Comprehensive Guide on How to Build a Bot for Banking: Types, Use Cases, and Steps to Take

These days we live in a fast-paced world where individuals use mobile technologies and gadgets to solve issues, hold meetings, or communicate with their financial managers on the run.

Big tech companies — Google, Apple, Facebook, Amazon, and Microsoft (GAFAM) — have set the bar high, so now, customers expect a myriad of services and products to be available around-the-clock, all while ensuring exclusive user experiences and immediate omnichannel support.

Financial service providers and banks often have a large number of customers, and offering an individual approach to each client at scale can be challenging. Without cognitive technologies and automation, it’s nearly impossible to meet those demanding standards and keep your leading positions.

The State of Conversational Banking and Importance of NLP Chatbot Services

Even though chat bots using NLP (natural language processing) technology are still maturing, they have already proved their relevance. As the more tech-savvy and demanding generations evolve, so do the applications and opportunities for bots in banking.

Currently, virtual assistants combined with artificial intelligence (AI), NLP, and machine learning (ML) technologies can mimic human conversations to help financial companies fill the communication gaps between the business and its audience, as well as set up additional communication channels. By 2022, banks and other financial institutions can automate around 90 percent of client relations using conversational AI bots and so save more than $8 billion per year.

All these estimates are not so far-fetched as it may seem on the surface. Conversational AI in banking and financial services is already embraced by the Bank of America, HSBC, MasterCard, HDFC, and many others. Consequently, the value of the chatbot market is projected to hit $7.59 billion by 2024.

Types of Bots in the Financial Services Sector

Depending on the way how a bot is programmed, we can distinguish two main categories of bots for financial services: simple (rule-based) and advanced (AI-driven).

Rule-based bots

This category of assistants is also called ‘scripted’ since they rely on particular keywords and commands. Developers build a decision tree hierarchy with a list of queries and relevant answers that the system utilizes while communicating with your customers. However, if a user asks a question without a single trigger, it will respond: “Sorry, I don’t understand,” and so affect user experience.

AI-based bots

Talking to users, these smart assistants mainly depend on artificial intelligence, but can also leverage machine learning or NLP applications. ML and AI-driven chatbots learn autonomously based on the provided data and previous interactions. NLP in chatbot development is embraced to enable the system to analyze, comprehend, and prioritize queries based on their complexity or intent.

How Conversational Chatbots Contribute to the Financial Business Growth

There’s no doubt that intelligent assistants have come a long way since the very first launch in 1966, and they have proven successful in healthcare, e-commerce, travel, and education industries. Meanwhile, opportunities for bots in financial services are nearly endless, and we believe that use cases existing today are just the beginning of a long-lasting and rewarding engagement. Today, analyzing the financial services sector, we can identify the following chatbot use cases as the most efficient ones:

• Twenty-four-seven customer support. Communication with clients is the initial and still the major area of application. Individuals no longer need to wait for business hours to visit a brick-and-mortar branch and get assistance from a banker. Conversational interface chatbots are always available, and they can effectively deal with a diverse range of inquiries, which are often repetitive in nature. Today customer services chatbots can be found at N26, Wells Fargo, Capital One, USAA, HSBC, SEB, and more.
• Lead generation and selling. With the use of artificial intelligence and machine learning technologies, advanced bots can not only answer generic queries but also identify potential customers, pitch banking services and products, make cross-selling, or manage subscriptions. This type of financial services bots can act as a part of the sales team and pass qualified leads to human employees for further discussion. Mastercard and American Express widely adopt this strategy.
• Proactive advice. The application of intelligent personal assistants is booming. They can track users’ spending habits and identify patterns, help to draw up and manage budgets, share exclusive budget planning tips, or even investment advice. Moreover, banking chat bots send bill reminders and balance alerts. Financial advisor bots have already proved their efficiency for the Bank of America (Erica bot), Ally Bank, American Express, Commonwealth Bank, and others.
• Prevention of fraud. These days fraudulent activity remains one of the biggest problems in the financial service sector. One of the latest examples is the situation with Equifax in 2019 when they had to pay over $575 million for failing to secure their network. Bots can help banks to mitigate risks by designing behavioral patterns and analyzing all online transactions. If any atypical behavior or fraudulent action is detected, the system blocks the process and notifies customers and/or human employees and thus initiates a further investigation. One of the functions of fintech chatbot at Santander UK is to de-risk clients’ funds and data.
• Back-office optimization. The facilitation of tedious and low-value procedures within the organization is among the most anticipated AI banking bot projects so far. JPMorgan Chase is among the pioneers, and currently, it applies smart assistants to solve corporate IT inquiries, including but not limited to password resets for employees. Chatbots are expected to process around 1.7 million requests per annum.

These are just a few of the most promising chatbot examples in the banking sector, and you can also meet AI in customer onboarding, feedback management, authentication, and other areas.

How to Create a Bot for Your Financial Business and Make It Thrive

In simple terms, chatbots are software programs built to promote communication; thus, you can make them as smart as your business needs them to be. It can be a simple rule-based tool or an intelligent assistant depending on the desired use cases and strategic objectives. So, how to build a bot for your financial business? Regardless of the selected type, you should consider the following elements of the chatbot development process:

• Purposes and channels. Bots can be added to one or multiple platforms; meanwhile, your mobile banking apps and websites are the immediate ones. You can also integrate them with instant messaging solutions, depending on your customers’ preferences, behavior, and the outlined strategy. It’d be better not to fix on textual communication only since, with the rise of deep learning, voice-based chatbots are gaining momentum.
• Interface and UI. Ease of use is one of the key features and something that customers expect to get by default. Inherently, chatting is more convenient than calling, and this is the main reason why messengers have been so pervasive over the last decade or two. Consequently, when you decide to build a chatbot, keep its interface similar to messaging applications, to facilitate the onboarding process, and help individuals quickly make a connection. In this way, you’ll mimic human-to-human interaction and thus give a feeling of personalized service.
• Data security. The feeling of safety is critical when it comes to financial assets and banking operations. Moreover, people will be more confident and comfortable in using conversational AI bots if they trust the provider. Whereas, trust is a result of sophisticated security and reliable authentication processes that should be embedded in all the areas of the banking system, including chatbot NLP architecture.
• NLP integration. Undoubtedly, not all chatbots require the Natural Language Processing technology, and the decision mainly depends on the use cases and your business goals. However, if you want to ensure better user experiences, NLP is a must-have since it makes bots better understand and reply to customer queries. It also enables them to utilize relevant data for training, and so improve the quality of provided services.
• Development approach. Today there are two options for chatbot software development that allow companies to shorten their time-to-market, bring down costs, and facilitate further scalability. You can either create a chatbot from scratch or use end-to-end conversational services. Custom solutions ensure better flexibility and full compliance with your specific needs, but they require engineers with domain knowledge and advanced skills. Meanwhile, SaaS (Software-as-a-Service) solutions provide all the essential integrations and services, but they also have drawbacks mainly associated with narrow configuration and your full dependence on the service.

Final Thoughts

These days, AI chatbots differ in approach, complexity, and use cases, thus providing business owners with even more options to choose from. What we know for sure is that these intelligent assistants are here to stay, and they will only get smarter over time. They have all the essential capabilities to smoothly navigate teams through large volumes of data, help them make wise decisions faster, and gain better predictability.

Ready to augment your in-house teams with the best AI chatbots? — TEAM International is at the forefront of the conversational revolution. It provides the world-leading banks and other financial institutions with extensive knowledge and experience to ensure seamless project launch and on-time delivery of fully-fledged AI solutions.

Don’t hesitate to contact us today and find out how TEAM can help you achieve the determined goals faster and more efficiently.