Operations

Digital banks have exposed two main operational gaps in traditional banks:

• From back-end centralized processes (e.g., loan processing) to distributed processes, (e.g., account opening at a branch) everything continues to be largely manual. And whatever automation does exist is typically at the task level; the processes themselves haven’t changed over all these years. Digital banks, meanwhile, have shown that vastly simplified processes are possible.
• The scale advantage that larger retail banks enjoyed over the years, with their shared overheads for IT, infrastructure, etc., may not exist for too long. Digital banks have shown that productivity improvements led by technology (not just RPA, but cognitive AI too) are possible across a bank’s processes.

Together, these two factors are not just impacting the cost structure of banks, but also creating vastly superior customer experiences. I see three big areas of opportunity for transforming banking operations:

1. Self-service and straight-through processing: Processes were defined with a ‘risk and control’ mindset that focused on minimizing operational risk at the cost of speed and flexibility. As the digital natives from banking to retail have demonstrated, it is possible to reimagine the processes with customer experience as the primary goal, and then define technology-enabled risk guard-rails for exception processing. This is possible, especially because customers increasingly prefer digital self-service options.One success story here is that of cheque deposits: this process has moved from being a manual, in-branch activity to a near- flawless straight-through processing experience. Many more such opportunities do exist – it will take a rigorous approach to identify them, define the SLAs that could be impacted, and drive the solutions to enable them.
2. Intelligent automation: Task automation through RPA (e.g., bots that auto-populate forms) has, by and large, run its course and delivered point efficiency gains. The next wave of automation is a combination of AI-enabled intelligent task automation (e.g., document scanning to extract data for a variety of document verification activities) and workflow-enabled end-to-end process automation (e.g., customer onboarding, verification and disbursement of consumer loans). These not only create great customer experiences by reducing the time and effort required but can also free up time for relationship managers to engage with their customers in deeper and more meaningful ways.
3. Redefinition of operating processes: This would be to take a more dynamic and flexible approach in response to a rapidly changing macro-environment and its impact on the customer base, as evidenced by the events of 2020. A leading consumer bank is relooking its consumer portfolio-level risk assessment in light of the economic chaos triggered by the Covid-19 pandemic. Instead of relying on payment history alone to assess risk of default, the bank is looking to glean underlying behavioural factors from data points like contact history – not just at the number of contacts and contact channels, but also the contact sequences – to assess the default risk scores on a daily basis. This not only requires building AI models that can supplement the risk-scoring models but also means suggesting interventions for the field agents.

Nitin’s conclusion:

I believe the Digital Transformation 2.0 journey has just begun. Over the next few years, I expect to see a dramatic change in digital retail banking. In much the same way that computing technology with algorithmic trading and automation helped drive radical change in banking capital markets, technology, I believe, will be a key enabler in driving transformation in consumer banking too.