Deep Learning for Predictive Maintenance Revolutionizing Asset Management in Finance

As a result, predictive maintenance is a critical requirement in finance to reduce downtime, minimize expenses, and ensure asset integrity. Traditional frameworks for asset management are characterized by a reactive approach that leads to business interruptions and increased costs. As a solution to these concerns, a learning-based system for predictive asset management is described in the paper. The device is built on data science models like deep learning, which draw data from their older reporters kept in economic databases, preprocess the data to eliminate noise and missing terms, then use feature engineering to find crucial insights. The system uses modern deep learning technologies like Long short-term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict asset utilization and calculate potential faults. Finally, it supervises processes in real-time and updates the solution whenever new data is received, sending notifications to the installation to address any logical fixations. The results show substantial benefits, with downtime reduced by 45%, maintenance expenditures reduced by 40%, and asset uptime increased by 50%. The accuracy of anomaly detection will increase to 85%, prediction model accuracy will improve, and the early warning lead time will be extended to 72 hours, hence boosting total asset reliability. The transformational technique optimizes financial asset control while increasing operational efficiency and risk avoidance.