Assessing Human Information Processing in Lending Decisions: A Machine Learning Approach
利用小企业贷款数据，研究发现机器学习在处理硬信息上远超贷款官员，而人类擅长利用显著信号引导获取软信息，揭示了人机在信息处理中的互补优势。
ABSTRACT Effective financial reporting requires efficient information processing. This paper studies factors that determine efficient information processing. I exploit a unique small business lending setting where I am able to observe the entire codified demographic and accounting information set that loan officers use to make decisions. I decompose the loan officers’ decisions into a part driven by codified hard information and a part driven by uncodified soft information. I show that a machine learning model substantially outperforms loan officers in processing hard information. Loan officers can only process a sparse set of useful hard information identified by the machine learning model and focus their attention on salient signals such as large jumps in cash flows. However, the loan officers use salient hard information as “red flags” to highlight where to acquire more soft information. This result suggests that salient information is an attention allocation device: It guides humans to allocate their limited cognitive resources to acquire soft information, a task in which humans have an advantage over machines.