AI & Digital Twins for Financial Risk & Asset Optimization
summary
An end-to-end AI-powered digital twin framework is built for real-time financial risk and asset optimization, ingesting transaction and market streams, using Random Forest, LSTM, SVM and autoencoder models and reinforcement learning for policy updates. Random Forest leads with RFC at 95.5% accuracy and LSTM nearly matches on recall; anomaly detection uses autoencoders. Deployment claims a 25% reduction in unplanned downtime, 18% lower maintenance, 20% higher asset utilization and a 145% ROI, so the system reports clear operational and financial gains.
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chapters (11)
- 0:00Introduction to AI in Finance
- 0:41Understanding Digital Twin Technology
- 1:11Integrating AI and Machine Learning
- 1:34Framework for AI-Powered Digital Twins
- 2:52Evolution and Application of Digital Twins in Finance
- 4:46Challenges in Current Financial Systems
- 6:16Data Collection and Pre-Processing
- 7:59Proposed Framework for Financial Risk Management
- 11:34Model Performance and Evaluation
- 15:39Cost-Benefit Analysis
- 16:41Conclusion and Future Outlook
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