Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?

• URL: http://arxiv.org/abs/2202.04218v1
• Date: Wed, 9 Feb 2022 01:20:44 GMT
• Title: Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?
• Authors: Matthew Harding and Gabriel F. R. Vasconcelos
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Bank Loan Prediction Using Machine Learning Techniques [0.0]
  We have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99%.
  arXiv  Detail & Related papers  (2024-10-11T15:01:47Z)
• Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management [0.0]
  With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses.
  arXiv  Detail & Related papers  (2024-04-28T13:29:35Z)
• A machine learning workflow to address credit default prediction [0.44943951389724796]
  Credit default prediction (CDP) plays a crucial role in assessing the creditworthiness of individuals and businesses. We propose a workflow-based approach to improve CDP, which refers to the task of assessing the probability that a borrower will default on his or her credit obligations.
  arXiv  Detail & Related papers  (2024-03-06T15:30:41Z)
• Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement [65.26723285209853]
  We derive a framework to analyze whether a transparent implementation in a computing model is feasible. Based on previous results, we find that Blum-Shub-Smale Machines have the potential to establish trustworthy solvers for inverse problems.
  arXiv  Detail & Related papers  (2024-01-18T15:32:38Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models [53.620827459684094]
  Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks. We propose the first open-source comprehensive framework for exploring LLMs for credit scoring. We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
  arXiv  Detail & Related papers  (2023-10-01T03:50:34Z)
• Selective Credit Assignment [57.41789233550586]
  We describe a unified view on temporal-difference algorithms for selective credit assignment. We present insights into applying weightings to value-based learning and planning algorithms.
  arXiv  Detail & Related papers  (2022-02-20T00:07:57Z)
• Inspecting the Process of Bank Credit Rating via Visual Analytics [34.55692862750793]
  Bank credit rating classifies banks into different levels based on publicly disclosed and internal information. We propose RatingVis to assist experts in exploring and comparing different bank credit rating schemes.
  arXiv  Detail & Related papers  (2021-08-06T09:08:00Z)
• Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts [1.7778609937758323]
  We focus on companies credit scoring and we benchmark different machine learning models. The aim is to build a model to predict whether a company will experience financial problems in a given time horizon. We bring light by providing an expert-aligned feature relevance score highlighting the disagreement between a credit risk expert and a model feature attribution explanation.
  arXiv  Detail & Related papers  (2021-03-15T12:59:15Z)
• Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes [61.20223338508952]
  Credit Risk Modelling plays a paramount role. Recent machine and deep learning techniques have been applied to the task. We suggest to use LIME technique to tackle the explainability problem in this field.
  arXiv  Detail & Related papers  (2020-12-30T10:27:59Z)
• Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications [110.54266632357673]
  We present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals.
  arXiv  Detail & Related papers  (2020-05-09T01:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.