Catch fraud before it costs you

Predictive fraud and chargeback prevention uses machine learning on your transaction, account, and behavior data to score risk in real time. Instead of waiting for a chargeback to land or relying on static rules, you get a probability score on each transaction or account, the moment it appears, plus the drivers behind that score. High-risk cases get blocked, held, or sent to step-up verification before they cost you money.

Rules are great at catching what you’ve already seen. They struggle when fraudsters change tactics. Pecan’s models look at dozens of signals together (device, behavior, payment, order history, velocity) and learn the patterns that separate real fraud from legitimate but unusual orders. As your data evolves, the model evolves with it. Rules don’t.

That’s one of the main reasons teams switch. Static rules tend to flag broad patterns (“any order over $X from new accounts”) and catch a lot of legitimate buyers in the net. Pecan looks at the combination of factors, so a high-value order from a new customer with consistent device, IP, and shipping signals gets approved, while a smaller order with mismatched markers gets flagged. More real fraud caught. Fewer good customers turned away.

Historical, event-level data from your warehouse (Snowflake, BigQuery, Redshift, Databricks, and similar). Useful inputs include order details, payment data, customer history, device and IP signals, prior fraud or chargeback labels, and any review notes. PII isn’t required. Pecan handles data prep and feature engineering automatically.

Yes. Scores flow into your payments platform, ERP, order management, or CRM via secure connectors and APIs. Your team configures the thresholds: auto-approve below a certain score, hold for review in the middle, auto-decline above a higher one. Pecan provides the signal. You control the action.

Yes. Beyond outright fraud, Pecan can predict which legitimate-looking transactions are likely to become chargebacks (think friendly fraud, dispute-prone customers, or service issues). That gives your team a window to intervene with extra verification, proactive customer service, or tighter shipping terms before the chargeback lands.

Every score comes with the top contributing factors, so your team understands why a transaction looked risky (mismatched billing/IP location, velocity from a single device, behavioral anomalies, and so on). That transparency matters for chargeback dispute evidence, compliance reviews, and continuous improvement.

No. Pecan’s predictive agent is built for risk, finance, and analytics teams. If you can ask a clear question about what you want to predict, the agent handles modeling, validation, and deployment end to end. Book a live demo.

Most teams build their first model in under a day and have it deployed within a couple of weeks of integration work. Iterations happen in minutes, so you can keep up as fraud patterns shift.

Pecan is ISO 27001 certified, SOC 2 Type II audited, GDPR ready, and encrypts data at rest and in transit. You control what data is shared, and PII is never required. More on our security here.

Far less than a single fraud-focused data scientist’s salary, with enterprise-grade automation working 24/7. We’ll share a tailored quote during the demo.

Because models go live in days, customers often see measurable reductions in fraud losses, chargeback fees, and false-positive rates within the first quarter.

Every plan is commitment-free, and you can cancel anytime. See the value first, keep us only if it lands.