Sift

Free

Sift is a fraud prevention platform for digital businesses. It uses machine learning to tell real customers from fraudsters in real time.

You feed it event data from logins, payments, and account changes. It returns a risk score that triggers automatic decisions or routes cases to your team.

The work sits in finance. Think chargeback protection, account takeover defense, and payment fraud for e-commerce, marketplaces, and fintech.

Open it if you process payments at scale and need real-time fraud scoring. Skip it if you want a social media management tool or basic rule-based screening.

It helps when your business faces specific fraud risks and transaction volume.

• E-commerce businesses needing chargeback fraud protection: You get a 90-day proof of concept to test before you buy. The extended trial lets you measure real results on your own data.
• Marketplaces with seller/buyer fraud problems: You can pipe in custom data fields specific to your transaction patterns. This matters because marketplace fraud looks different from retail fraud.
• Fintech companies processing payments: You get millisecond API responses for real-time accept, block, or challenge decisions. Speed keeps checkout friction low.
• Businesses facing coordinated, cross-border fraud attacks: You tap a network of anonymized signals across 700+ brands. This spots fraud rings moving between merchants and borders.
• Teams wanting to reduce manual review burden: You set thresholds for automatic decisions while keeping human control for edge cases. The workflow balances speed with oversight.
• Travel and on-demand services dealing with voucher abuse: You catch repeat offenders and fake accounts before they drain promotional budgets.
• Social platforms fighting spam and fake accounts: You flag suspicious signups and behavior patterns at creation, not after damage spreads.
• Small businesses wanting extended trial periods: You avoid long-term contracts until you see proof the system works for your specific fraud patterns.

These are the parts you notice after running real transactions through it.

• Global data network: You get pattern detection from 1 trillion+ annual events across 34,000+ sites. Fraudsters rarely hit just one merchant, so this shared intelligence spots threats early.
• Custom data fields: You adapt the models to your business, not the other way around. Add events and signals specific to how your customers actually behave.
• Flexible decision routing: You choose full automation, full manual review, or a mix. The score feeds into your existing workflows via API.
• Continuous model retraining: The system updates as fraud tactics evolve. You avoid the trap of static rules that attackers learn to bypass.

Skip it when these limits matter to your situation.

• Dated interface: You work with a functional but old design. Daily operations feel slower than they should when you are moving fast on decisions.
• Machine learning feels underdeveloped: You expect more adaptive sophistication given how the space has evolved. The core ML exists but may not wow you.
• Integration effort required: You need engineering time to instrument event streams and tune rules. This is not a plug-and-play widget.
• Pricing opacity: No public rates or clear implementation costs. You must talk to sales and run the full POC to understand total investment.
• Tightly coupled architecture: The chargeback module locks into the broader system. You cannot easily swap or upgrade parts independently.