Company: RSKD
Filing Date: 2025-03-06
Form Type: 20-F
Source: 0001851112-25-000006
Chunk: 8

Company: RISKIFIED LTD.
Filing Date: 2025-03-06
Form: 20-F
Item: Item 3
Chunk 8
---
 process fail, we will typically experience higher than forecasted chargebacks, which in turn puts downward pressure on our gross margins, and may impact our ability to attract new merchants, retain existing merchants or increase sales of our products to existing merchants and our business, financial condition, and results of operations may be adversely affected.

Our machine learning models are designed to analyze data attributes to identify complex transaction and behavior patterns, which enables us to detect fraud and illegitimate consumers quickly and accurately. Our ability to accurately detect fraud even as methods of committing fraud evolve and become more sophisticated is dependent on our ability to continuously improve and train these models. However, our machine learning models have at times been, and may in the future prove to be, less accurate than we expect, or than they have been in the past, for a variety of reasons, including inaccurate assumptions or other errors made in building or training such models, incorrect interpretations of the results of such models, increased fraud sophistication beyond the capabilities of our machine learning models, evolving fraud methodologies, and failure to timely update model assumptions and paramet ers. For example, in the third quarter of 2023, one of our merchants experienced a significant fraud event. In this instance, our machine learning models were not able to effectively block the relevant fraudulent transactions. In addition, because we had not correctly predicted the scope of the fraud event, the overall risk level in the merchant’s account was underrepresented at the time the event occurred. The combination of these factors meant that we were unable to mitigate the effects of the fraud event quickly enough, which impacted our quarterly gross profit. In addition, our machine learning models may initially be less accurate following expansion into new industry verticals, geographic regions and use-cases, such as review of automated clearing house (“ ACH”) payments or covering non-fraud related chargebacks. Further, the successful performance of our machine learning models relies on the ability to constantly review and process large amounts of transactions and other data. If we are unable to attract new merchants, retain existing merchants or increase sales of our products to existing merchants, or if our merchants do not provide us with access to a signific ant volume of their transaction data or if the number of transactions processed by our existing merchants declines, the amount of data reviewed and processed by our machine learning models will be reduced or may fail to grow at a pace that will allow us to continue to improve the efficiency of our machine learning models, which may reduce the accuracy of such models. Additionally, such models may not be able to effectively account for matters that