DATA SCIENCE USE CASE IN BFSI
Banks often need to operate a large number of ATMs. It is quite possible that the ATMs can breakdown anytime and cause inconvenience to the customers as well as to the banks, equally. Data Science and AI technologies help in predicting the breakdowns of bank ATMs.
With technology advancing, many ATM manufacturers are claiming to be using Predictive Maintenance to reduce the downtime of ATMs. Banks and other financial institutions are leaning towards technologies like Data Science and Artificial Intelligence, and are able to predict when their ATMs might require maintenance and servicing.
We attempted this use case to understand and analyze how Data Science and AI technologies can help ATMs.
The role of the ATMs is getting increasingly important for banks in the age of customer satisfaction and omnichannel experience. From being a cash-dispensing machine, the ATMs of today have become kiosks offering financial solutions to customers and engaging them in various ways. In that context, the question that poses banks is: If your ATMs are down, what are the chances of your customers switching to your competitors?
In ATMs, tracking run- time status by generating system messages, error events, and log files can help in predicting impending failures. We can then apply Machine learning techniques to predict whether the system/machine works with respect to the error that the machine faced at any time on the day.
We use Data Science and Machine Learning models to predict whether the machine fails or works on a respective day if it faces an error.
Our predictive maintenance model could identify ATMs that would go downtime with 94% accuracy.
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