Details

Autor(en) / Beteiligte

• Sachan, Swati
• Yang, Jian-Bo
• Xu, Dong-Ling
• Benavides, David Eraso
• Li, Yang

Titel

An explainable AI decision-support-system to automate loan underwriting

Ist Teil von

• Expert systems with applications, 2020-04, Vol.144, p.113100, Article 113100

Ort / Verlag

New York: Elsevier Ltd

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •An explainable system to automated loan underwriting is proposed.•It can accommodate human knowledge and can learn from credit data.•Provide transparency by revealing a chain of events leading to a decision.•Provide textual explanation for a rejected loan application. Widespread adoption of automated decision making by artificial intelligence (AI) is witnessed due to specular advances in computation power and improvements in optimization algorithms especially in machine learning (ML). Complex ML models provide good prediction accuracy; however, the opacity of ML models does not provide sufficient assurance for their adoption in the automation of lending decisions. This paper presents an explainable AI decision-support-system to automate the loan underwriting process by belief-rule-base (BRB). This system can accommodate human knowledge and can also learn from historical data by supervised learning. The hierarchical structure of BRB can accommodates factual and heuristic rules. The system can explain the chain of events leading to a decision for a loan application by the importance of an activated rule and the contribution of antecedent attributes in the rule. A business case study on automation of mortgage underwriting is demonstrated to show that the BRB system can provide a good trade-off between accuracy and explainability. The textual explanation produced by the activation of rules could be used as a reason for denial of a loan. The decision-making process for an application can be comprehended by the significance of rules in providing the decision and contribution of its antecedent attributes.