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<p>Digital lending has become a high impact setting for applied artificial intelligence (AI), where institutions seek faster credit decisions while also facing growing demands for transparency, calibration, governance, and human oversight. This study develops a calibrated and explainable triage decision framework for responsible digital lending and evaluates it as a deployable AI decision support architecture rather than as a prediction task alone. Using the United States Small Business Administration (SBA) loan dataset, the study constructs a leakage free pipeline based on information available at or before approval. LightGBM is selected as the predictive engine, isotonic calibration is used to produce decision ready probabilities, and SHapley Additive exPlanations (SHAP) are used to support interpretability. The calibrated probabilities are then translated into two competing downstream policies: a conventional binary approval rule and an optimized triage policy with approval, rejection, and manual review. Under the base cost scenario, the best binary baseline yields an expected decision cost of 0.111333, whereas the selected uncertainty aware triage policy yields 0.098225, an improvement of 11.77%. The triage policy approves 73.75% of applications, rejects 16.62%, and routes 9.63% to manual review. It also lowers the default rate among approved loans from 1.43 to 0.83% and reduces the good loan rejection rate from 6.74 to 2.36%. Additional validation examines whether the triage result remains informative under temporal shift, dynamic review cost, subgroup variation, and multidimensional deployment criteria. Chronological holdout analysis shows that triage reduces expected decision cost by 14.66% when earlier approval years are used for training and later approval years are used for testing. Dynamic review cost simulation shows that triage remains most valuable when manual review is economically manageable, but its advantage narrows when review cost becomes high or strongly linked to uncertainty and case complexity. Subgroup diagnostics indicate that triage improves expected decision cost across observable business, loan, and geographic groups, while also showing that manual review allocation should be monitored across subgroups. A SAFE inspired deployment quality index, based on Sustainability, Accuracy, Fairness, and Explainability (SAFE), further shows that triage improves integrated deployment quality relative to binary automation because it performs better on cost, approval quality, and opportunity preservation. The findings indicate that the value of AI in lending depends not only on predictive discrimination, but also on how calibrated and interpretable risk estimates are translated into selective automation, review escalation, and monitored deployment governance.</p>

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Selective automation for responsible digital lending using a calibrated and explainable AI triage framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月19日

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  • Nguyen Thanh Quang

摘要

Digital lending has become a high impact setting for applied artificial intelligence (AI), where institutions seek faster credit decisions while also facing growing demands for transparency, calibration, governance, and human oversight. This study develops a calibrated and explainable triage decision framework for responsible digital lending and evaluates it as a deployable AI decision support architecture rather than as a prediction task alone. Using the United States Small Business Administration (SBA) loan dataset, the study constructs a leakage free pipeline based on information available at or before approval. LightGBM is selected as the predictive engine, isotonic calibration is used to produce decision ready probabilities, and SHapley Additive exPlanations (SHAP) are used to support interpretability. The calibrated probabilities are then translated into two competing downstream policies: a conventional binary approval rule and an optimized triage policy with approval, rejection, and manual review. Under the base cost scenario, the best binary baseline yields an expected decision cost of 0.111333, whereas the selected uncertainty aware triage policy yields 0.098225, an improvement of 11.77%. The triage policy approves 73.75% of applications, rejects 16.62%, and routes 9.63% to manual review. It also lowers the default rate among approved loans from 1.43 to 0.83% and reduces the good loan rejection rate from 6.74 to 2.36%. Additional validation examines whether the triage result remains informative under temporal shift, dynamic review cost, subgroup variation, and multidimensional deployment criteria. Chronological holdout analysis shows that triage reduces expected decision cost by 14.66% when earlier approval years are used for training and later approval years are used for testing. Dynamic review cost simulation shows that triage remains most valuable when manual review is economically manageable, but its advantage narrows when review cost becomes high or strongly linked to uncertainty and case complexity. Subgroup diagnostics indicate that triage improves expected decision cost across observable business, loan, and geographic groups, while also showing that manual review allocation should be monitored across subgroups. A SAFE inspired deployment quality index, based on Sustainability, Accuracy, Fairness, and Explainability (SAFE), further shows that triage improves integrated deployment quality relative to binary automation because it performs better on cost, approval quality, and opportunity preservation. The findings indicate that the value of AI in lending depends not only on predictive discrimination, but also on how calibrated and interpretable risk estimates are translated into selective automation, review escalation, and monitored deployment governance.