By Vijai Shankar, VP of Product & Growth Marketing at Uniphore
Even pre-pandemic, the financial services industry was facing ‘innovate or die’ decision points, in the face of newer, more technologically nimble competition and changing consumer behaviour.
More customers were banking remotely than ever before, with one study suggesting that four out of five people had started to prefer digital banking to in-person visits.
Then came the watershed moment of Covid.
Customers unable to access their branch locations increasingly took to digital channels for customer service.
The need for digital transformation accelerated rapidly, as contact centres became the main – and often the only – human touchpoint for banks and an increasingly demanding customer base.
Optimising these human-to-human conversations is imperative to create the kind of positive, empathetic experiences that drive customer satisfaction and loyalty as well as help deepen existing relationships.
Conversational artificial intelligence (AI) can make that happen, and it’s helping financial service organisations in four main ways:
Enhancing the Conversation and the Experience
Conversational AI optimises every conversation by helping call centre agents be more productive and empathetic while personalising the experience for customers.
With Conversational AI on hand to do the work of searching for a customer’s information and past banking history, the agent is freed up to focus on the customer, without being distracted by manualling searching for the right information.
With the human agent’s response augmented by Conversational AI, the query is answered quicker and more effectively. 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 while they are talking to the customer about products and services that could be useful to that customer.
Minimising After-Call Work
What happens after the call ends is just as important to a bank’s business outcomes as what happens during the conversation. The time spent in after-call work — including categorising and summarising the call, updating systems, and taking follow-up actions — impacts average handle time, call waiting times, customer satisfaction, costs, agent productivity, and satisfaction.
There are automation solutions that, during a conversation, can listen and automatically transcribe calls in real time. After a call ends, AI automatically creates and presents the call summary to the agent to edit and confirm. In addition, it can automatically update the bank’s CRM system.
This kind of conversational AI to automate after call work improves the experience for both customers and agents, while improving productivity and accuracy within a financial institution.
Capturing and Fulfilling Promises Made During the Conversation
A promise made that is not kept or tasks that are not performed correctly can quickly negate 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 the fulfillment of the promise after the call. Typical promises include: issuing a credit for closing fees, committing to a delivery such as appointment with a personal banker and more. Automating promise management will drive reduction in repeat callers while also improving the overall customer experience.
Extracting Insight from Every Conversation
By understanding and analysing every conversation, banks gain deep insight into trends and opportunities for improving contact centre outcomes. A conversational automation platform that includes AI-powered interaction analytics for voice, email, and chat interactions helps banks uncover the true reasons for 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 over half (60%) of financial services companies have embedded at least one AI compatibility. Some of the most commonly used AI technologies implemented are: robotics process automation (36%) for structured operational tasks; virtual assistants or conversational interfaces (32%) for customer service divisions; and machine learning techniques (25%) to detect fraud and support underwriting and risk management.
As we enter what is set to be one of the toughest markets for over a decade, those embracing the opportunities of AI to help with operational efficiency, staff satisfaction and customer loyalty – making sure that the value of every conversation is fed back into the business – will likely pave the way to another watershed moment for how banks operate.
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