Customers are not the only ones who share data with a bank when they interact with a banking touchpoint, ATMs and ASSTs also emit hundreds of thousands of data points per day. So, a financial institution that manages a network of hundreds or more of these machines, holds a lot of data that could be sifted through and usefully used. And yet too many banks sit on this data and do little with it.
This is a huge waste that must be rectified. Close to one in two banks already acknowledge that data analytics is one of the most effective ways of staying ahead of the competition. Indeed, data harvested from their self-service banking network is a key way for banks to improve offerings, lower operational costs and increase profitability. Not only that, but this data can also create insights into how customers are using that machine and how the ATM itself is performing.
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Collecting and analysing data from ATMs is essential for how banks improve their omnichannel approach to customer engagement. Omnichannel banking has driven the need for tools which provide actionable insights into performance across various banking channels and services. Those ATM data insights complete the picture of an integrated customer experience across all touchpoints, including in-person, online and mobile apps.
One of the reasons that this has not happened so far is because traditional methods of managing self-service channels are more focused on operational and cost management rather than the broader view of the value that can be gained. The banking and financial services industry must look towards solutions which include real-time performance monitoring and predictive analytics capabilities. But how can financial services organisations become more data-led?
Unlocking the power of data analytics
Every banking touchpoint, no matter if it is in person or online, provides enormous amounts of data which can be leveraged. The easy part is collecting the data, but the challenge is what to do with it. Converting the data into valuable insights requires more intelligent processes. This involves a strong data management and analytics process, as well as complete mapping of self-service banking channels, including ATM and ASST networks.
One part of deriving value from these banking channels is real time insights into how each part of the network is performing. This information should be easily accessible across the organisation. It will enable financial services institutions to better identify if there are any inefficiencies in the network, and if any touchpoints are not working.
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By GlobalDataThrough identifying issues, analytics tools can support banks in creating more efficient operations and enhance service quality. Here, financial services institutions can take steps to fix the issue with minimal disruption to ensure seamless customer service.
Further, real time monitoring of endpoints enables banks to see any attacks on their services or machines from threat actors. Financial services organisations can collect data from the sensors positioned around the ATM to have greater insight into how people, not only customers, are interacting with the machines themselves. Sometimes the sensors will pick up harmless interactions with the ATM, but other times the data could be an indicator that a threat actor was trying to take money out of the machine. So, collecting, sorting and analysing data from sensors around the machine can protect the bank itself, and mitigate harmful threats.
Through continuous monitoring and predictive capabilities, financial services institutions can forecast the future performance of each channel. In this, banks can apply precise parameters depending on the strategic indicators and current business objectives to understand how each self-service channel is expected to perform in a specific situation.
The transformational impact of advanced analytics
As operational costs for running ATM networks continue to rise, specialist ATM data analytics can support smarter data forecasting to optimise cash management. Understanding how each machine is performing delivers insights beyond knowing when and where to replenish the cash cartridge.
Real time data tracking can provide invaluable insights into customer behaviour that are needed for fundamental service performance improvements. This can include how the ATMs self-service interface is working, or not. Banks can collect data based on the transaction flow, which could indicate if there is a more intuitive way for customers to complete their transactions. Not only does this enable financial services institutions to see where network inefficiencies and challenges lie, but also fosters a culture of continuous improvement.
Through banks understanding how customers are using ATMs, they will be able to better forecast how much cash the endpoint will need. This will create efficiencies around delivering cash to machines that require it, therefore reducing their Cash-In-Transit (CIT) movements, as well as security and insurance costs. The ATM is a vital touch point for a full omnichannel service offering, so using data in an intuitive way will make the endpoint itself and the network more user friendly.
When the performance of the self-service banking channel network is being constantly monitored and reviewed, strategies can be altered based on the insights provided. As such, financial services institutions can adapt their networks to how customer needs are evolving and meet demands that much quicker, and flex to changing market conditions.
In order for banks to do this, it is crucial for them to leverage a dynamic, industry-specific banking business analytics platform which is made available and easy to use for everyone within the business. This platform should also seamlessly integrate into their current systems. It is important for this platform to be able to collect and analyse data in real time from a number of different self-service areas in the network, including in-person ATMs and online banking methods, and convert this into usable insights.
Further, being able to use the data to forecast trends enables greater investment in those up-and-coming areas. However, it is key for financial services institutions to get their data right, before they use these insights to develop products and services. Therefore, banks can get the most out of their self-service channels and improve their market competitiveness and customer service, and at the same time, lower their operational costs.
As the banking industry continues to digitally transform, financial services institutions must place a greater focus on how they can remain competitive. Understanding how self-service channels are performing across their network, especially with the drive towards omnichannel banking, it enables financial institutions to focus on how they can improve their services and better meet customer demands. Through leveraging real time insights which banks gain from ongoing data collection and analysis of network performance, it is a transformational approach to self-service banking. Data analytics will not only improve current strategies, but will be a driving force for continued success and market differentiation.
Brendan Thorpe is Customer Success Manager at Auriga
