Machine Learning in Finance: Predictive Modeling and Risk Management

EasyChair Preprint 12359

Abstract

This paper presents an overview of machine learning algorithms' applications in finance, with a focus on predictive modeling and risk assessment. Various machine learning methods, including regression, classification, clustering, and deep learning, are examined in their roles in forecasting stock prices, detecting fraudulent activities, evaluating credit risk, and optimizing investment strategies. Furthermore, the challenges and opportunities inherent in deploying machine learning in finance are discussed, encompassing issues such as data quality, model interpretability, regulatory compliance, and ethical considerations. Finally, potential future research directions and opportunities for incorporating machine learning techniques into financial decision-making processes are highlighted to enhance efficiency, accuracy, and risk mitigation.

Keyphrases: Finance, learning, machine

@booklet{EasyChair:12359,
  author    = {Julia Anderson and Nick Smith},
  title     = {Machine Learning in Finance: Predictive Modeling and Risk Management},
  howpublished = {EasyChair Preprint 12359},
  year      = {EasyChair, 2024}}