Paper: arXiv 2311.10799

Authors: Minati Rath, Hema Date

Abstract

In many real-world datasets, rows may have distinct characteristics and require different modeling approaches for accurate predictions. In this paper, we propose an adaptive modeling approach for row-type dependent predictive analysis(RTDPA). Our framework enables the development of models that can effectively handle diverse row types within a single dataset. Our dataset from XXX bank contains two different risk categories, personal loan and agriculture loan. each of them are categorised into four classes standard, sub-standard, doubtful and loss. We performed tailored data pre processing and feature engineering to different row types. We selected traditional machine learning predictive models and advanced ensemble techniques. Our findings indicate that all predictive approaches consistently achieve a precision rate of no less than 90%. For RTDPA, the algorithms are applied separately for each row type, allowing the models to capture the specific patterns and characteristics of each row type. This approach enables targeted predictions based on the row type, providing a more accurate and tailored classification for the given dataset.Additionally, the suggested model consistently offers decision makers valuable and enduring insights that are strategic in nature in banking sector.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper introduces ‘Row-Type Dependent Predictive Analysis’ (RTDPA), which essentially amounts to segmenting data by category (e.g., personal vs. agriculture loans) before training standard machine learning models. The mathematical framework is rudimentary, and the empirical validation lacks rigorous backtesting or statistical significance testing despite claiming high precision.

Research Flowchart

  flowchart TD
    A[Research Goal: RTDPA Framework for Credit Risk] --> B[(Bank Dataset: Personal & Agriculture Loans)]
    B --> C[Tailored Preprocessing & Feature Engineering]
    C --> D[Adaptive ML & Advanced Ensemble Models]
    D --> E[Row-Type Dependent Predictions]
    E --> F[Key Findings: ≥90% Precision & Strategic Insights]

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0 of 89 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.