A hybrid interpretable credit card users default prediction model based on RIPPER

Pu Xu · Zhijun Ding · Meiqin Pan

19 citations0/0 files storedConcurrency and Computation Practice and Experience

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

Summary With the vigorous development of the financial sector, financial risks are showing a tendency toward diversification, particularly regarding the customer credit risk of commercial banks. Therefore, the customer's credit risk is being considered by financial institutions, and a credit evaluating model has emerged as a result. Currently, research has concentrated on enhancing the precision of the model, ignoring the interpretability, which makes it difficult to apply in the industry. Compared to precision, studies related to the interpretable model are limited. In our previous work, we did not consider model operation time and stability. Therefore, this study proposes a hybrid model based on the RIPPER algorithm. First, according to the characteristics of credit card data sets, targeted special data pretreatment methods are proposed. Next, the RELIEF method for feature selection removes the redundant features and further improves the interpretability of the model. Then, to address the problem of the imbalanced distribution of credit card data sets, a synthetic minority class sampling algorithm is used to equalize the samples. Finally, default credit card users are predicted by taking advantage of the rules generated by the RIPPER algorithm. To test the performance of the model, we used Taiwanese credit card customer data for empirical research. We considered model accuracy and interpretability when comparing the proposed SPR‐RIPPER model with the existing mainstream models. The results of the experiments indicate that the proposed model achieves acceptable results. This study demonstrates that the proposed credit card user default prediction model, SPR‐RIPPER, has practical application value.

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 — 19 citations exist upstream.

References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 2000Neural network credit scoring models 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients 2010A comparative assessment of ensemble learning for credit scoring 2007Recent developments in consumer credit risk assessment 2013Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches 2014Credit scoring using the clustered support vector machine 2011Credit card churn forecasting by logistic regression and decision tree

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.