Not so long ago, many banking pundits were heralding the extinction of the human bank employee. Tellers would be replaced by bots. Digital-advisors would put financial advisors and product specialists out of work. Customer service centers would go dark as cloud-based chatbots took over the customer interaction.
Somewhat predictably, that sparked a flurry of new experimentation, investment and innovation within the financial services sector as banks, fintechs and service providers vied to beat each other to market with a new automation or bot.
Lessons were quickly learned. For example, both first-mover banks and fintechs found out that a 'pure' digital-advisory model simply wasn't viable; margins were too low, customer acquisition costs were too high and few customers (particularly high-net worth ones) were willing to turn their backs on their human financial advisor for a bot.
Banks (and those in other service industries) also found it impossible to turn the lights off on their customer service centers. Bots simply didn't have the training or access to data they needed to convincingly replace humans (particularly when those interactions were governed by strict conduct rules).
Rather than taking the 'all or nothing' route, many banks are now experimenting with how automation – tied to predictive analytics – can help their human employees become more strategic and their customers enjoy a better experience.
The signs are everywhere. The shift towards 'next best offer' engines in product sales and financial management is perhaps the best example of this at work. Instead of blindly selling products based on generic customer segmentation models, these platforms leverage extensive customer data, machine learning and smart automation tools to allow agents and advisors to quickly guide their clients to solutions and products that suit their unique needs.
The more sophisticated engines go beyond this, scraping the entire ecosystem of customer data (both inside the organization and outside) to accurately predict when customers will need certain products and how they will want to engage with them. Instead of 'next best offer', they are moving to 'next critical need'.
This type of predictive augmented decision-making is cropping up everywhere. Inside the enterprise, many banks are starting to deploy bots that essentially predict the type of information their agents will need and then serve it up to them in a smart, user-friendly visualization.
At the customer level, predictive analytics and automations are being combined to deliver personal financial management tools aimed at helping customers make better financial decisions. Based on a user's stated financial goals and leveraging application programming interfaces (APIs) and visualizations, these tools are helping customers better predict – and then respond to – their future financial needs.
Some predictive engines are being used to deliver exciting insights, particularly around customer trends and future technologies. They are also being used for much more mundane, yet important tasks such as automating the prediction of ATM downtime, branch utilization models and operational performance.
Our view is that the measured introduction of bots and automations is just the first step on the path to smarter, more predictive banking models and solutions.
Our view is that the measured introduction of bots and automations is just the first step on the path to smarter, more predictive banking models and solutions. And while they won't lead to the extinction of human employees, they will lead to a major shift in perspectives – from the historical to the predictive.
The impact of that shift will be massive. Currently, much of what a bank knows about its customers comes from , for example, things like credit histories, past account activity and recent life changes. Every decision is based on an understanding of what has happened in the past.
These trends towards greater use of automation and predictive analytics, however, suggests that interactions and processes will increasingly be based on what the customer is likely to want in the future. And that will require a very different approach to everything from customer relationship management and channel development through to technology investments and product design.
Our experience working with leading banks and fintechs suggests there are four areas where bank executives should focus if they want to help their organization move towards more predictive models.
Over the coming year, we expect to see many of the leading banks take much more deliberate steps towards linking their automation with predictive analytics. And that, in turn, should unlock new ways of thinking about future growth, customer experience and technology investment.
To be sure, more experimentation will be required. And there will certainly be some failures. But our prediction is that customers will increasingly expect the predictive. Banks should start moving now to get ahead of their expectations.