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
A Hybrid Predictive Model for Employee Turnover: Integrating Ensemble Learning and Feature-Driven Insights from IBM HR Analytics

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Approaches for modelling the term-structure of default risk under IFRS 9: a tutorial using discrete-time survival analysis

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Audit fees in the mandatory joint audit setting: a comparative study between the European Union and the MENA region

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Enhanced Rotating Machinery Fault Diagnosis Using Hybrid RBSO–MRFO Adaptive Transformer-LSTM for Binary and Multi-Class Classification

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Strengthening corporate financial resilience: Comprehensive fraud examination report

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Impact of Sample Size on the Robustness of Machine Learning Algorithms for Detecting Loan Defaults Using Imbalanced Data

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Cloud-Based AI Solutions for Real-time Monitoring of E-commerce Compliance and Risk

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Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking

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Comparative Analysis and Optimisation of Machine Learning Models for Regression and Classification on Structured Tabular Datasets

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Preference disaggregation-based multiclass Mahalanobis-Taguchi system applied to medical insurance fraud

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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 407 FWCI71.8787

Artificial intelligence and machine learning in finance: A bibliometric review

review Full Text OpenAlex 392 FWCI109.8404

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

article Full Text OpenAlex 315 FWCI41.1127

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

article Full Text OpenAlex 313 FWCI55.4543

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

article Full Text OpenAlex 282 FWCI36.2159

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

article Full Text OpenAlex 256 FWCI71.8515

The class imbalance problem in deep learning

article Full Text OpenAlex 220 FWCI27.3099

Corporate finance risk prediction based on LightGBM

article Full Text OpenAlex 199 FWCI53.1584

Enhanced Credit Card Fraud Detection Model Using Machine Learning

article Full Text OpenAlex 186 FWCI24.8344

Review of Machine Learning Approach on Credit Card Fraud Detection

article Full Text OpenAlex 179 FWCI23.8684