Machine Learning Applications in Finance: Predictive Analytics and Risk Management

EasyChair Preprint 12239

Abstract

This paper explores the diverse applications of ML techniques in finance, focusing particularly on predictive analytics and risk management. In predictive analytics, ML algorithms are employed to forecast asset prices, identify trading signals, and optimize investment strategies. Techniques such as regression, decision trees, random forests, and neural networks are leveraged to analyze historical data, recognize patterns, and make accurate predictions about future market movements. Furthermore, ML models are capable of adapting to changing market conditions and learning from new data in real-time, enhancing their predictive capabilities. By analyzing large datasets and identifying complex patterns, ML models help financial institutions in credit scoring, fraud detection, and portfolio optimization.

Keyphrases: detection, fraud, portfolio

@booklet{EasyChair:12239,
  author    = {Kurez Oroy and Patrick Evan},
  title     = {Machine Learning Applications in Finance: Predictive Analytics and Risk Management},
  howpublished = {EasyChair Preprint 12239},
  year      = {EasyChair, 2024}}