The virtual agents are designed to resolve most customer-facing queries without the need for human intervention. The bank, having over 9 million private customers and more than 500,000 active corporate accounts, chose the Boost.ai platform, which is currently used by nine of the ten largest Scandinavian banks.
Nova, Nordea’s first-line customer-facing virtual agent, averages over 220,000 conversations per month across the Nordics, for private banking customers. Of the total customer interactions, the bank claims a 91% resolution rate for private banking customers and a 95% resolution rate for corporate customers.
In a change of strategy, Nordea has adopted a chat-first approach to banking across all markets, meaning that all online customer queries are first received by a chatbot. The company states that over 50% of customers are satisfied with the help they get from this initial interaction. If a query cannot be resolved by a virtual agent, a transition to a human agent takes place.
In recent years, many financial institutions have devoted significant capital to digital-and-analytics transformations, aiming to improve customer journeys across mobile and web channels. Despite these big investments, most banks still lag well behind consumer-tech companies in their efforts to engage customers with better service and experiences. The prevailing models for bank customer acquisition and service delivery are beset by missed cues: incumbents often fail to recognise and decipher the signals customers leave behind in their digital journeys.
For banks, successfully integrating core personalisation elements across the range of touchpoints with customers is critical to deliver a superior experience and better outcomes. The reimagined engagement layer should provide the AI bank with a deeper and more accurate understanding of each customer’s context, behaviour, needs, and preferences. This understanding, in turn, enables the bank to craft an intelligent, personalised offering. To support this, banks need to analyse customer data in real time and embed analytical outcomes within customer journeys for fast execution of customer transaction requests and service queries, enabling instant fulfilment.
Constantly evolving digital context, cutting-edge competition, and ever-changing customer expectations are challenges that almost every industry is facing, comprising banking and financial institutions.
Considering the fact that half of the global population is using online banking nowadays, banks need to put considerable effort into improving their digital platforms to retain current users and attract new ones. Although customer experience is the key to scaling the revenue growth of financial service providers, improving it isn’t an easy task, especially when customers are getting more conscious of digital banking services.
To serve the digital customer more effectively, banks will need to move beyond traditional customer support to chatbots, customer relationship management (CRM), and automation workflows. As customer inquiries can be countless and repetitive, solving case-by-case manually can unnecessarily consume time and resources. Automating the customer support process with pre-built scenarios can help banks manage numerous users’ issues in real-time while reducing headcount and manual customer support dependency.
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