About | HeinOnline Law Journal Library | HeinOnline Law Journal Library | HeinOnline

LOG IN
LOG IN


1 [1] (May 10, 2023)

handle is hein.crs/govelok0001 and id is 1 raw text is: Con gressionol Research Service Informing the IegisIative debate since 1914 S May 10, 2023 Automation, Artificial Intelligence, and Machine Learning in Consumer Lending Financial firms may use algorithms-pre-coded sets of instructions and calculations that are executed automatically-to enhance consumer loan underwriting, the process of evaluating the likelihood that applicants will make timely loan repayments. Lenders may rely upon forms of automated analysis to help decide whether to offer consumers loans and at what terms. Faster computing power, internet-based products, and cheaper data storage at scale have increased the prevalence of algorithms. This In Focus discusses developments in automated decisionmaking, artificial intelligence (AI), and machine learning (ML) in consumer loan underwriting. First, it focuses on market developments, then it discusses the current regulatory framework, then finally, it highlights selected policy issues. Market Developments Since the 1970s, consumer loan underwriting has become more automated, first with the increasing use of credit scores and more recently with new data and technologies. Credit scores are a (numeric) metric calculated with information in consumer credit reports and prepared for lenders to determine the likelihood of loan default. New technological innovations have been used to update automated processes, in some cases beyond traditional numeric credit scores. For example, for some lenders, the internet has been incorporated to accept applications, and new data sources are used to conduct consumer loan underwriting. Alternative data generally refers to information that may be used to determine a consumer's creditworthiness that the national credit reporting agencies-Equifax, Experian, and TransUnion-have not traditionally used when calculating credit scores for consumers. Further, Al and ML technologies have advanced rapidly in recent decades. Al technologies are computerized systems that work and react in ways commonly thought to require intelligence, such as solving complex problems in real-world situations. ML is often referred to as a subfield of Al with algorithms designed to automatically improve their performance through experience with little or no human input. These technological developments potentially allow for greater speed, accuracy, and confidence in loan decisions. They are currently used more frequently in fintech products than in more traditional consumer lending products, particularly ML models and alternative data. Fintech (short for financial technology) refers to advances in technology incorporated into financial products and services. Many companies-both traditional financial firms and new technology-focused entrants to the market-are developing fintech products, making it a subject of increased interest for the public and policymakers. ML Models in Consumer Loan Underwriting Consumer loan underwriting can potentially be enhanced by ML models. ML models could improve efficiency and performance and reduce costs for financial institutions, potentially expanding credit access or making credit less expensive for some consumers. ML models could make consumer underwriting decisions more accurate by identifying new patterns, such as changing credit conditions, and by automatically updating the models to make more accurate underwriting assessments. However, ML models can also introduce risks. One risk is a lack of explainability, the inability to explain why programs make particular decisions. Another risk is dynamic updating, which is when models evolve over time without oversight. ML models also raise concerns that they may not perform as intended, possibly resulting in higher loan losses in new market environments or discrimination against protected groups. Current Federal Regukitory Framework The Consumer Financial Protection Bureau (CFPB) is the primary consumer protection regulator for consumer financial products and services. One of the CFPB's statutory objectives is to ensure that markets for consumer financial products and services operate transparently and efficiently to facilitate access and innovation. The CFPB has the authority in consumer financial markets to write regulations and enforce the law for both bank and nonbank financial institutions. However, the CFPB's supervisory authority to examine financial institutions for consumer protection compliance varies based on the charters, activities, and size of institutions. Therefore, financial regulators may monitor some nonbank fintech companies less than traditional banks. Regulatory Uncertainty Many financial laws and regulations that existed prior to recent ML technological developments have led to questions concerning their effectiveness achieving their designed policy goals as these potentially beneficial technologies evolve. Relevant laws and regulations may need to be reconsidered or updated in response to the future use of ML models in consumer loan underwriting. This often involves balancing efforts to encourage innovation while protecting consumers. Federal financial regulators have been monitoring ML models in consumer lending. In March 2021, the bank and credit union regulators, along with the CFPB, requested