专题:Financial Distress and Bankruptcy Prediction

This cluster of papers focuses on the development and comparison of machine learning models, including neural networks, support vector machines, and ensemble methods, for predicting bankruptcy and assessing credit risk. The research explores various techniques for financial distress prediction, credit scoring, and risk assessment in both corporate and consumer contexts.
最新文献
From Social Engineering to Financial Fraud: A Multi-Agent LLM Approach to Vishing Detection

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Design and Evaluation of Quantitative Investment Strategies Using Reinforcement Learning and Multi-Factor Models

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An Intelligent Approach for Machine Downtime Prediction Using Ensembled Machine Learning Models

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Uncovering cross-organizational risk patterns: a machine learning approach to predicting financial fraud via chain leader attributes

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Multi-Hop Relational Modeling for Credit Fraud Detection via Graph Neural Networks

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Erudit AI SaaS: an artificial intelligence tool based on RoBERTa for classifying employee engagement

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AI-Based Causal Reasoning over Knowledge Graphs for Data-Driven and Intervention-Oriented Enterprise Performance Analysis

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Evaluating model concordance in a multi-model binary scoring framework: feature-specific and sampling-driven agreement analyses using Kendall’s tau rank correlation for credit risk assessment

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A Residual-Regulated Machine Learning Method for Non-Stationary Time Series Forecasting Using Second-Order Differencing

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Estimating project cost of equity using explainable ensemble learning: An empirical assessment of annual report readability

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近5年高被引文献
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation

review Full Text OpenAlex 433 FWCI72.5202

Artificial intelligence and machine learning in finance: A bibliometric review

review Full Text OpenAlex 403 FWCI110.848

Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review

article Full Text OpenAlex 345 FWCI42.7516

A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining

article Full Text OpenAlex 327 FWCI54.767

A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

article Full Text OpenAlex 299 FWCI36.783

Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy

article Full Text OpenAlex 266 FWCI73.819

The class imbalance problem in deep learning

article Full Text OpenAlex 246 FWCI27.762

Corporate finance risk prediction based on LightGBM

article Full Text OpenAlex 212 FWCI53.8027

Enhanced Credit Card Fraud Detection Model Using Machine Learning

article Full Text OpenAlex 196 FWCI25.5399

Review of Machine Learning Approach on Credit Card Fraud Detection

article Full Text OpenAlex 193 FWCI24.8459