Application of Ensemble Models in Credit Scoring Models

Anjali Chopra · Priyanka Bhilare

42 citations0/0 files storedBusiness Perspectives and Research

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

Loan default is a serious problem in banking industries. Banking systems have strong processes in place for identification of customers with poor credit risk scores; however, most of the credit scoring models need to be constantly updated with newer variables and statistical techniques for improved accuracy. While totally eliminating default is almost impossible, loan risk teams, however, minimize the rate of default, thereby protecting banks from the adverse effects of loan default. Credit scoring models have used logistic regression and linear discriminant analysis for identification of potential defaulters. Newer and contemporary machine learning techniques have the ability to outperform classic old age techniques. This article aims to conduct empirical analysis on publically available bank loan dataset to study banking loan default using decision tree as the base learner and comparing it with ensemble tree learning techniques such as bagging, boosting, and random forests. The results of the empirical analysis suggest that the gradient boosting model outperforms the base decision tree learner, indicating that ensemble model works better than individual models. The study recommends that the risk team should adopt newer contemporary techniques to achieve better accuracy resulting in effective loan recovery strategies.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 42 citations exist upstream.

References

2000Neural network credit scoring models 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress 2007Using neural network ensembles for bankruptcy prediction and credit scoring 2011CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE 2007A neural network approach for credit risk evaluation 2004Neural network ensemble strategies for financial decision applications 2008An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring

Cited by

2022Utilization of artificial intelligence in the banking sector: a systematic literature review 2024Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management 2021Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization 2023An Ensemble Machine Learning Approach for Forecasting Credit risk of Loan Applications 2019

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.