Artificial Intelligence Banking Fintech News

Finn AI Report: Eight Considerations for Conversational AI in Banking

Finn AI Report: Eight Considerations for Conversational AI in Banking

Training, datasets, and chatbot position all play an important role in AI chatbot success

Finn AI, the world’s leading AI-powered conversational banking technology provider, released its research report: Eight Considerations for Conversational AI in Banking, a primer for banks that are considering implementing AI in their contact center. The report findings will be revealed during a Webinar on December 3, 2020.

Read More: Blackboxstocks Closes $1 Million in Debt Financing from Feenix Venture Partners

“Throughout the COVID-19 crisis, many organizations have doubled down on their investments in AI, likely because AI can deliver near immediate cost and efficiency savings,” said Jake Tyler, CEO of Finn AI. “Yet, while considerable effort has been made to make AI less of a black box and approachable to decision makers, banks still want to better understand all the factors at play when making a product decision.”

AI can be financially and technologically accessible to banks of all sizes, and a competitive advantage for banks looking to compete with fintech and large banks in delivering a superior customer experience. Yet, there are several best practices that banks should take into account to increase their chance of success in an AI implementation, including:

Read More: Gemini selects ClearBank as UK banking services provider

  • Training, not software development, is the largest expense in creating an AI chatbot: Large datasets of questions are required to train the bot pre-launch. The systems used by Google, Amazon and others have tremendous learning capacity but start out with no banking knowledge. Banks should instead use a banking specific dataset.
  • An industry-specific managed service can significantly reduce initial and long term lifecycle cost: Using a managed service, specifically one built for banking, offloads perpetual training and takes advantage of banking knowledge already built into the model. A managed service is by far a more cost effective answer throughout the project lifecycle.
  • AI in the contact center delivers faster service for routine tasks: Using AI powered bots to handle contact center volume, banks can realize increased efficiency, offer more accessible service and scale quickly. Specifically, automating the contact center with AI has the potential to offload 75% incoming requests.
  • AI chatbots enable self service, the desired option for most customers: Banks should think of success as giving customers the medium of their choice. Establishing multiple channels that address the needs of the customer at different times, and with different levels of commitment, makes your digital banking experience more sticky.
  • Chatbot position plays a key role in defining project success: To the extent that conversational AI achieves excellent customer service depends on just how prominent banks make it. Questions often arise about how much traffic is diverted from the call center, but actual results depend on how visible the chatbot is.

The growth of fintech, changes in customer needs and behavior, Covid-19 and financial insecurity has increased the need to explore how automation can better service customers. Using AI to improve the efficiency of customer service can have a positive impact on operational costs and customer satisfaction that can translate to banks better servicing a broad range of customers.

Read More: VendEngine Named A Fastest-Growing Company In North America In Deloitte’s 2020 Technology Fast 500™

Related posts

Cenlar Sees 1 Million Homeowners Move to Paperless Billing

Fintech News Desk

HID Global Works With Temenos to Help Banks Meet Heightened Demand for Trusted Mobile and Online Transactions

Fintech News Desk

Zappix Extends its Reach in FinTech; Signs New Customers for Digital Payments

Fintech News Desk