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Date: April 11, 2024

By 2020, 85% of banks and businesses will be interacting with customers through chatbots

Since the advent of information technology, we’ve been trying to eliminate or significantly reduce human involvement to speed up service delivery and increase productivity. The rapid evolution of cloud and cellular technologies like 4G and 5G technology have enabled streamlined automation of processes and workflows, which affected nearly every niche and verticals. 

FinTech is one of those domains that has benefited most from digital transformation and breakthrough innovation enabled by progressive technology and new-gen communication protocols and standards. 

Chatbots seem to have finally allowed us to eliminate human involvement and automate many things from conversations with users to market research and surveys to IT system management to the helpdesk to recruitment. 

When it comes to FinTech, online chatbots, especially those based on artificial intelligence (AI) and machine learning algorithms, prove to be one of the most effective ways that banks and financial service companies can use to increase their efficiency on a large scale along with conversational UX design.

According to Gartner, by 2020, 85% of banks and businesses will be interacting with customers through chatbots. Juniper research also shows that chatbots can help save billions of work hours and about $8 billion through automation and the implementation of conversational tools. 

However, as in any traditional sector, resistance to change is enormous in the financial industry. Many banks and finance companies still do not support this kind of innovation and digital transformation as a result of resistance from the aging C-level execs and managers who use proven yet old-school and obsolete approaches in their work. 

As the latest research suggests, failure to implement innovative tools like AI chatbots within finance organizations will lead companies to lose up to 35% of their revenue, while AI and chatbot pioneers will most likely be able to increase revenue by 45% and more.

As relationship banking (i.e., banking services based on close relationships with clients) gains traction and becomes the new norm in FinTech, AI is an extremely powerful tool that can help banks build rapport with clients, improve loyalty and establish a long-term relationship with clients based on trust, convenience, engagement and of course conversational User Interface.

While AI (Artificial Intelligence) chatbots help companies handle their day-to-day routine and build relationships with clients, human beings can be used for more strategic tasks that require outside-the-box thinking, creativity, and a sharp mind. Alternatively, people can be involved in making chatbots even more intelligent and better tailored to each particular client’s needs.

Let's take a closer look at the five ways AI chatbots empower FinTech and can improve customer service in the traditional finance and banking industry.

Major Advancements in Personal Banking

The two biggest issues facing the majority of bank customers today are service delays and lack or poor quality of personalization. Now that we have chatbots that have become more and more intelligent every year with conversational interface design, personal banking can be significantly improved.

By reducing waiting time, the bank can get rid of long lines in departments and help customers get personalized services faster, which will allow for time and money-saving alike. 

Powered by machine learning and NLP technologies, chatbots can help with the provision of information regarding the current terms and conditions of various services, implement  KYC and AML compliance procedures, and quickly resolve customer queries. 

The most valuable thing is that chatbots allow banks to solve requests in very short timeframes without any human intervention. Moreover, customers will not even feel that they are interacting with the robot because the experience can be as realistic as communicating with a person!

“AI could bring increased breadth, scale, and frequency to holistic KYC reviews in a way that better integrates ongoing screening and monitoring analysis. Risk and detection models will assess and learn from a richer set of inputs and produce outcomes in the context of both the customer’s profile and behavior. By leveraging AI’s dynamic learning capability coupled with skilled investigators, this model could be used to augment operations, provide quality control, and even be used to train new resources.” Scott Zoldi, chief analytics officer at Fair Isaac Corp.

The video below is an example of a KYC chatbot for banks!

Customer Service Automation -

In addition to being extremely useful in providing customer services, chatbots also help with data analysis, fraud detection, and data collection. Because conversational bots are highly automated, users are notified of each transaction. This helps prevent fraud by identifying possible discrepancies at the earliest stage with user experience design. 

Users are always aware of the events that are happening to their bank account. Bots can help customers at any time because they are trained to understand their needs and offer the best possible solution, while at the same time giving them the feeling that they are interacting with a human operator by using a better user experience strategy. 

Chatbots also help banks respond to any customer complaint by analyzing customer feedback and tone of voice in their sentiments and providing important information instantaneously. 

Bank of America launched their chatbot Erica (the word is derived from the word “America”) two years ago. It’s currently used to provide account balance information, updates on credit reports, make suggestions on how to save money, send various notifications to customers, pay bills, and help customers with transactions. It's a virtual assistant that helps customers make the right choices.

Feedback management

Customer feedback is one of the most essential elements of any banking service. With intranet-based chatbots, banks can get more specific feedback from customers that can help them improve their services. Employees can get information about shortcomings, and management can offer useful solutions. 

HSBC Hong Kong built and introduced an AI-based customer servicing platform called Amy. It provides instant customer support on a 24/7 basis and covers a lot of product pages. Amy is fitted with an embedded customer feedback mechanism that enables timely feedback collection, processing, and interpretation, thanks to machine learning and NLP. Amy is integrated with the bank’s live chat to enable fast human intervention for inquiries that can’t be processed and responded by Amy due to technology limitations.

Employee Productivity 

Activities such as access to personal data and payroll information, requesting vacation or sick leave, updating contact information, performing detailed scheduling reviews, and others can now be executed by AI chatbots. This helps employees increase their productivity during working hours and use that time more efficiently.

In Sweden, SEB released two AI chatbots: Aida for customers and Amelia for bank employees. In the first three weeks upon the launch, Amelia had processed more than 4,000 conversations with 700 employees and solved most of the issues immediately without delays. 

The automation of employee help desk and ITSM at SEB has improved internal employee satisfaction as bank operators can focus on higher-value tasks and projects. To date, Amelia has achieved a 90% accuracy rate in understanding and completing tasks. 

In 2017, one of 8allocate’s FinTech client’s loan portfolio grew 10-15% every month, and the company eventually found itself in a so-called “growth trap” when it lacked internal resources to process the constantly growing number of incoming user requests as well as inquiries. 

The company’s only solution was to inflate the staff proportionally, which affected business profitability and efficiency. After a certain threshold, the service began to lag behind, as call center operators did not have enough time to process all incoming requests with the appropriate quality level. That is, the company was forced to constantly “put out the fire” instead of solving client issues systematically.

In response to this crisis, they were going to use a traditional approach and hire a dozen new call center operators and a team of lawyers to work with an ever-growing number of client requests.

However, after indepth cost and resources analysis, they made a decision of not investing in new hires but invest in software development instead. As such, they built and deployed an AI-based chatbot which responded to more than 3,500 customer requests within the first 30 days. This allowed the company to save 450 man-hours a month and save a lot of paperless communication.

The Future of Chatbots in FinTech

Despite all the hype around them, the vast majority of chatbots available in FinTech today are still largely limited in functionality and badly need to improve their understanding of conversational interactions, the ability to derive and analyze customer or employee insights, provide recommendations based on user behavior and habits, and more. 

Also, due to the lack of internal expertise and sufficient budgets, some banks build and deploy pretty basic chatbots by nature that can only fulfill limited tasks and can’t properly handle the requests that require knowledge outside the functional domain.

As AI tech becomes more intelligent over time, so will chatbots. They provide an excellent opportunity for banks and financial services organizations to differentiate user experience by applying insights for advanced advice and recommendations and contextual decision making. 

According to PwC, chatbots will have the following use cases across the FinTech industry:

• Facial recognition for zero-click transactions;
• Visual demonstrations of benefits and advantages (e.g., the impact of long-term savings);
• Real-time status updates on cross-border blockchain transactions;
• Using IoT devices to increase customer conversion through voice assistance.

At the heart of all these advances is the ability of AI to collect insights and use advanced data analytics to foster informed decision making and benefit the customer.

Now, with that being said, what other thought-provoking future use cases of conversational AI in FinTech can you add? Also, in case you are looking to get your own chatbot app created then our suggestion would be to hire the best chatbot app development company in the industry. 

Apart from this, if you are interested in reading more such updates regarding the mobile app industry, then make sure to stay tuned with MobileAppDaily!