Company: AFRM
Filing Date: 2025-08-28
Form Type: 10-K
Source: 0001820953-25-000080
Chunk: 16

Company: Affirm Holdings, Inc.
Filing Date: 2025-08-28
Form: 10-K
Item: Item 1
Chunk 16
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 our own technology and investing in engineering talent, as we believe these are enduring competitive advantages that are difficult to replicate.

Our direct API also allows merchant partners to easily integrate Affirm. From the smallest direct-to-consumer online brand to the largest merchants running on mainframe computers, the technical aspects of integrating with Affirm are quick and painless. Full integration can be completed very quickly, often within days after signing our merchant agreement.

Data advantages that compound over time

Our expertise in sourcing, aggregating, protecting, and analyzing data has been what we believe to be a core competitive advantage of our platform since our founding. We use data to inform our analysis and decision-making, including risk assessment, in a way that empowers consumers and generates value for our merchants and funding sources. 

Our technology is built to handle the immense scale of our data-driven operations — we are capable of processing thousands of checkouts per minute, often relaying underwriting decisions within seconds. Our machine learning-based risk models are calibrated and validated on an extensive amount of data from over 343 million loans, and are custom built to effectively detect fraud, price risk, and provide customized recommendations. We consider 

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data beyond traditional credit scores, such as transaction history and credit usage, and our own repayment history, to predict repayment ability, and leverage this with real-time response data. 

Better outcomes generated by our proprietary risk models

We believe our risk model informs our ability to better assess risk. Unlike legacy payment and credit systems, we can assess and price risk at a transaction level, rather than relying solely on a static consumer credit score. We believe our proprietary risk model has translated this advantage into the ability to facilitate a greater volume of transactions from a wider and more diverse segment of consumers. The greater accuracy of our risk model also generally benefits our provision for credit losses on loans we retain.

Our continuously-learning risk model benefits from increasing scale. As data from new transactions are incorporated into our risk algorithms, we are able to more effectively assess a given credit profile. This process is further enhanced by a constant feedback loop that allows us to monitor performance against our predictions and quickly adjust risk, as needed.

Our ability to quickly assess, price, and manage risk enables us to generate high quality assets that attract funding sources and generate predictable servicing and interest income as consumers repay over time. Our risk model is designed to comply with our originating bank partners’ credit policies and underwriting procedures and is designed to support lower fraud rates and higher approval rates compared to traditional credit underwriting models