Sign In

Five lessons banks can learn from disrupted industries


The utilities industry has long struggled to minimise leakage from water networks, with conventional methods failing to fix the problem. But IT consultancy Capgemini worked with their utility clients to help limit water loss through the effective use of predictive analytics.

“The application of advanced analytics combined with extensive integration of multiple data sources has enabled us to identify leaks up to three weeks before they would normally be identified,” says Colin Payne, principal at Capgemini Consulting. “The application of data solutions here is a classic example of a step change that turns a traditional reactive business model into a predictive one.”

Financial institutions can leverage their customer data, including payment history, phone transcripts and even social media accounts, to capitalise on the offer of predictive analytics to assist in the detection of fraud. Predictive analytics tools can be applied to virtually any part of a bank’s operations to discover consumer behaviour, showing the potential for data solutions to go far beyond just improving operations.

Established business models in the banking industry are on course to be shaken up as every click, purchase, like and search a consumer makes is used to create a unique digital identity, which can then be fed into predictive analytics solutions.




Online banking has made access to banking services easier than ever for millions of people and in the process reduced the need for an extensive branch network. Consumer group Which? says more than 1,000 branches were shuttered between January 2015 and January 2017, with further closures expected this year.

However, many customers still want to use their local bank branch for face-to-face services, leading to difficult conversations around which branches to close. The effective use of data can make it simpler to decide which location is the best to provide these services.

“Businesses such as parcel collection firm Doddle have used data intelligently to both provide a service and at the same time shrink the footfall needed, so they can operate out of a simple kiosk in a supermarket or station, without needing to tear up the area to incorporate it,” says Rowan Scranage, vice president and general manager, Europe, Middle East, Africa and Asia-Pacific, at database provider Couchbase.

These data solutions will become increasingly important as new physical banking initiatives, such as drive-through banks, are trialled in the UK over the next few years.



Establishing a comprehensive customer identity is vital for banks who want to offer the most relevant products and services. One of the key benefits of data is its ability to illuminate customer needs and requirements, but obtaining hyperspecific details on clients can be difficult. To improve understanding of exactly what consumers want, banks can follow the example of healthcare organisations such as BD which is using data to transform diabetes treatment.

“IoT-enabled EpiPens combine with phone apps to not only record exactly when insulin is administered, but share that information with the patient’s doctor. Patients can also take photos of meals, which are then available for their doctor to view, giving much more insight into patients’ lifestyles and potential risks,” says Rowan Scranage at Couchbase.

If banks were to duplicate this method for mortgage, loan and insurance products they could present more accurate advice and a personalised service. The Financial Conduct Authority’s Mortgage Market Review means lenders are carrying out more detailed affordability checks and expecting applicants to divulge more personal financial information than ever, which can often be seen as intrusive.

However, if banks were able to collect this data seamlessly from the customers’ IoT-enabled devices and in the process gain insights into their lifestyle, more appropriate products could be offered.



The use of artificial intelligence (AI) in banking is not a new phenomenon, but rapidly developing AI technologies are expected to become commonplace in banking over the next few years.

“Early applications for AI have spread through many industries, from healthcare where providers are starting to use cognitive analytics to aid in the diagnosis of patients, to consumer products such as Apple’s Siri, with varying degrees of success,” says Dr Richard Harmon, director of Europe, Middle East and Africa financial services at Cloudera. “This is viewed as one of the key areas where big data analytics will accelerate continued innovation and development.”

Retail companies have been at the forefront of using AI-enabled chatbots and virtual agents, with banks yet fully to embrace these solutions. As speech recognition and decision-making technologies improve, financial institutions will be more comfortable investing in data-backed solutions such as robo-advisers that offer automated financial planning services.

If the technology continues to grow at its current rate then it would not be outlandish to expect to see experimental physical robots in-store at banks, insurance firms and other institutions within the next five years, especially as technologies around visual perception and language translation are perfected.



 Major retail banks now all have mobile and internet banking apps, but these services are often cumbersome to access and provide little beside a basic account summary. With the rise of digital-only challenger banks, such as Atom Bank, traditional financial institutions have to make better use of the data they possess to offer a better user experience.

“Companies like Amazon, Uber and Netflix have won customers from competitors by prioritising the end-user. They have used customer data to offer a more personalised, seamless and connected experience. Banks must contend with the fact that consumers have raised the bar on speed, ease of use and consistent service in a digitised world. Just like other industries, bank customers expect personalised, targeted and contextually relevant interactions anywhere and anytime,” says Richard Harmon at Cloudera.

By making it easier to access banking on the go and present pertinent products to users on mobile, based on extensive customer data, conventional banks can utilise the most user-friendly elements of startup banks.

“Big data has made this possible as well as breaking the departmental silos and adding new types of data sources. Use-cases include next best offer, lifetime customer value, churn analysis, sentiment analysis, enhanced actuarial models and others,” adds Dr Harmon.