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SMART: Structured Missingness Analysis and Reconstruction Technique for credit scoring.

Seongil Han1, Haemin Jung2, Paul D Yoo3

  • 1Department of Computer Science, University of Suwon, Hwaseong, South Korea.

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|April 29, 2025
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Summary
This summary is machine-generated.

This study introduces SMART, a new method for handling missing data in credit scoring. SMART improves Probability of Default (PD) estimation, enhancing credit risk management models.

Keywords:
Credit scoringGenerative adversarial imputation networksImputationMissing valuesRandomized singular value decomposition

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Area of Science:

  • Financial Risk Management
  • Machine Learning in Finance
  • Data Science

Background:

  • Basel Accord mandates internal models for credit risk components like Probability of Default (PD).
  • Incomplete datasets hinder accurate PD estimation and credit scoring model performance.
  • Traditional missing data methods (deletion, mean imputation) are often inadequate.

Purpose of the Study:

  • To propose a novel imputation technique, SMART (Structured Missingness Analysis and Reconstruction Technique), for credit scoring datasets.
  • To address challenges posed by incomplete data in accurately estimating PD.
  • To enhance the robustness of credit risk management.

Main Methods:

  • SMART employs a two-stage approach: data normalization/denoising via randomized Singular Value Decomposition (rSVD) and imputation using Generative Adversarial Imputation Networks (GAIN).
  • The method focuses on structured missingness analysis and reconstruction.
  • GAIN leverages generative adversarial networks to model data distributions for precise imputation.

Main Results:

  • SMART significantly outperforms existing state-of-the-art imputation methods.
  • Demonstrated substantial improvements in imputation accuracy across high missing data scenarios (20%, 50%, 80%).
  • Achieved accuracy improvements of 7.04%, 6.34%, and 13.38% in respective missing data contexts.

Conclusions:

  • SMART offers a significant advancement in managing incomplete credit scoring datasets.
  • The technique leads to more precise Probability of Default (PD) estimation.
  • Enhanced PD estimation strengthens the overall reliability of credit risk management models.