Machine Learning in Financial Crisis Prediction: A Survey

By Wei-Yang Lin, Ya-Han Hu, Chih-Fong Tsai. 2012

312 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Bernardete Ribeiro
2. An Chen
3. Ningshan Chen
4. Qinghua Huang
5. Kai-Yu He
6. Victor-Emil Neagoe
7. Adrian-Dumitru Ciotec
8. Hui Li

In Artificial Intelligence

1. Phillip E. Pope
2. Bernardete Ribeiro
3. An Chen
4. Samyadeep Basu
5. George Danezis
6. Sung-Bae Cho
7. Ningshan Chen
8. Chang-Seok Lee

In Management Science and Operations Research

1. Phillip E. Pope
2. Bernardete Ribeiro
3. An Chen
4. Samyadeep Basu
5. George Danezis
6. Sung-Bae Cho
7. Ningshan Chen
8. Qinghua Huang

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, neutral): “[93] surveyed the financial crisis prediction studies based on ML models.”
• Deep Convolutional Neural Networks Versus Multilayer Perceptron for Financial Prediction (2018, methodology): “INTRODUCTION The banks are aware of various risks [1], [2], [3].”
• Learning Universal Adversarial Perturbations with Generative Models (2017, background): “Machine Learning models are increasingly relied upon for safety and business critical tasks such as in medicine [23], [29], [39], robotics and automotive [27], [31], [38], security [2], [17], [36] and financial [13], [18], [34] applications.”
• Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples (2017, background): “Machine Learning models are increasingly relied upon for safety and business critical tasks such as in medicine [24], [31], [40], robotics and automotive [29], [33], [38], security [2], [16], [37] and financial [12], [17], [35] applications.”
• Financial credit risk assessment: a recent review (2015, methodology): “Lin et al. reviewed the development of state-of-the-art machine learning techniques for bankruptcy prediction and credit scoring from the year 1995 to 2010 (Lin et al. 2012).”
• Predicting the success of bank telemarketing using deep convolutional neural network (2015, methodology): “There are 130 papers with 19 kinds of classifiers for predicting a financial crisis [1].”
• Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, background): “[74] made a review of FDP literatures between 1995 and 2010.”
• Influence Functions in Deep Learning Are Fragile (2020, background): “language processing [7], high-stakes applications such as medicine [8] and finance [9], it has become particularly important to interpret deep model predictions.”