Paper: arXiv 2407.12044

Authors: Aditya Saxena, Dr Parizad Dungore

Abstract

Credit ratings are becoming one of the primary references for financial institutions of the country to assess credit risk in order to accurately predict the likelihood of business failure of an individual or an enterprise. Financial institutions, therefore, depend on credit rating tools and services to help them predict the ability of creditors to meet financial persuasions. Conventional credit rating is broadly categorized into two classes namely: good credit and bad credit. This approach lacks adequate precision to perform credit risk analysis in practice. Related studies have shown that data-driven machine learning algorithms outperform many conventional statistical approaches in solving this type of problem, both in terms of accuracy and efficiency. The purpose of this paper is to construct and validate a credit risk assessment model using Linear Discriminant Analysis as a dimensionality reduction technique to discriminate good creditors from bad ones and identify the best classifier for credit assessment of commercial banks based on real-world data. This will help commercial banks to avoid monetary losses and prevent financial crisis

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: This paper applies standard standard machine learning techniques (LDA for dimensionality reduction alongside standard classifiers) to UAE commercial banking data. The mathematical depth is basic, relies heavily on existing standard methodologies, and the dataset descriptions present several inconsistencies (e.g. sample size discrepancies in section 4).

Research Flowchart

  flowchart TD
    A[Goal: Predict Credit Default & Improve Assessment Precision] --> B[(Input: Real-World UAE Commercial Banks Financial Data)]
    B --> C[Dimensionality Reduction: Linear Discriminant Analysis]
    C --> D[Machine Learning Classification Models]
    D --> E[Model Validation & Performance Evaluation]
    E --> F[Outcome: Identification of Best Classifier to Discriminate Good vs. Bad Creditors]
    F --> G[Impact: Reduced Monetary Losses & Financial Crisis Prevention]

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0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.