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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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A United States Fair Lending Perspective on Machine Learning.

Patrick Hall1,2, Benjamin Cox3, Steven Dickerson4

  • 1The George Washington University, Washington, DC, United States.

Frontiers in Artificial Intelligence
|June 24, 2021
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Summary

Machine learning (ML) is transforming consumer finance, offering faster credit decisions. This review defines key ML and legal terms, addressing discrimination and interpretability in credit underwriting.

Keywords:
Shapley valuesXAI (explainable artificial intelligence)credit underwritingdeep learning—artificial neural network (DL-ANN)evolutionary learningfairnessinterpretabilitymachine learning

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Area of Science:

  • Consumer Finance
  • Machine Learning
  • Credit Risk Modeling

Background:

  • Machine learning (ML) is increasingly adopted in consumer financial services, particularly for credit underwriting and loan pricing.
  • ML models are recognized for their ability to capture complex data patterns, potentially improving credit decision accuracy and speed over traditional methods.

Purpose of the Study:

  • To propose standardized definitions for crucial machine learning and legal concepts pertinent to discrimination and interpretability in consumer finance.
  • To contextualize ML methodologies within the U.S. legal and regulatory framework for credit underwriting.
  • To review strategies for mitigating adverse implications of ML in consumer finance.

Main Methods:

  • Literature review and conceptual analysis of machine learning methodologies in credit underwriting.
  • Examination of the United States legal and regulatory landscape concerning financial services and algorithmic decision-making.
  • Synthesis of strategies for addressing potential biases and interpretability challenges in ML models.

Main Results:

  • Identified a need for uniform definitions of ML and legal concepts (discrimination, interpretability) in consumer finance.
  • Highlighted the U.S. legal and regulatory environment as a critical factor in the application of ML for credit underwriting.
  • Cataloged various approaches to mitigate adverse impacts associated with ML deployment in credit decisions.

Conclusions:

  • Standardized definitions and a clear understanding of the legal context are essential for responsible ML adoption in consumer finance.
  • Proactive strategies are necessary to manage the risks of discrimination and lack of interpretability inherent in ML models.
  • ML presents a significant, yet complex, evolution in credit modeling, requiring careful navigation of technical and regulatory considerations.