Failure Prediction Model for Predictive Maintenance

Abstract

As financial organizations strive to deliver superior omnichannel customer experiences, they are transforming their branch environments with latest digital technologies for ATMs, Branch platforms, self-service devices and other branch technologies. Simultaneously, mixing new with older, installed technologies from multiple vendors can create complex maintenance challenges. One could opt for each individual vendor’s solution, but this can add complexity and may not put the crucial needs of the customer first. To maintain a customer-centric approach that leads to a high-quality brand image, improved customer satisfaction and ultimately a better bottom line, there is a need for service-oriented, vendor focused approach on delivering an integrated maintenance and technical support strategy, so that concentration on customers can be accomplished. In this direction, predictive maintenance plays a very vital role in enabling financial organizations to drive their ATM and branch business effectively to create maximum impact through predictive maintenance leveraging predictive analytics and machine learning technologies. We propose a method and Machine Learning model that takes various input data and determines likelihood of failure at a device and its component level within a stipulated future time-period with certain accuracy and precision for financial clients.