The digital transformation trend began a few years ago in the banking industry when banks saw an opportunity to improve the customer experience and improve their competitiveness. And the Covid-19 pandemic has certainly accelerated the digital trend. The pandemic also revealed where banks have been successful and exposed their weaknesses.
Many banks have implemented Robotic Process Automation (RPA) and noticed a certain increase in efficiency, which has piqued their appetite for new technologies such as artificial intelligence (AI) and machine learning to increase the level of automation. Many who have started are heading to the cloud and have moved some of their workflows to the cloud with a lot more to move.
Nevertheless, the path of digital transformation did not go smoothly for many established companies. These organizations face cultural challenges in addition to the challenges associated with legacy technologies. “Transformation doesn’t come for free, either in terms of money or in terms of the elements of your culture that you have to discard,” said Jason Maude, chief technology advocate at British challenger Starling Bank.
In addition to the statement from the management about what they want to achieve in a digital transformation program, Maude says they should also recognize what they want to give up. By changing a bank’s culture, “technology will follow,” he adds. But trying to implement digital technologies without changing culture results in a “white elephant”.
While Starling Bank is a digital-first organization and is not weighed down by legacy technology or even legacy culture, transformation for a bank of higher age and size requires a different approach to achieve the same goals, says Claire Dancy , Head of ServiceNow Product and Technology Data at Lloyds Banking Group. “You have to be aware of the landscape you are working in,” she says. “You have to focus on the risk in the technology because you are not completely in the cloud.”
To drive this culture shift, she explains that Lloyds Bank’s Competence and Innovation Center is “about colleagues and how we can tap them to work on more exciting opportunities”.
Niall Bellabarba, fintech innovation specialist at Deutsche Bank, points out the dichotomy between size and culture and argues that cultural change at a large bank does not happen overnight or without friction losses. “The management of a bank plays a big role, as does the partnerships that banks enter into.” Deutsche Bank’s latest cloud and innovation partnership with Google Cloud represents a significant cultural change for the bank, he says. This cultural shift overshadows the technological aspects of the partnership and hits the core of the structure and goals of the bank’s technology teams. “This is fundamental to digital transformation for a large company like Deutsche Bank,” he adds.
One of the cultural challenges for larger institutions is the tendency to “look very inward,” says Bellabarba, but most of the technological changes take place “outside” of a bank. Therefore the concept of bringing in fresh ideas and technology from outside is one of
Deutsche Bank’s key performance metrics, he adds.
The journey for banks from their current technological level to a world of hyper-automation, which means bringing together technologies like AI, advanced analytics and RPA to improve people and automate processes, will not necessarily involve replacing the “human element” as a whole Cases, says Mr Bellabarba. “In wealth management, for example, trust is fundamental and human contact in this area is geared towards this. Hyperautomation has a business case in the sense that it cuts costs, but a bank has to be careful not to cut into the bones of a customer relationship. “
That is not to say that certain elements of asset management, such as advice, exchange traded funds and portfolio construction, cannot be hyper-automated, but the relationship between the asset manager and the client is “about emotions,” he adds. “If your portfolio is down 15%, there are few computers that can handle this human fear and anxiety.”
Focusing on hyper-automation makes sense as most banks have return on equity and a cost base that is too high, but “human contact is one of the aspects that I think should always be maintained,” concludes Bellabarba.
Maude agrees, saying that the “big gains” in automation are moving data from one place to another, which machines can do much more easily, reliably, and efficiently than humans. “I’ve heard a lot from companies that want to automate their call centers and customer contacts, but I don’t think that will work,” he says. “We shouldn’t ignore the emotional level on which money works.”
To improve the customer experience, the “first contact” has to be human contact, says Ms. Dancy. She agrees with Mr. Maude that back office activities like moving data are ideal for hyper automation and lead to an improved customer experience. It is important that the customer experience is taken into account and if they want a “first touch” to be human it should be enabled, but the customer should be informed that their result was done in seconds.
Measuring the success of a hyper-automated environment requires a number of factors, says Keith Pearson, global head of financial services, go-to-market at ServiceNow. “I think of business cases in a bank in terms of the ‘six Cs’: cost, compliance, control, peer experience, customer experience and continuous improvement. A bank should be able to measure progress in each of these areas to demonstrate success, ”he says, adding that the definition of success will vary based on the bank’s priorities.
Determining the success or value of a hyper-automation project is also about transparency, says Ms. Dancy. “There has to be a relationship between the value you want to deliver and the time it takes to get it. Transparency between the business area and the engineering / IT department, allowing them to have a mature conversation about what value is needed and what is prioritized, is critical to delivering that value, ”she says.
Given his background as a software engineer, Mr. Maude points out the folly of measuring success against project delivery times. Whenever a project plan is created asking software engineers to comment, it says that one of two things is going to happen. When asked how long a project will take, a software engineer will either come up with a number that gives him enough time to be sure or he is not experienced enough to lie. “Any case will lead to bad results,” he says.
In general, there is a “tug-of-war” between software engineers and managers over project schedules. “As a software engineer at the age of 12, I can’t tell you how long a particular software project will take, and I don’t care,” says Maude. “One of the reasons for taking a Minimum Viable Product (MVP) approach is to eliminate the need for a project plan with dates.”
The “illusion of control” of drawing out of thin air about how long a project will last must be abandoned, he adds.
Risk aversion in a highly regulated industry forces a culture where certainty of results is a “good thing,” notes Mr. Bellabarba. “But certainty is a fallacy, that’s why we have this bizarre situation of wanting estimates that are inevitably a game of chance.”
There are many different ways to measure the success of a transformation project, he adds. These include some “mundane” ones like comparing the total cost of ownership of an infrastructure in-house or in the cloud, and others like MVP approaches where results are achieved on a smaller basis and then scaled up to the cloud.
Deutsche Bank measures success by a variety of means, including the results of hackathons, which Mr. Bellabarba describes as powerful ways to advance the idea that change can come through small projects. “Smaller teams can move forward faster than larger project teams, which are more likely to hit and miss time estimates,” he says.
The end of a digital transformation journey is “when you realize that there is no end,” says Maude. “For Starling Bank, digital transformation is something that happens constantly and every day. We are constantly changing the underlying code base on which the bank runs, be it the core platform server code in the backend or our public application programming interfaces or our connections to other parts of the financial services landscape, our mobile apps, our web app, etc. Digitization is a constant fact. “