The banking industry has had its share of transformational periods over the past decade, from the rise of online banking to the explosion of apps and digital-only financial services that challenged traditional business models. Then the global COVID pandemic ushered in a brand new wave of disruption, further accelerating the shift to digitization.
But with complexity comes opportunity. For the banking industry, this period opens the door for a reinvention of traditional models to grab the attention and loyalty of customers, employees, and the market. This requires a mindset shift for bankers, who otherwise risk losing people and profits to the fintech companies and challenger banks that are comfortable changing the banking landscape.
So how can banks embrace this necessary transformation while also managing the balance sheet and mitigating risk? Breaking down operational silos is a solid first step but embracing strong data and advanced intelligence is the real key.
With inflation and continued global disruptions driving market uncertainty, banks need to be more forward-looking than ever before. Stability in banking is critical, and retail banks in particular are being tested by their ability to analyze variables, make performance assumptions, and adjust plans based on those assumptions to maintain continuity for their customers, employees, and portfolios.
Data and intelligence can help banks monitor core customer balance sheets and retain a clear view of their cash flows. For instance, banks can leverage predictive data to model out potential credit loss calculations and develop actionable plans based on variables like changing default rates and asset staging. This information can be used to anticipate debt covenants and more accurately assess and mitigate exposure in the short and long term.
Given the rapid shifts, leveraging predictive insights to build different ‘what-if’ scenarios will enable banks to be more dynamic and agile in the execution of their plans in real-time. Actionable, predictive data can also help banking CFOs stay on top of their own solvency and liquidity with real-time insight into key performance indicators. Not only will this insight help banks maintain their own financial stability and make key decisions on lending activities, but it will also allow them to better support their clients as the market continues to shift and evolve, putting them ahead of the competition.
Data-driven scenario analysis tools can provide banking industry CFOs the visibility into overall business health they need to make decisions on a variety of fronts. Globally, for example, retail banks have been rethinking their distribution networks to optimize capacity across channels – from the role of physical branches to preparing for the new future of work and planning out the pace of digital-first product transformation.
In addition to historical trend data, external and third-party signals can help banks anticipate market, consumer, and business trends and events to help with all of that. With built-in intelligence – like artificial intelligence and machine learning algorithms in forecasting, for instance – banks can use the mix of internal and external data to predict the potential impact of trends or events on their business, and adjust plans accordingly.
Consider a bank focused on both short and longer-term contact center forecasting. Statistical forecasting and scenario planning capabilities can help unlock insights into the root causes of issues. That can help mitigate risk, ensure continuity and drive customer retention through better customer service. In branch planning, such an approach can help with P&L forecasting across the branch network to support strategic modeling of the future of the branch network – openings, closures and repurposing – aligned with the bank’s location strategy.
The bottom line benefit: Being able to anticipate and proactively respond to market changes so the organization can look forward – and act – with confidence.
While it’s not clear what the next 12, six or even three months will look like, some level of volatility is likely a guarantee. For banks, this economic complexity – coupled with industry disruption at the hands of new entrants – puts added pressure on transformation.
Banks that can tap into predictive, real-time data and embrace intelligence capabilities – as opposed to relying on traditional systems and processes – will be better positioned to adapt strategically to change, maintain continuity, and provide higher levels of support to their customers to drive loyalty without risking liquidity.
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