SaaS 사기 점수 임계값이란 무엇인가요?

SaaS 사기 점수 임계값은 거래가 나타내는 위험 수준에 따라 조치를 취하는 숫자 경계입니다. 일반적으로 머신러닝 모델이 거래를 분석한 후, 사기와의 유사성 정도에 따라 점수가 할당됩니다. 이 임계값은 주문 승인, 수동 검토 요청 또는 거절과 같이 다음에 취해야 할 조치를 나타냅니다. 이 차단점의 올바른 설정은 보안과 회사 수익 모두에 영향을 미치므로 매우 중요합니다.

The definition of a favorable score can vary, influenced by the specific criteria adopted by each vendor. A low risk score is typically associated with transactions that do not present significant anomalies; a high score may correspond to situations requiring further review, such as discrepancies in customer or account details, an atypical transaction volume within a short timeframe, or an originating IP address that raises concerns. 

SaaS merchants set their own bands; for example, 0–30 means “approval,” 31–70 indicates “review,” and 71+ means “decline,” depending mainly on their risk tolerance, average order value, and the industry.

Commonly, SaaS companies use these approaches:

• Single fixed threshold — this technique utilizes a single fixed threshold to differentiate between approved and declined statuses, without an intermediate category.
• Tiered thresholds — scores are arranged under multiple categories or bands (e.g., “approve”, “review”, “decline”), thereby allowing the manual review aspect.
• Dynamic threshold — the cutoff level is impacted by transaction factors such as quantity, item type, or customer history.
• Segmented threshold — different thresholds are set for different segments such as customer groups, payment means, or location (e.g., geographically).
• Thresholds adjusted by machine learning — the cutoff level evolves continuously as new patterns of fraud appear, so the model has to self-adjust on the fly.

Finding the right SaaS threshold is based mainly on historical data. Here are some of the steps that you can follow to achieve that:

1. Check past transactions to locate where fraud and genuine orders have clustered over risk score ranges.
2. Determine the quantities of regular customers turned away and the quantities of fraud cases identified across cutoff levels.
3. Instead of employing a single blanket threshold, group customers by risk indicators such as order value, geography, or product category.

Merchants typically consider the balance between risk and revenue when establishing fraud score thresholds. A reduction in the authorization cutoff point typically corresponds to a greater number of orders that present intrinsic risks. This may influence the frequency of chargebacks, fee collection, and the handling of penalties. Data suggests a connection between a higher cutoff point and the process verified customers undertake for specific requirements, potentially influencing sales conversion, revenue, and customer trust.

The precision of a fixed fraud detection threshold is influenced by shifts in fraud tactics. The majority of SaaS merchants review their thresholds at least quarterly, and when major sales events, new product lines launch, or there is a substantial increase in chargebacks or false declines.

Before making any decisions on where to devote your efforts, get a few of your thoughts straight.

1.   What aspects of 지불 거절 및 오탐으로 인한 거절 are relevant to your business’s financial operations?
2.   With a high transaction volume, is evaluating the practicality of manually reviewing every business order pertinent?
3.   Has there been a change in the customer demographic or product offerings?

결정 요인:

•       Industry-specific metrics for fraud prevalence
•       사기 예방 methods and resources available
•       Acceptance of workload by human reviewers