Credit Scoring

Credit scoring aims to predict the likelihood of loan default, enabling financial institutions to make informed lending decisions. Current research emphasizes improving prediction accuracy using ensemble methods combining tree-based models (like XGBoost and LightGBM) with deep learning architectures (like TabNet and neural networks), while simultaneously addressing ethical concerns such as bias and fairness through techniques like subgroup threshold optimization and distributionally robust optimization. This field is significant because advancements in credit scoring models directly impact access to credit, financial inclusion, and the overall stability of the financial system, with ongoing research focusing on explainability, handling missing data, and leveraging alternative data sources like network data and text analysis.

Papers

December 22, 2022

Benchmarking Machine Learning Models to Predict Corporate Bankruptcy
Emmanuel Alanis, Sudheer Chava, Agam Shah
Machine Learning Random Forest Credit Scoring Machine Learning Benchmark Financial Distress

December 15, 2022

Reinforcement Learning in Credit Scoring and Underwriting
Seksan Kiatsupaibul, Pakawan Chansiripas, Pojtanut Manopanjasiri, Kantapong Visantavarakul, Zheng Wen
Reinforcement Learning Active Exploration Novel Reinforcement Learning Credit Scoring Digital Action

December 12, 2022

Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Hué Sullivan, Hurlin Christophe, Pérignon Christophe, Saurin Sébastien
Machine Learning Model Application Proficiency Model Performance Predictive Performance Credit Scoring

November 30, 2022

Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance
Janos Borst, Thomas Wencker, Andreas Niekler
Text Classification High Quality Data Credit Scoring Climate Change Adaptation

November 2, 2022

A Machine Learning system to monitor student progress in educational institutes
Bibhuprasad Mahakud, Bibhuti Parida, Ipsit Panda, Souvik Maity, Arpita Sahoo, Reeta Sharma
Machine Learning Machine Learning System Credit Scoring Student Performance Progress Monitoring Learning Management System

September 21, 2022

Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring
Dangxing Chen, Weicheng Ye
Machine Learning Machine Learning Model Credit Scoring Neural Additive Model

September 19, 2022

Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions
Swati Tyagi
Machine Learning Machine Learning Model Explainable AI Credit Scoring Investment Decision

July 10, 2022

Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction
Han Yue, Steve Xia, Hongfu Liu
Transformer Based Multi Task Credit Scoring Rating Prediction Credit Rating

June 30, 2022

Discrimination in machine learning algorithms
Roberta Pappadà, Francesco Pauli
Practical Algorithm Machine Learning Algorithm Sensitive Attribute Group DIscrimination Credit Scoring Potential Bias

June 1, 2022

RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang
Multi Task Learning Multi Task Classification Task Biased Datasets ATAC Net Credit Scoring Missingness Pattern

May 21, 2022

Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring
Marc Schmitt
Deep Learning Machine Learning Artificial Intelligence Practical Algorithm Gradient Boosting Credit Scoring Credit Risk

December 25, 2021

A comparative study on machine learning models combining with outlier detection and balanced sampling methods for credit scoring
Hongyi Qian, Shen Zhang, Baohui Wang, Lei Peng, Songfeng Gao, You Song
Machine Learning Model Comparative Study Class Imbalance Outlier Detection Credit Scoring Balanced Sampling

December 19, 2021

Managing dataset shift by adversarial validation for credit scoring
Hongyi Qian, Baohui Wang, Ping Ma, Lei Peng, Songfeng Gao, You Song
Training Data Cross Validation Credit Scoring Dataset Shift Adversarial Validation

November 26, 2021

On the combination of graph data for assessing thin-file borrowers' creditworthiness
Ricardo Muñoz-Cancino, Cristián Bravo, Sebastián A. Ríos, Manuel Graña
Graph Data New Combination Credit Scoring Credit Decision Credit Scoring Model Credit Risk Model

November 17, 2021

A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS
Yusuf Efe
Model Training Long Term Cooperation Vertical Federated Learning Data Representation Credit Scoring Reference Microphone

Credit Scoring

Papers

Benchmarking Machine Learning Models to Predict Corporate Bankruptcy

Reinforcement Learning in Credit Scoring and Underwriting

Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance

A Machine Learning system to monitor student progress in educational institutes

Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring

Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions

Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction

Discrimination in machine learning algorithms

RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring

Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring

A comparative study on machine learning models combining with outlier detection and balanced sampling methods for credit scoring

Managing dataset shift by adversarial validation for credit scoring

On the combination of graph data for assessing thin-file borrowers' creditworthiness

A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS