History repeats itself with every technological revolution, sparking the same old fears about job loss and human redundancy. Much of this is unfounded because each time, when routine and repetitive jobs got mechanized, those who were displaced often were elevated into bigger, better roles. The pattern was consistent across industries; whether it was manufacturing, retail, entertainment or banking, the technology of the day – from the steam engine to the mainframe – augmented and amplified, rather than eliminated, the work of human beings.
This time around, the debate is about cloud-driven banking shaking up employment in the financial sector. But the reality is the same, as always.
A Look at Banking Functions
To understand what is happening, let us segregate various banking functions from an inside-out perspective. Strategy definition occurs at the top, at the level of the board and senior management. Lower down the order, the strategy is converted into plans, which are then executed by various teams. All these activities involve data operations such as collection, comparison, tracking, monitoring and course correction. There is no interaction with the end customer just yet.
The second set of functions is aimed at achieving strategic and financial goals. These functions relate to business execution – expanding the corporate, retail or investment banking business, adding branches, acquiring customers and so on. The desired outcomes are defined as KPIs for individual managers, expressed as growth targets and budgets.
The moment of truth is when these internal functions flow out of the bank to the last mile – in other words, when a frontline employee, such as a branch manager or relationship officer, comes in contact with the end customer. Not much has changed in the way banking has been transacted since 14th century Greece. The counter has been replaced by the teller window, but the interactions are still about facilitating lending, borrowing and payments. What has changed, however, is the number and type of participants. In addition to the money lenders and joint stock companies of old, today there are banks, of course, but also fintech firms and big tech companies. The mode of service delivery also has changed (perhaps beyond recognition) to include branches, mobile phones, websites, wearable devices and even social media applications.
The Role of Machines
Let us examine the role of machines (which includes software and digital technology, including cloud) in each set of functions. Strategy visioning and implementation clearly need human intelligence; however, machines can provide rich, accurate and timely data so the people in charge can make better decisions. Similarly, while planning activities – involving interpretation, visualization and application of past experience – too cannot be handed over to machines, they can certainly leverage machine learning to do what-if analyses and simulate various scenarios in real-time.
Business execution functions offer more potential for humans and machines to actually work together, by farming out repetitive and routine work, such as data collection, comparison and analysis, to the latter. Following that, the human workers will apply their wisdom to machine-generated insights to take course-corrective actions to, say, improve revenue, lower cost, enter new markets, etc. In each case, far from taking over the role of human workers as feared, the machines are actually elevating them.
Several operational goals are set up as part of business functions, with managers at the head office, zonal or branch level assigned responsibility for achieving them. While the managers make the decisions – for example, which channels to employ – implementing those decisions at scale is only possible with the help of machines – think origination, onboarding, document verification, KYC checks, credit rating analyses etc. This is how Paytm onboarded 200 million customers when India announced demonetization, and RBL acquired massive digital deposits during the pandemic lockdown. Without support from machines, their human employees would only have been able to handle a fraction of the business volume.
That being said, there are some jobs that can, and should, be given to machines. A good use case is technical resolution – attending to issues in databases, networks, servers or applications – that can be performed proactively, preemptively and efficiently by AI-driven systems at a scale that is out of reach of human workers. What’s more, the systems will continue to learn and improve over time, so humans need only step in to resolve complex or exceptional issues. When a customer calls a contact center, call tracking, tracing and auditing, as well as caller identification, are best left to the machines, which will also route the call to the best available agent; the system can also suggest the best solution based on past history. Note that while the machines take over these jobs, no roles are actually lost. The agent remains in charge of making the final decision and communicating it to the customer.
Cloud-Driven Banking
Now let’s look specifically at the impact of cloud-driven banking on human workers. When it comes to lowering cost while improving agility, the cloud is unbeatable. Today, when banks are under immense pressure to reduce cost from both a low-interest environment and new competitors operating at half their cost-to-income ratios, it is unviable for them to maintain expensive physical infrastructure, such as data centers. Cloud service providers and hyperscalers are the answer for managing everything from servers and systems to databases and data centers. Outsourcing these functions relieves the banks’ operators but does not make them redundant, because they can be redeployed in higher roles, such as planning for growth. This means that instead of providing services to the business organization, they will demand services from the cloud providers; in other words they will become “service seekers” instead of “servicers.” In this role, they will require higher-level skills like negotiation, critical thinking and decision making.
To sum up, whether it is artificial intelligence or machine learning, when technology takes over a routine banking function, it always opens the doors for human workers to elevate themselves into a more rewarding role. Cloud-driven banking is no different.
