Company: PGYWW
Filing Date: 2025-03-12
Form Type: 10-K
Source: 0001883085-25-000050
Chunk: 202

Company: Pagaya Technologies Ltd.
Filing Date: 2025-03-12
Form: 10-K
Item: Item 1A
Chunk 202
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 resources and significant defense and compliance costs and other professional fees. As a general matter, enforcement actions and sanctions could harm our business, financial condition and results of operations.

As the political and regulatory framework for AI technology and machine learning evolves, our business, financial condition and results of operations may be adversely affected.

The political and regulatory framework for AI technology and machine learning is evolving and remains uncertain. It is possible that new laws and regulations will be adopted in the United States, or in certain U.S. states, or existing laws and regulations may be interpreted in new ways, that would affect the operation of our network and the way in which we use AI technology and machine learning, including with respect to lending laws, fair lending laws and model risk management guidance. In the last few years, U.S. banking and consumer protection regulators have increased focus on financial institutions that rely on AI technology in their business and have sent requests for information to various companies to better understand the use of AI technology and machine learning by financial institutions. Further, the cost to comply with such laws or regulations could be significant and would increase our operating expenses, which could adversely affect our business, financial condition and results of operations. In addition, a number of U.S. lawmakers have stated that algorithmic underwriting technologies may result in disparate impact discrimination and urged consumer regulatory agencies to increase enforcement actions where necessary to ensure that consumer lending technology is not being used to discriminate or exacerbate existing biases. Our proprietary technology uses machine learning models as a subset of our AI, but those machine learning models are static and do not have the ability to self-correct, self-improve, and/or learn over time, and any change to the models requires human intervention, testing, validation, and governance approvals before a change can be made. Nevertheless, we face a risk that the use of machine learning in our models, or one or more variables in our model, could be deemed to have resulted in a “disparate impact” on protected groups. Such a result would require us to revise the loan decisioning model in a manner that might generate lower approval rates or higher credit losses.

If obligations by one or more Partners that utilize our network were subject to successful challenge that the Partner was not the “true lender,” such obligations may be unenforceable, subject to rescission or otherwise impaired, we or other program participants may be subject to penalties, and/or our commercial relationships may suffer, each of which would adversely affect our business, financial condition and results of operations.

Obligations are originated by our