Bussmann Niklas, Giudici Paolo, Marinelli Dimitri, Papenbrock Jochen
FIRAMIS, Frankfurt, Germany.
Department of Economics and Management, University of Pavia, Pavia, Italy.
Front Artif Intell. 2020 Apr 24;3:26. doi: 10.3389/frai.2020.00026. eCollection 2020.
The paper proposes an explainable AI model that can be used in fintech risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model employs Shapley values, so that AI predictions are interpreted according to the underlying explanatory variables. The empirical analysis of 15,000 small and medium companies asking for peer to peer lending credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain and understand their credit score and, therefore, to predict their future behavior.
本文提出了一种可解释的人工智能模型，该模型可用于金融科技风险管理，特别是用于衡量通过点对点借贷平台借款时出现的风险。该模型采用夏普利值，以便根据潜在的解释变量来解释人工智能预测结果。对15000家申请点对点借贷信用的中小企业进行的实证分析表明，有风险和无风险的借款人都可以根据一组相似的财务特征进行分组，这些特征可用于解释和理解他们的信用评分，从而预测他们未来的行为。
