rule-based chatbots in terms of understanding the context. Moreover, through ML models, these chatbots can even consider past user history and queries to provide relevant feedback that matches the user’s intent the most.

Through AI chatbots, customers can find answers to all their queries in one chat window, as the ML model would be able to process all the available data from the company and provide the right answers instantly. This ensures that customers are not left wandering on the website to find bits of information.

  1. Quick and on-demand support:

Often, there is a long queue if a customer is in need of a manual support executive. Moreover, the support executives are often unavailable after business hours, which makes it hard for the customers to perform banking actions during their free time, 24x7 for 365 days a year - that’s where banking chatbots help.

  1. Consistent and reliable answers:

AI chatbots can provide consistent and reliable answers to users who might have been confused by old information on the website or are misguided by untrained support executives. In such cases, one can vouch for AI chatbots to provide the most updated and confirmed responses.

  1. Human-like conversations:

Natural Language Processing (NLP) and Natural Language Understanding (NLU) allow AI chatbots to drive human-like conversations through a chatbot. Such engaging communication makes AI chatbots usable among customers and would still provide quick resolution without any human dependencies.

  1. Personalized recommendation engine:

AI recommendation engines can learn through users’ preferences and purchase history. Such a relevance and contextual background allows AI chatbots to present any user with personalized recommendations about banking products and solutions.

  1. AI chatbots & automation:

AI chatbots can perform seamlessly in rigorous, recurring, and repetitive tasks. Through integrations and automation in workflows, AI banking chatbots can automatically generate tickets, email transaction history, share a loan repayment schedule, and perform many such actions.

  1. Data collections and insights:

AI chatbots can help collect data about customer queries, demographics, and dropoff points. Such insights about customer behavior can help banking businesses find loopholes in their processes and improve operations where necessary.

What are the challenges of using AI chatbots in banking?

However, just like with any new trend and innovation, there are several gray areas and challenges about the use of AI chatbots that any banking company must account for. Some of these challenges include:

  1. Limitations of ML models:

AI chatbots function as per the referenced data sets and trained machine learning models. Limitations of ML models are, accordingly, the limitations of the AI chatbot. Complex queries, new scenarios, and paradoxes are scenarios where AI chatbots can fall short.

  1. Security measures & data protection:

For banking, AI chatbots have to find a fine line when it comes to sharing confidential and sensitive information. A poorly trained ML model without privacy and security measures might not be ready to help customers.

  1. Gray areas & incorrect commitments:

If the user is not providing accurate prompts, it can hamper the AI chatbot's abilities to understand the query and provide an effective resolution. More importantly, incomplete or ambiguous prompts can lead AI models to make erroneous commitments, creating confusion and dissatisfaction.

Any such novel solution, like AI banking chatbots, must present several challenges. However, with proper due diligence and expertise, AI banking chatbots can yield transformational output for companies.

If you are planning to leverage AI in the banking sector, then we would be happy to help you out. Alltius has helped financial institutions increase their sales by 300% and reduce customer support costs by 50% within weeks of going live. Our AI expert team will help you set up the AI assistant and guide you through the way. Get on a free consultation call to see how you can transform your banking customer experience with gen AI today. 

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