Artificial Intelligence in Finance: Predictive Analytics, Fraud Detection, and Risk Management in 2024
Home Research Details
Goutham Kacheru, Rohit Bajjuru, Nagaraju Arthan

Artificial Intelligence in Finance: Predictive Analytics, Fraud Detection, and Risk Management in 2024

0.0 (0 ratings)

Introduction

Artificial intelligence in finance: predictive analytics, fraud detection, and risk management in 2024. Explore AI's transformative impact on finance in 2024. Covers predictive analytics, fraud detection, and risk management with ML, NLP, credit scoring, and automated trading.

0
9 views

Abstract

AI is poised to be transformative across virtually all industries, and the financial sector has already experienced major impacts from AI in predictive analytics, fraud detection and risk management among others. This paper also describes the innovation of AI, machine learning and natural language processing (NLP) technologies and their availability in financial services in 2024. Its scope covers richer credit scoring models which harness predictive analytics to assess borrower performance, more sophisticated fraudulent activity detection frameworks that can identify suspicious transactions in real-time, and countless automated trading algorithms which can dynamically adapt to changing market behaviors. Moreover, Algorithms have also deployed in the way financial institutions are evaluating and handling second risk management; AIdriven Risk Management tools have been also there to facilitate decision making process for operational efficiency. We discuss these challenges, and also show how AI will be a crucial part of fundamentally transforming financial analysis from optimizing customer service interactions to stabilizing the economy.


Review

This paper, titled "Artificial Intelligence in Finance: Predictive Analytics, Fraud Detection, and Risk Management in 2024," presents a highly relevant and timely topic concerning the transformative impact of AI within the financial sector. The abstract clearly outlines an ambitious scope, aiming to cover critical applications such as predictive analytics for credit scoring, real-time fraud detection, and sophisticated automated trading algorithms. Furthermore, it touches upon AI's role in risk management, operational efficiency, and even the broader economic stability, positioning AI as a fundamental force in modern financial analysis and decision-making. The focus on 2024 suggests an up-to-date analysis of current technological capabilities and their immediate future implications. The abstract highlights several specific areas of innovation, including the deployment of machine learning and natural language processing (NLP) technologies. It details how these technologies facilitate richer credit scoring models for assessing borrower performance, enhance the identification of suspicious transactions, and enable dynamic adaptation in trading algorithms. The mention of AI-driven risk management tools to aid decision-making processes for operational efficiency is also a commendable focus. The paper intends to discuss associated challenges while simultaneously demonstrating AI's potential to optimize customer service and contribute to economic stabilization, underscoring a comprehensive view of AI's pervasive influence. While the abstract promises a broad and impactful discussion, it currently lacks specific details regarding the methodology employed to support these claims. Phrases like "describes the innovation" and "we discuss these challenges, and also show how AI will be a crucial part" are descriptive of intent but do not convey the depth or empirical foundation of the paper. To strengthen its contribution, the abstract would benefit from a clearer indication of whether this is a comprehensive review, a conceptual framework, a case study analysis, or an empirical study. Without a glimpse into the evidence or analytical approach, the claims of showing how AI will be transformative and discussing challenges remain high-level aspirations. Further specificity on the nature of the "challenges" discussed would also enhance the abstract's overall impact and clarity for potential readers.


Full Text

You need to be logged in to view the full text and Download file of this article - Artificial Intelligence in Finance: Predictive Analytics, Fraud Detection, and Risk Management in 2024 from Formosa Journal of Science and Technology .

Login to View Full Text And Download

Comments


You need to be logged in to post a comment.