论文标题
经理与机器：算法是否在信用评级中复制人类直觉？
Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?
论文作者
论文摘要
我们使用机器学习技术来研究是否有可能复制评估美国大型商业银行商业贷款风险的银行经理的行为。即使典型的银行已经依靠算法记分卡流程来评估风险，但银行经理在调整风险评分以根据其直觉和经验来考虑其他整体因素方面有很大的纬度。我们表明，可以找到可以复制银行经理行为的机器学习算法。该算法的输入包括标准财务和软信息的组合，作为典型贷款审查过程的一部分。我们还记录了可以追溯到经理和行业之间差异的调整过程中存在重大异质性的存在。我们的结果强调了基于机器学习的银行业分析方法的有效性以及金融部门高技能工作的潜在挑战。
We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.
