The combination of automation and predictive analytics is creating new opportunities for banks to reinvent the customer experience and retain relevance.

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 鈥榩ure鈥� digital-advisory model simply wasn鈥檛 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鈥檛 have the training or access to data they needed to convincingly replace humans (particularly when those interactions were governed by strict conduct rules).

Predicting a better experience

Rather than taking the 鈥榓ll or nothing鈥� route, many banks are now experimenting with how automation 鈥� tied to predictive analytics 鈥� can help their human employees become more strategic and their customers enjoy a better experience.

The signs are everywhere. The shift towards 鈥榥ext best offer鈥� 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 鈥榥ext best offer鈥�, they are moving to 鈥榥ext critical need鈥�.

 

Everyone wins

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鈥檚 stated financial goals and leveraging application programming interfaces (APIs) and visualizations, these tools are helping customers better predict 鈥� and then respond to 鈥� 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.

Beyond bots

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鈥檛 lead to the extinction of human employees, they will lead to a major shift in perspectives 鈥� 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.