By Vijai Shankar, VP Product & Growth Marketing at uniphora
Even before the pandemic, the financial services industry faced “innovate or die” decision points, in the face of newer, more technologically nimble competition and changing consumer behavior.
More customers were banking remotely than ever before, with a study suggesting that four in five people had started to prefer digital banking services to in-person visits.
Then came the defining Covid moment.
Customers unable to access their branches are increasingly turning to digital channels for customer service.
The need for digital transformation has rapidly accelerated, with contact centers becoming the primary – and often the only – human touchpoint for banks and an increasingly demanding customer base.
Optimizing these human-to-human conversations is imperative to creating the kind of positive, empathetic experiences that drive customer satisfaction and loyalty and help deepen existing relationships.
Conversational artificial intelligence (AI) can make this possible, and it helps financial services organizations in four main ways:
Improve conversation and experience
Conversational AI powers every conversation, helping call center agents be more productive and empathetic while personalizing the customer experience.
With Conversational AI at hand to do the work of finding a customer’s information and banking history, the agent is freed up to focus on the customer, without the distraction of manually searching for the right information.
With the human agent’s response augmented by conversational AI, the request is processed faster and more efficiently. By identifying patterns and changes in the customer’s banking habits, such as cash flow trends, the machine behind the conversational AI can even alert agents as they talk to the customer about products and services that might be useful. to this customer.
Minimize after-call work
What happens after the call ends is just as important to a bank’s business results as what happens during the conversation. Time spent on after-call work – including categorizing and summarizing the call, updating systems, and taking follow-up actions – impacts average handle time, wait times calls, customer satisfaction, costs, productivity and agent satisfaction.
There are automation solutions that, during a conversation, can automatically listen in and transcribe calls in real time. After the call ends, AI automatically creates and presents the call summary to the agent for editing and confirmation. In addition, it can automatically update the bank’s CRM system.
This type of conversational AI to automate after-call work improves the customer and agent experience, while improving productivity and accuracy within a financial institution.
Seize and keep the promises made during the conversation
A promise made that is not kept or tasks that are not carried out correctly can quickly undo the positive effects of a good customer conversation. Conversational AI and automation platforms that help automate after-call work summaries can also detect promises made during calls and automatically manage post-call promise fulfillment. Typical promises include: issuing a credit for closing costs, committing to a delivery such as an appointment with a personal banker, etc. Automating promise management will lead to a reduction in repeat calls while improving the overall customer experience.
Extract information from each conversation
By understanding and analyzing every conversation, banks gain deep insights into trends and opportunities to improve contact center results. A conversational automation platform that includes AI-powered interaction analytics for voice, email, and chat interactions helps banks uncover the real reasons behind customer churn, drive compliance and identify other opportunities for planning and operational improvements.
The Future of AI in Financial Services
According to McKinsey’s Global AI Survey report, more than half (60%) of financial services companies have integrated at least one AI compatibility. Some of the most commonly used AI technologies are: robotic process automation (36%) for structured operational tasks; virtual assistants or conversational interfaces (32%) for customer services; and machine learning techniques (25%) to detect fraud and support underwriting and risk management.
As we enter what is expected to be one of the toughest markets in over a decade, those who seize the opportunities of AI to help drive operational efficiency, employee satisfaction and loyalty of customers – ensuring that the value of every conversation flows back into the business – will likely pave the way for another watershed moment for the operation of banks.