Report
493
Number
Jan 15 2020

Fairness and Accountability for Algorithms in Financial Services

How can credit unions differentiate on trust? This report reviews a key area where trust is increasingly at a premium: the use of consumers’ data in algorithmic credit scoring. With this change comes new questions and concerns, especially about the potential for bias and discrimination in algorithmic underwriting.

Bill Maurer
Bill Maurer
Dean, School of Social Sciences; Professor, Department of Anthropology and School of Law; Director, Institute for Money, Technology and Financial Inclusion
University of California, Irvine
Melissa K. Wrapp
Melissa K. Wrapp
PhD Candidate, Department of Anthropology
University of California, Irvine
Report Number 493

Executive Summary

Consumers have expressed distrust in the financial services industry while also indicating a high degree of trust in their own primary financial services provider. Given the rise in use of alternative data for credit scoring, credit unions have a responsibility to ensure that bias and discrimination do not occur while implementing algorithmic underwriting. With the rapid advancement of technology, the time to build authentic trustworthiness and consider the ethics of algorithmic decisionmaking is now. 

This workshop will help your team understand bias, algorithms, and the negative impact of discrimination, while also providing tools for taking ethical steps toward adopting algorithmic processes.