Company: RSKD
Filing Date: 2025-03-06
Form Type: 20-F
Source: 0001851112-25-000006
Chunk: 74

Company: RISKIFIED LTD.
Filing Date: 2025-03-06
Form: 20-F
Item: Item 4
Chunk 74
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 we are unwilling to guarantee a transaction. Our dashboards also include up-to-date key performance metrics, including order approval rates, account challenges, order declines and chargebacks.

Risk Management

Risk management is at the very heart of our business. We have established processes that are designed to help us manage our overall chargeback exposure and control realized chargeback expenses

within predetermined budget levels. In addition, we adjust our merchants’ approval and chargeback rates in real time to rebalance our exposure and our expected expenses during any given period.

•Strong track record driven by real-time training data: Our engagement model has built-in feedback loops that provide us with up-to-date, high quality training data in the form of chargeback transactions from across our merchant network. This data constantly updates our models to improve their accuracy. Additionally, we have observed a large and diverse population of chargeback transactions since our founding. Combined with our real-time feedback loops, we believe the volume of our high-quality, historical chargeback data is critical to the accuracy of our models. Since our founding, we have incurred relatively few chargebacks, measured as a percentage of our total approved transactions. Historically, chargeback expenses have been in line with our annual budgets.

•Active risk monitoring: We supplement our models with a variety of tools that detect anomalies and prevent fraud in real time. We use a layered approach to ensure that there are multiple levels of analyses performed on each transaction we approve. This layered approach optimizes the overall performance of our AI-powered ecommerce risk intelligence platform while also creating fail safe mechanisms. For example, our anomaly detection models are designed to detect blind spots that our supervised models cannot. We also employ real-time alerting systems for early detection any time our exposure exceeds certain thresholds. Lastly, we use a combination of automated and manual reviews from highly skilled risk analysts to review our exposure on a regular basis, no less than daily.

•Real-time adjustments optimize long term outcomes: We adjust our merchants’ approval rates in real time as we detect riskier order populations. This allows us to optimize outcomes for our merchants when we see safer transactions, while also reducing approval rates when we see riskier transactions. This ability to react opportunistically to changing market conditions and fraud trends allows us to optimize our long term approval rate while also managing our overall chargeback exposure.

•Risk diversification: Significant fraud events are typically isolated to a particular merchant or industry. As a result, our chargeback expenses have become less volatile over time as we scale and diversify our merchant base. Furthermore,