Jihong Kim, Nammee Moon, Journal of Information Processing Systems Vol. 20, No. 6, pp. 812-826, Dec. 2024  

https://doi.org/10.3745/JIPS.01.0110
Keywords: Big Data Processing and Analysis, Credit Risk Prediction, Deep Learning, Explainable AI
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Abstract

The modern financial industry demands rapid decision-making based on diverse information from dynamic environments. Predicting outcomes from such data is complex due to rapid shifts influenced by numerous factors. Despite advancements in artificial intelligence technology that offer sophisticated analytical models, accurately predicting outcomes and providing sufficient justification for these predictions remain challenging, particularly with fragmented model constructions. In this paper, we propose a novel approach for efficient processing of available public personal credit data, deriving new analysis elements, and comparing prediction interpretations. Specifically, we develop 11 prediction models that can be categorized into two types: data image transformation and time-series transformation. The models undergo standardization, preprocessing, and cross-validation for optimization, with their predictive performances compared and validated. Models leveraging convolutional neural network (CNN) and convolutional neural network-long short-term memory (CNN-LSTM) architectures demonstrate strong performance across both categories. To fully interpret the classification process, SHAP is applied to compare and explain the prediction results for each model type.

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