Chatbots and other forms of technology in consumer-facing roles are proving to be a problem as the demand for human interaction increases. One solution to keep up with this is conversational AI.
James J. Sloan is chief information officer and executive vice president of financial services at Excellarate, the global, digital transformation company, and oversees financial service business strategy, sales and marketing, account management and client solutions delivery.
Having worked in the financial technology sector for over 20 years, Sloan has extensive experience in the evolution of dealing with consumer concerns. Having worked a variety of roles at Dow Jones Indexes and Prime Technology Group, he has experienced how poor communication can impact a customer and have a negative impact on a business.
Speaking to The Fintech Times, Sloan explains how conversational AI is transforming financial customer service:
Banking and finance are among many service industries finding tangible benefits from implementing artificial intelligence to its customer-facing functions. Their virtual assistants and chatbots streamline service and provide 24/7 assistance to many repetitive requests in the retail environment.
Yet modern technology can accomplish even more when also incorporating conversational AI. When combined, sophisticated systems like natural language processing, deep learning, emotion recognition, and other software development enable human-like interactions and generate vastly improved engagement that serves businesses and their customers even better.
Interpreting and responding to human input
Machine learning and AI have become critical components of modern-day finance. They are being used to optimise and streamline processes ranging from credit decisions to quantitative trading and financial risk management. AI’s strength at assessing data is being applied to wealth management by helping to build portfolios, evaluate investment risk and optimise trades.
Similarly, AI and ML are used for risk modelling, to set credit scores and predict bad loans, detect and monitor fraud and other functions that underpin banking and lending. And in capital markets, trading systems are calibrated to analyse market conditions, identify new investment opportunities, automate trade planning and execute it. All of this demonstrates how well AI and ML technology is established throughout fintech.
Yet now, it’s going further by humanising interactions through conversational AI.
Conversational AI meshes significant technologies: AI, ML, natural language processing, emotion recognition, deep learning, and others. Together, they interpret human auditory and visual input and respond, generating service to customers based on the information, cues and traits they exhibit.
The significant advancement is conversational AI recognises, interprets and displays emotion.
Functioning as a human operating system, conversational AI can present itself as a digital person, reacting to verbal and visual input not as a mirror or puppet but as an empathetic agent eager to serve. It interprets not only what the customer says or requests but engages in the emotional context of the interaction to respond appropriately.
Conversational AI functions autonomously to the person and the individual interaction they are having without preconception. Acting as a digital brain, conversational AI doesn’t get upset. It doesn’t have a bad day.
These same characteristics apply greatly to customer service interactions and the overall customer journey. It’s a hyper-personalisation that is otherwise limited to the best customer service agents only, who simply cannot cost-effectively address all customer inquiries 24/7/365, and who can be adversely affected by outside influences.
Enhancing service level
This level of AI refinement moves customer interaction and engagement well beyond what chatbots and earlier versions of virtual agents achieve.
Conversation AI is configured to have at its disposal all the knowledge the organisation wishes to be drawn upon. It easily accesses this information, plugging into existing data, allowing the customer and business to engage well beyond FAQs.
Customers can lean into the conversation to inquire beyond the usual requests, and, in turn, conversational AI engages the dialogue. It can move the conversation into a financial advising realm. “Tell me about your risk tolerance,” it can inquire. “If you have an investment philosophy, please describe it,” it probes.
And like the technology that’s come before it, conversational AI can use intelligence gathered from the customer to seamlessly direct them to product selections or different solutions based upon this natural give-and-take. It can screen customer responses for precise placement with a specific advisor or agent, if necessary, to culminate the preferred action.
Unlimited access
This triage using conversational AI takes place regardless of day or night. A customer who’s apprehensive about overseas market fluctuations and the effect on a portfolio can engage naturally, emotionally, and receive empathetic feedback. This empathy quotient allows the institution to engage more productively with customers and, in turn, fulfill their requests beyond rote responses. Similar results greet the mortgage lender, loan applicant, account holder or traveler who wants to act on a beneficial or unsettling discovery and immediately engages with their bank.
The scale at which conversational AI can operate is large. For example, the World Health Organization deployed a multi-cloud conversational AI digital person named Florence for 24/7 access worldwide during covid-19. As a smoking cessation tool to reduce the impact of the deadly virus on a susceptible population group, Florence counsels WHO visitors in any of four different languages.
Next-level evolution
Conversational AI does not remain static. It evolves to improve performance and outcomes.
Its natural language processing delivers details that help identify where conversation flow became snagged. Did the interaction abruptly change? Where did the person become confused? Are there unforeseen triggers that, upon removal, enhance more empathetic interaction?
While the information it gathers in individual interactions is anonymised for privacy, the metadata it compiles is leveraged to further its empathy quotient abilities. This improves conversational AI’s function to recognise and pivot conversations more quickly away from distress or unhappiness. It also proves to be a powerful feature, improving all subsequent interactions based upon customers’ emotional information and making those conversations more productive.
Fintech businesses are serving their customers every day. And like organisations across a variety of verticals, these businesses expend significant effort aligning their customer agents with the brand, its attributes, and the characteristics it wishes to portray and exude in every interaction. These same qualities can and should be incorporated into the conversational AI agent.
Successful businesses regularly separate themselves from competitors by offering better customer service. Conversational AI enhances that pursuit far beyond traditional methods. It leverages AI, ML and other advanced, evolving automation tools to create the most intelligent, human-like virtual engagement that thrives in customers’ emotional spaces to serve those clients and deliver more comprehensively on services.
