Company: PGYWW
Filing Date: 2025-08-07
Form Type: 10-Q
Source: 0001883085-25-000169
Chunk: 42

Company: Pagaya Technologies Ltd.
Filing Date: 2025-08-07
Form: 10-Q
Item: Part I, Item 1
Chunk 42
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 and investment income associated with our ownership interests in certain Financing Vehicles and other proprietary investments.

We incur costs when Network Volume is acquired by the Financing Vehicles. These costs, which we refer to as ‘‘Production Costs,’’ compensate our Partners for acquiring and originating assets. Accordingly, the amount and growth of our Production Costs are highly correlated to Network Volume. An important operating metric to evaluate the success of our economic model, therefore, is FRLPC, or fee revenue less Production Costs.

Additionally, we have built what we believe to be a leading data science and AI organization that has enabled us to assist our Partners as they make decisions to extend credit to consumers or for the identification and purchase, or property management, of single-family rental properties. Excluding Production Costs, headcount, technology overhead and research and development expenses represent the significant portion of our expenses.

Key Factors Affecting Our Performance

Expanded Usage of Our Network by Our Existing Partners 

Our AI technology typically enables Partners to convert a larger proportion of their application volume into originated loans, enabling them to expand their ecosystem and generate incremental revenues. Our Partners have historically seen rapid scaling of origination volume on our network shortly after onboarding and the contribution of Pagaya’s network to Partners’ total origination volume tends to increase over time. Additionally, we continue to introduce and develop new lines of business, asset types, and products and services, enabling Partners to expand their relationship with Pagaya and further increase origination volumes.

Adoption of Our Network by New Partners 

1 Our proprietary technology uses machine learning models as a subset of artificial intelligence that go through extensive testing, validation, and governance processes before they can be used or modified. The machine learning models are static and do not have the ability to self-correct, self-improve, and/or learn over time. Any change to the models requires human intervention, testing, validation, and governance approvals before a change can be made.

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We devote significant time to, and have a team that focuses on, onboarding and managing Partners to our network. We believe that our success in adding new Partners to our network is driven by our distinctive value proposition: driving significant revenue uplift to our Partners at limited incremental cost or credit risk to the Partner. Our success adding new Partners has contributed to our overall Network Volume growth and driven our ability to rapidly scale new asset classes. 

Continued Improvements to Our AI Technology 

We believe our historical growth has been significantly influenced by improvements to our AI technology, which are in turn driven both by the deepening of our proprietary