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Facing new competitors, banks need to make better use of data

When it comes to leveraging transformative technology, banks have been at it for years. From ATMs to cloud services, technology has been at the heart of banking. Moving early and moving in the right direction, banks know, is critical.

Yet today, they are facing an arguably more challenging situation than before, as technology has enabled digital-native rivals to muscle in, while consumer preferences have changed as digital habits evolve.

The COVID-19 pandemic crystallised this even more clearly. During lockdowns, transactions had to be carried out digitally. Loans had to be approved or denied based on an online interaction. 

For traditional banks this means they must leverage technology to not only deliver products, but to ensure they are providing customer experiences that keep people coming back and avoid losing out to new digital-native entrants.

Deepening customer relationships

As they look to a recovery in 2021 and beyond, one of the most urgent imperatives for banks is to make much better use of data analytics to deepen the relationships they have with customers. This will be critical to building new experiences that solve problems, delight customers and ultimately convince customers the bank is still the centre of their financial lives.

To get there, banks need all the data that is relevant, to anticipate and deliver what a customer wants based on what they know about the customer's past. This means understanding and remembering a customer’s financial life, say, through starting their first job (getting a credit card) to applying for significant financial assistance (getting a home loan) and starting a family (insurance for the young ones). 

Data based on previous transactions can tell a bank what the customer needs and wants before they know it, facilitating more customised offerings. In other words, using data to look forward into the future and deliver a solution that cuts through the complexity of multiple products.

It also helps manage risks. With the greater visibility data affords, banks can better assess if a lender should be extended more credit, based on their repayment history as well as their regular expenditure, for example. 

Moving at scale

It’s true that many banks already drive the use of data and artificial intelligence (AI) in a variety of scenarios. Some already offer the kind of insights that AI can deliver, such as personalised tips on better spending habits based on the credit card expenditure of a customer each month.

But there is a lot more to do. For one, a bank has to look to incorporate more meaningful data to make better analyses of the customers they serve. They need to use AI at scale, as consultancy firm McKinsey advises.

Unfortunately, data is still often kept in silos. One department selling a product — say, a home loan — to a customer should not be closing off that data from another department offering another product, like an investment service.

At the same time, banks have to also look beyond the data on their own premises to get a bigger picture of a customer they serve. 

In India, for example, Google Maps can offer an idea of the amount of cattle that a farm can raise while weather patterns help a bank understand the possible yield of a piece of farmland before a loan is approved.

Many other challenges facing banks today already require the use of AI. Fraud prevention and anti-money laundering efforts, for example, can make use of more data to confirm if a suspicious transaction really is one. 

The machine learning behind these efforts needs to improve based on risk-based scoring rather than one based on strict rules. For example, travel patterns changed during 2020, so AI needs to better understand today’s risk by adding other variables into the calculations.

Being agile

All this calls for a rethink of how a bank’s infrastructure is going to serve its needs in the years ahead. Can legacy setups provide the scalability and agility they need to manage and leverage the data coming in? 

Until recently, you could argue that a bank’s stability was valued above other factors like convenience, so it would not face the same disruption in other sectors. That cannot be said today, as consumer habits change and convenience becomes a top consideration.

Banks have to move as fast as their digital-native rivals. While it’s true, moving a well-set system is hard – there are regulatory compliance and other considerations – the tide is changing, and we’ll see more and more critical financial services moving to the cloud in the years ahead.

Just as they quickly took up new technologies to ride waves of change in the past, banks will need all the tools at hand to harness data and make a difference with it.

 

 

52020

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