Sift Science
Sift (formerly Sift Science) provides a machine learning–based fraud detection and risk management platform that helps online businesses prevent payment fraud, account takeover, abuse, and other malicious behavior across the customer lifecycle. Its software integrates with digital commerce, marketplaces, and fintech applications to score risk in real time and automate decisions such as blocking, challenging, or approving transactions. The company generates revenue through subscription-based SaaS plans and usage-based pricing for enterprises and mid-market customers.
- Fraud Detection & Prevention
- Payment Processing
- Revenue
- $70.0M
- Employees
- 350
- Founded
- 2011
- Headquarters
- San Francisco, United States of America
What 24 Arbolus experts think of Sift Science
NPS scores are predominantly positive, with Promoters (scores 9–10) representing over half of expert responses. Renewal intent is high among long-term and high-spend customers, especially in digital commerce and fintech sectors. Stickiness is evidenced by most customers being in the MoreThan1Year implementation stage, indicating deep integration. Key themes include appreciation for network intelligence, real-time ML scoring, and automation, balanced against concerns over explainability and cost at scale. Overall Customer Sentiment: Positive
Who reviewed Sift Science
- C-Suite 67% (16)
- Director 17% (4)
- Manager 17% (4)
Employer names and spend open in the full report, along with the other 21 customers.
Everything 24 customers told us about Sift Science.
Employer names, spend, renewal intent and every written answer behind the numbers on this page.
Sentiment
25 Net Promoter Score, from 24 scored reviews- Promoters 10
- Passives 10
- Detractors 4
Cumulative NPS by month
+1 over 4 months- Jan 7
- Jun 8
- Aug 8
- Sep 8
Net Promoter Score across 1 reviews, accumulated up to each month. Category median 25 across 1 companies.
Praised
- Impactful Fraud Loss Reduction
- Powerful Real-Time Network Intelligence
- Scalable and Automatable Fraud Controls
- Greater Model Explainability
Criticised
- Ongoing Operational Tuning Needed
- Opaque Machine-Learning Scoring
- Cost Scaling with Transaction Volume
- Competitor Innovation and Switching Risk
What customers say
Renewal intent
Plan to renew 33%Plan to renew, by seniority
- 30% 10 answers
- 0% 1 answer
- 100% 1 answer
- 8.5/10 Average score from customers who will renew
- 7.8/10 Average score from undecided customers
- 6.7/10 Average score from customers not renewing
Report
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