Machine Learning for Personalized Financial Planning on Cloud

Abstract

In the modern digital landscape, financial systems face increasing threats from sophisticated cyber 
attacks while striving to offer personalized financial planning services to clients. This study 
presents a comprehensive approach that leverages machine learning-enhanced cloud solutions for 
real-time cyber-attack mitigation and personalized financial planning. By integrating anomaly 
detection models, such as autoencoders and Support Vector Machines (SVM), with cloud-native 
security frameworks, the proposed system identifies and responds to potential threats in real-time, 
reducing the average threat detection time to less than 3 seconds. Concurrently, the system 
employs machine learning algorithms, including collaborative filtering and reinforcement 
learning, to deliver tailored financial advice by analyzing user behavior, preferences, and market 
trends. The integration of these components within a scalable cloud environment ensures seamless 
operation and enhances both security and user experience. Experimental results demonstrate a 95% 
reduction in successful cyber attack incidences and a 40% increase in user satisfaction with 
personalized financial services. This research provides a viable pathway for financial institutions 
to simultaneously fortify their security posture and enhance client engagement through AI-driven 
solutions.