Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector
This work addresses credit risk analysis for banks by providing a more accurate classification method, though it is incremental as it applies existing algorithms separately to different data categories.
The paper tackles the problem of predicting credit risk for different loan types in banking by proposing an adaptive modeling framework (RTDPA) that tailors machine learning models to distinct row types, achieving precision rates of at least 90% across all predictive approaches.
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.