Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910889915973632 |
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| author | Wu, Zongxiao Dong, Yizhe Li, Yaoyiran Shi, Baofeng |
| author_facet | Wu, Zongxiao Dong, Yizhe Li, Yaoyiran Shi, Baofeng |
| contents | This study explores the integration of a representative large language model, ChatGPT, into lending decision-making with a focus on credit default prediction. Specifically, we use ChatGPT to analyse and interpret loan assessments written by loan officers and generate refined versions of these texts. Our comparative analysis reveals significant differences between generative artificial intelligence (AI)-refined and human-written texts in terms of text length, semantic similarity, and linguistic representations. Using deep learning techniques, we show that incorporating unstructured text data, particularly ChatGPT-refined texts, alongside conventional structured data significantly enhances credit default predictions. Furthermore, we demonstrate how the contents of both human-written and ChatGPT-refined assessments contribute to the models' prediction and show that the effect of essential words is highly context-dependent. Moreover, we find that ChatGPT's analysis of borrower delinquency contributes the most to improving predictive accuracy. We also evaluate the business impact of the models based on human-written and ChatGPT-refined texts, and find that, in most cases, the latter yields higher profitability than the former. This study provides valuable insights into the transformative potential of generative AI in financial services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts Wu, Zongxiao Dong, Yizhe Li, Yaoyiran Shi, Baofeng Risk Management Computational Finance This study explores the integration of a representative large language model, ChatGPT, into lending decision-making with a focus on credit default prediction. Specifically, we use ChatGPT to analyse and interpret loan assessments written by loan officers and generate refined versions of these texts. Our comparative analysis reveals significant differences between generative artificial intelligence (AI)-refined and human-written texts in terms of text length, semantic similarity, and linguistic representations. Using deep learning techniques, we show that incorporating unstructured text data, particularly ChatGPT-refined texts, alongside conventional structured data significantly enhances credit default predictions. Furthermore, we demonstrate how the contents of both human-written and ChatGPT-refined assessments contribute to the models' prediction and show that the effect of essential words is highly context-dependent. Moreover, we find that ChatGPT's analysis of borrower delinquency contributes the most to improving predictive accuracy. We also evaluate the business impact of the models based on human-written and ChatGPT-refined texts, and find that, in most cases, the latter yields higher profitability than the former. This study provides valuable insights into the transformative potential of generative AI in financial services. |
| title | Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts |
| topic | Risk Management Computational Finance |
| url | https://arxiv.org/abs/2503.18029 |