Strategy, Data, Feb 15, 2021

How can retail banking adapt to the new normal to keep up with customer demand?

Bhavna Parwani

Today’s retail banks are undergoing historic levels of change. The effects of the Covid-19 pandemic has caused a shift in people’s living and working arrangements, resulting in a change to their financial behaviours and expectations. Existing banking systems, such as payment processing and short- term lending, will need to significantly adjust to reflect changing consumer requirements, with a greater focus on data & analytics solutions.

Banks now operate in a world of constant change across all layers of their enterprise architecture, with basic assumptions about how banks operate being challenged by rapid and complex change.

Retail banks need a business-led data strategy that embraces and enables constant reinvention, facilitating new technology solutions and customer experiences to be delivered at record pace.

New customers, new bank

According to research by McKinsey, up to 80% of households have suffered significant decreases in income and savings, whilst up to 60% fear for their long-term employment status as a result of the Covid-19 pandemic. This has resulted in more people considering savings and investments as a way of providing financial security, as well as an increased desire to manage products more easily across financial institutions.

Customers want ‘tangible’ support in the form of payment holidays and adjusted credit terms, and banks are expected to be able to personalise their response by using rich data about their individual household conditions. In addition, digital payment volumes have accelerated, with banks now having to integrate with more points of sale and process a higher number of online transactions.

The new consensus is that these changes will not be short lived. So what does this mean for banks?

Read next: How banks can achieve the promise of open banking with a data mesh architecture

Responding to change

According to Deutsche Bank, only 29% of banks report getting real business value from data. The gap between appetite and execution highlights the need for a clear data strategy which will enable the following:

  • Single customer view: Consumers now have transient relationships with many institutions and so banks need to integrate with external parties to build a complete customer view.

  • Credit decisioning: New techniques like psychometric testing of applicants for risk management is being introduced into credit decisioning processes by new FinTechs.

  • Payment fraud: Monitoring online behaviours to prevent fraudulent behaviour will be increasingly critical in a digital world and banks need to invest in these new capabilities.

Banks therefore need to improve customer analytics in order to tailor financial support and enable greater cross-selling, integrating a wider variety of data points into their data architecture.

Meeting the demands of a new type of customer, integrating a greater variety of payment ‘points’, and embedding advanced analytics into customer interactions requires rapid delivery and adoption. Banks need to understand that change is no longer a long-term concept - it is the ‘new normal’.

The need for greater speed and agility

Banks need to embrace data & analytics capabilities to enable constant innovation, adopting a mindset that assumes that every process, product, and channel will need to be improved over time.

Whilst recent regulations such as BCBS 239 have driven improvements in data & analytics, many banks are still failing to achieve full compliance, heightening the need for greater agility. Only by relegating the concept of a long- term BAU can solutions be delivered at pace across all layers of the enterprise architecture. Introducing a new data strategy will reveal how data can power this radical change in mindset.

The need for a data strategy

CDOs must recognise that whilst they do not own the business problem or its requirements, they must enable the organisation to make use of its data, and this should also account for undefined future use cases.

A data strategy defines why the business needs data & analytics capabilities, focuses investment priorities, and explains how the business will transition to the ‘new normal’. The data strategy should enable constant change and innovation led by front-line business users, and the CDO should use it to explain new operating models that will support this disruption:

  • Agile delivery: Putting in place new delivery models that combine business, data, and technology talent to imagine and prototype solutions based on new business needs.

  •  Federated models: Delivery teams are empowered to deliver under business sponsorship to ensure that solutions directly align to user requirements, which are supported by data governance.

  • Continuous deployment: Change management is used to drive adoption of new solutions into the front line and create continuous mechanisms for feedback and improvement.

The data strategy should reimagine how capabilities like data management can be delivered in new, decentralised ways, as well as explain how flexible, rapidly deployed technologies such as cloud will support the ongoing delivery of data & analytics solutions across business and IT.

In a nutshell

A new type of customer requires a new type of bank - one that can embraces disruption through data. CDOs need to lead their organisations through this journey, educating and engaging the business with a data strategy that explains how new capabilities and delivery models will be delivered.

A bank’s data strategy should enable continuous improvement to address the needs of customers in an evolving industry, and explain how improvements will be realised across the enterprise architecture. CDOs should use their data strategy to lead their organisation on its journey to the ‘new normal’.

Read more

Read more:
How wealth managers can use data to keep focused on what really matters: Achieving digitisation success

Podcast: Is brand loyalty a dying trait in banking? 
Podcast: Is brand loyalty a dying trait in banking? - Part two
How a data 'A-Team' can help overcome systematic changes in banking
Understanding data and analytics maturity: A focus on Strategy and Alignment

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