- April 7, 2023
- Posted by: Shalini W
- Category: Information Technology
AI has quickly moved from science fiction to the mainstream in a variety of industries, including retail and commercial banking. AI is proving useful in a variety of back-, middle-, and front-office applications. Not all of these applications have a discernible impact on the customer experience (CX). Those that do are attracting significant and growing interest from institutions of all sizes around the world.
AI is influencing customer experience in two ways. The first is through personalized data insights and advice generation (also known as next-best-action or next-best-conversation), which can be delivered directly to the consumer via digital channels and/or through interactions with a business banker, contact center, or branch staff. More recently, conversational AI has been influencing CX in a variety of ways. Natural language understanding, generation, and processing (NLU, NLG, and NLP) and various types of predictive or propensity modeling are examples of specific technologies. These technologies are not used in isolation, but are combined to support a wide range of use cases.
Data Analysis And Recommendation Generation
Many financial institutions have a broad understanding of their customers and/or customer segments. Fewer have a thorough understanding of each individual customer. Some companies have developed capabilities for determining individual customers’ profitability, lifetime value, and/or share of wallet. These are valuable viewpoints, but they are rarely used to guide how banks interact with individual customers. It is uncommon to see the use of customer data operationalized to generate actionable insights that inform the customer conversation in real-time across all touchpoints. But, thanks to artificial intelligence, it won’t be for long.
A small but growing number of banks are developing “decision hubs” that are powered by a portfolio of statistical models and fed by readily available customer data in order to create the next best conversations that are aware of the customer, the situation. Initially designed to maximize sales effectiveness at many banks, these hubs are increasingly designed to support a diverse set of business goals such as acquisition, retention, cross-sell, and compliance, but are stringently prioritized against one another—to ensure banks engage customers with the most relevant next-best-conversation possible—at every point of interaction, from branch and contact-center conversations to mobile apps and chatbots.
Conversational Ai, Chatbots, And Virtual Assistants
Chatbots have been a hotly debated topic in recent years. They are united by two characteristics:
Interaction through conversation: Conversational interfaces can provide a more convenient and often faster user experience than navigating through an app or website-user interface to meet a user’s needs. Text chat currently dominates this space, but voice is gaining ground for a subset of use cases.
Automation: Conversational interfaces are not a new concept. For years, businesses have offered live chat and encouraged person-to-person conversations in contact centers and branches. However, doing so necessitates significant human resources. The primary advantage of automation is its ability to meet routine customer needs in a convenient and low-cost manner for customers while freeing resources for more complex tasks.
Chatbots are natural language text interfaces that are used as an alternative to navigating an app or browser to complete specific tasks. Celent refers to chatbots as any type of automated conversational interface, and AI-powered bots as virtual assistants. First-generation chatbots were built with rules that favored linear interactions guided by pre-defined flows. Chatbots were created to perform a specific and narrowly defined set of tasks. The CX was favorable if a given user’s need could be met using predetermined flows within the bot. However, if the user’s intent deviates from the bot’s predetermined-scenario portfolio, the CX may quickly deteriorate. Because of this limitation, first-generation chatbots were generally a disappointment. The good news is that, thanks to conversational AI, more capable alternatives are now available.
Natural language processing (NLP) and natural language understanding (NLU) are used in conversational AI to enable dynamic dialogues between a customer and a machine, as opposed to a single-turn static conversation (for example, “What is my balance?” “What was my last credit-card charge?”). The AI model interprets context and sentiment, going beyond simply searching for and delivering an answer. The dialogue can be delivered orally or in writing. Text technology is more mature than voice technology. Both require a smooth transition to a human when the conversation exceeds the technology’s ability to provide a satisfactory outcome. Of course, artificial intelligence is used to make that determination.
Most virtual assistants today use a chat interface, but there are only a few avatars available. Avatars and virtual assistants are also used for internal purposes, such as the IT helpdesk.
What Artificial Intelligence is doing
AI has a significant impact on CX in both customer-facing and employee-facing applications via digital channels. Customer-facing use cases predominate in retail banking, whereas employee-facing use cases predominate in corporate banking.
Applications that interact with customers
The pyramid of customer needs is a useful way to differentiate use cases in AI applications. “Tell me—basic queries” is at the bottom of the pyramid, indicating that the customer has a basic question “Do it for me” is a step up, which includes account onboarding and basic-task optimization (“You can send an electronic request for payment to this buyer”). The following level is “Tell me-data insights,” which includes descriptive (like report generation) and predictive analytics (cash-flow forecasts). “Advise me” is at the top, and it involves personalized recommendations to address a specific need or resolve a specific issue (for example, “A cash shortfall is expected; here are three options to cover it”). most banks attempting to scale are focusing on the bottom two rows of the pyramid, which account for roughly 70% of all use cases in production among leading AI vendors.
While not as sophisticated as next-best-conversations, these use cases can improve customer experience while providing banks with a compelling return on investment.
Applications Aimed At Employees
AI is being used to improve the tools available to relationship managers (RMs), contact-center and branch staff in the same way that it is being used to improve customer-facing applications. Because employee-facing use cases are more common in commercial banking, we’ll concentrate on them. For example, an AI platform could detect customer dissatisfaction during her most recent call to the service center, prompting her RM to reach out to her. Or it could detect a negative forecast for a customer’s business made by an analyst, triggering a review of the customer’s outstanding loans.
AI will be embraced as a partner rather than a competitor as it increases RM productivity while making their jobs more interesting. RMs will be able to delegate basic tasks by sharing their desktops with their own virtual assistants. The concept of humans and machines working together has already been realized in compliance and fraud operations, where humans can review AI work and focus on cases that AI cannot resolve—and train the machine-learning model through their actions. A good way to evaluate RM-facing AI applications is to look at them throughout the customer lifecycle, from marketing and prospecting to engagement, cross-selling, and monitoring. the top use cases are customer engagement (36 percent includes inbound and outbound interactions), relationship building (22 percent includes cross-selling), and client monitoring (19 percent). Upstream implementations are scarce, as are customer-facing use cases.
The AI Business Case
A business case is required for any use case, no matter how appealing or attention-grabbing it is. AI vendors were asked to rank three business cases based on their banking experiences: cost savings, revenue generation, and customer experience improvements. The findings indicate that cost savings are the primary justification for front-office AI investments (see next figure). However, AI investments are rarely solely motivated by cost savings. This indicates that banks are growing more confident in AI’s ability to generate indirect return on investment (ROI), i.e. a return that is difficult to associate with a hard-dollar impact. Improvements in customer experience, for example, should drive revenue growth, but so could other factors (for example, prices and rewards).
There are numerous reasons why cost savings lead. First, banking possesses all of the characteristics of an industry in which AI has the potential to deliver significant cost savings. It is a process-heavy, paper-intensive industry with many repetitive tasks. Furthermore, its labor costs are generally higher than the national average. Second, advances in conversational AI are increasing banks’ confidence in experimenting with and then implementing AI to augment call centers, with impressive call deflection results. Third, customers have basic “tell me” and “do it for me” needs that a virtual assistant can meet more quickly than a customer service representative (CSR), resulting in a secondary positive impact on customer experience. However, success in realizing cost savings while fully supporting a customer is dependent on a seamless hand-off to a CSR when necessary.
Another business-case justification for AI introduced at COVID-19 is scale. The CARES (Coronavirus Aid, Relief, and Economic Security) Act was passed by Congress in the United States to help small businesses cope with the pandemic. It resulted in the processing of two decades’ worth of small-business loan applications in a single month. Because loan-application processes are not typically highly automated, the massive demand transient brought banks’ loan-application operations to a halt. Similarly, as banks closed branches, customers flocked to digital-banking channels and contact centers, resulting in multi-hour wait times to speak with a contact-center agent. During the pandemic, all of these scenarios destroyed CX while increasing bank costs. Judicial application of AI in each of these examples would have resulted in a win-win situation for banks and their customers.
Hopefully, we will not face another pandemic anytime soon, but demand fluctuations, albeit less severe, will occur again. Banks that had virtual assistants were able to train them on COVID-19-related scenarios and divert a large number of common queries away from their contact centers. With the current state of vendor readiness, a bank can implement and fully test a high-functioning virtual assistant in a matter of months. Front-office AI implementations have grown at a nearly 20% CAGR (compound annual growth rate) over the last two years globally. We anticipate that COVID-19 will catalyze rapid growth once more in the coming year.
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